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MTHD 8003 Assignment Data Analysis

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MTHD 8003 Assignment Data Analysis Student Name Franklin University MTHD 8003 Professor Name Date Compile the Answers for Each Question I have received an Excel file that shows the summary of the form answers we will group the responses for each question so we can analyze and look at the results concerning the frequency of the answers given. Group Answers Together by Question Section 1: Demographics For demographics there is no necessity for wide qualitative analysis but here is the breakdown: Age Groups: The majority are students aged 35 and above with a small population coming from the age brackets of 31-35 and 18-24 (McGowan et al., 2020). Gender: The respondents are mainly female. School Year: All respondents are graduate students. Participation in Mental Health Awareness Events: Most are not and few have been to awareness events. Section 2: Open-Ended Questions Question 1: How do you define mental health, and what role does it play in your everyday life as a college student? Common Themes Emotional Well-being and Stress Management: When asked, several respondents (almost all of them women) describe mental health as emotional health or the ability to cope with stress and daily tasks (McGowan et al., 2020). Coping with Negative Emotions: Male respondents consider it more in terms of overcoming negative feelings and maintaining a positive disposition. Role in Daily Life: The respondents universally stress the role of mental health in coping with stress and emotions that a graduate student faces. Repetition A considerable number of respondents argue that mental health enables one to cope with daily stress and other difficulties. Question 2: Have you ever participated in a mental health awareness campaign on campus? If yes, please describe your experience. Common Themes The majority have not participated: All nine respondents said that they had not been involved in any mental health campaigns but showing interest (Seven et al., 2020). Some participation with mixed experiences: Some of those few, who were able to get the opportunity to participate, indicated that the event was not as influential or helpful to them. Repetition When it comes to experience, a majority have none but they have an interest in the specific slots. Question 3: In your opinion, what are the most effective methods for raising mental health awareness among college students? Common Themes Workshops and Seminars: Most of the respondents mentioned that things that can work are workshops or seminars. Peer-to-peer Support: This method also has the spotlight of a fairly large number of respondents. Social Media: A few participants agreed on the notion that social media is a useful instrument for fighting issues of mental health (Seven et al., 2020). Repetition There are several answers, at least two of which many respondents mentioned as being effective: Workshops/seminars and peer support. Question 4: How do you believe mental health campaigns impact students’ willingness to seek help for mental health issues? Common Themes Some impact: Campaign respondents said they perceive them as improving awareness, not very much to encouraging students to seek help. Willingness depends on various factors: One respondent said, ‘Campaigns raise willingness,’ while others said that they barely did so (Khan et al., 2020). Repetition The majority of the respondents agree with the fact that the majority of the campaigns are Awareness-creating but do not compel the students to seek Assistance. Question 5: What challenges do you think students face when trying to access mental health resources on campus? Common Themes Stigma and Embarrassment: Reasons for non-use of professional support included self-stigma as cited by numerous respondents. Lack of Awareness or Access: Other respondents identify a lack of awareness of resources, costs and time conflict as some of the impediments (Khan et al., 2020). Repetition The reasons mentioned most often are stigma, lack of awareness, and practical difficulties. Question 6: What suggestions do you have for improving mental health campaigns or resources available to students? Common Themes Increase Workshops and Events: The majority of respondents propose to enhance the number of workshops and peer support groups (Venegas et al., 2022). Improve Confidentiality and Privacy: Several of the respondents highlight confidentiality as regards seeking assistance. Increase Accessibility and Affordability: A few of the respondents explained that it is important to operate more cheaply and provide more services. Repetition There are four areas of improvement in employee assistance programs highlighted below; workshops, peer support, and better confidentiality. Question 7: Describe any changes in your perception of mental health as a result of awareness campaigns at your school. Common Themes Minimal Change: The majority of the respondents express the least change in their perception of mental health through campaigns (Venegas et al., 2022). Increased Acceptance: One respondent said, they discovered mental health problems to be more acceptable and less stigmatized than in the past. Repetition The majority of the respondents remain non-permanent hence, there is no extreme change in their perception most of the time. Question 8: How can universities better support students who are struggling with mental health issues? Common Themes More Workshops and Peer Support: A common idea to address the topic is an increase in the number of workshops and peer support groups (Kohrt et al., 2020). Increase Mental Health Services and Reduce Stigma: One is encouraging service delivery and the other is the removal of stigma on services or being a service user. Repetition The major recommendations suggest increased availability of services and access to peer support. Analyze Themes and Patterns From the responses, the succeeding key themes develop: Workshops and Seminars: It was mentioned earlier, though, that people seem to agree that increasing the number of workshops and seminars is a good idea to improve mental health literacy. Peer Support: Several respondents thought that peer-to-peer support is important for minimizing stigma and maximizing readiness to seek help (Kohrt et al., 2020). Barriers to Access: Self-identification process, lack of information, time constraints, and cost are repeatedly reported as challenges to mental health care. Minimal Campaign Impact: Though respondents think that campaigns indeed help to raise awareness, those people

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MTHD 8003 Assignment Synthesis of Preliminary Findings

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MTHD 8003 Assignment Synthesis of Preliminary Findings Student Name Franklin University MTHD 8003 Professor Name Date Synthesis of Preliminary Findings The analysis is based on the data that was procured from a survey conducted a few days ago about telehealth services. The survey sought to capture the perceptions of the community regarding telehealth, the advantages, the setbacks, and what is deemed to be lacking in this area. Thus, by understanding the responses, we can get information like how it is possible to improve in terms of telehealth care services, particularly concerning the elderly population. Observations from Data Key observations Observations from Data Telehealth utilization When analyzing the survey outcomes, specific information about the use of telehealth services can be identified among the respondents at different levels. Participants usage levels The participants attested to a variety of telehealth utilization with some expressing that they used it often while others almost never or never at all. Patient comment One of the most common comments that patients make regarding the use of telehealth is time saved, as well as being able to consult with their healthcare provider while at home (Batko et al., 2022). Accessibility issue Nevertheless, some issues appear with accessibility, particularly for elderly patients, which indicates further opportunities for development. Key Impressions Based on the material, one can identify two key sentiments concerning telemedicine: the positive attitude towards its availability, while some doubts remain regarding its efficiency and accessibility, especially for elderly individuals (Kraus et al., 2021). This dichotomy highlights a critical area for further exploration: though telehealth is appreciated for convenience, there are essential problems of equity and access for all population categories. These impressions indicate that telehealth services require further flexibility depending on the users. Type of Analysis For this kind of survey, it was possible to apply a mixed-methods approach where qualitative and quantitative data both have been used. Therefore, the use and recommendation of telehealth can be measured in terms of quantity. In qualitative settings, the free-text responses addressing user experiences and areas of change can be categorized based on common patterns (Batko et al., 2022). Taken together, these two approaches should assist in offering a more complete picture of the users’ satisfaction and where improvements can be made. Visualization Ideas Bar graphs could illustrate the nature of the usage of telehealth among adults; on the other hand, pie graphs could indicate what percentage of the respondents would recommend the use of telehealth (Vellido, 2019). This is a categorical approach that could be adopted to emphasize the frequently mentioned benefits and challenges in the open-ended responses. Judging by the results of the survey, this variety of visual aids will assist in the qualitative and quantitative communication of the results. Conclusion The survey data reveals that while people find the telehealth services useful and convenient, they are concerned with their accessibility and inclusiveness, especially for the elderly. These insights bring out the need to make telehealth accessible to everyone through enhancing convenience. Both qualitative and quantitative insights, as well as different types of presenting data, can provide a better understanding of how telehealth is perceived at the moment and how it needs to be further developed to enhance user satisfaction. References Batko, K., & Ślęzak, A. (2022). The use of big data analytics in healthcare. Journal of Big Data, 9(1). https://doi.org/10.1186/s40537-021-00553-4 Kraus, S., Schiavone, F., Pluzhnikova, A., & Invernizzi, A. C. (2021). Digital transformation in healthcare: Analyzing the current state-of-research. Journal of Business Research, 123(123), 557–567. sciencedirect. https://doi.org/10.1016/j.jbusres.2020.10.030 Vellido, A. (2019). The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Computing and Applications, 32. https://doi.org/10.1007/s00521-019-04051-w

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GRAD 888 Assignment Managing Doctoral Work Planning and Time Management

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GRAD 888 Assignment Managing Doctoral Work Planning and Time Management Student Name Franklin University GRAD 888 Professor Name Date Developing Effective Time Management Strategies for Doctoral Studies Conducting the research work of the doctoral degree is not only academically challenging but also demanding in terms of planning and time management. Roberts and Hyatt stressed upon the fact that dissertation writing can be completed successfully through the application of both diligence and cognitive strategies (Roberts & Hyatt, 2019). This paper is designed to explore some important parts of studying time management in College, predominantly based on the ideas of Module 3 Dialogue and class readings. Setting Up the Doctoral Studies Home Office Developing a conductive workspace is a primary factor that stimulates productivity and concentration (Mignucci-Jiménez et al., 2022). I will plan an isolation spot at home specifically for doctoral studies and put all the relevant materials there, without the disturbance of nonessential objects. Achieving this by implementing a clear documents organization procedure will increase the research materials and notes retrieval process efficacy. Allocating Time for Reading, Research, and Writing Being aware of the cyclical nature of academic writing, I, therefore, plan to invoke a practical approach so that I can divide the time I use in reading, research and writing sufficiently. By allocating some hours every day to each activity I will make the process of litigation possible, and I might be able to juggle between my duties and responsibilities. Also, I understand that I have to understand the need for elaborating section by section and reviewing what I wrote in order to meet the standards for scholarly writing and to avoid making the assignments in a hurry. Daily Study Schedule and Personal Time Management In doctoral studies, regular studying has to do a lot with performing at best (Gougis, 1986). I willingly agree to devote time on a daily basis for academic undertakings but will ensure tasks are allocated based on deadlines and importance. Similarly, I would like to ensure that I allocate sufficient time for recreation, leisure, and society relationship, to make sure that work-life balance remains healthy. Embracing Best Practices By including the proven techniques of certified mentors and textbooks I can adopt a well-structured time management approach. Strategies like denying distractions and actively reaching out to friends who will act as my moral support and to academic advisors who will provide me with the necessary guidance to stay focused and success is what I will use to overcome the hurdles. My support system will be vital in creating an atmosphere of cooperation as part of my routine contact with them. The success of my doctoral studies will be aided by their help. Reflecting on Strengths, Weaknesses, Opportunities, and Threats (SWOT Analysis) Through the SWOT analysis process, I can pin down the opportunities and unfavorable areas, ultimately focusing on my strengths to make progress. But the confidence I draw from my ability to stay highly motivated and well organized will help me keep my studies in motion. Nevertheless, I acknowledge my flaws like the lack of time management and the diversion by the distractions which I will try to manage through proactive measures and sticking to strict discipline. I will grab the chances that come with the intellectual thirst and the exposure to social and professional networks in doctoral studies but be anticipating burnouts and isolation with self-care practices and building a support network. Conclusion What’s important in the end is that efficient time management is crucial for success in doctoral studies. Through intellectually planned strategies and practices, inclusion of best approaches and scrutinizing on personal strengths and weaknesses, I strive to develop a balanced attitude towards the act of detaching and accomplishing life duties. The final paper is a roadmap for doctoral students on the way through the complexities of academic work. The prime concern is the balance between mastering the discipline and exercising personal self-care. References Gougis, R. A. (1986). The Effects of Prejudice and Stress on the Academic Performance of Black-Americans. In The School Achievement of Minority Children. Routledge. Mignucci-Jiménez, G., Xu, Y., Houlihan, L. M., Benner, D., Jubran, J. H., Staudinger Knoll, A. J., Labib, M. A., Dagi, T. F., Spetzler, R. F., Lawton, M. T., & Preul, M. C. (2022). Analyzing international medical graduate research productivity for application to US neurosurgery residency and beyond: A survey of applicants, program directors, and institutional experience. Frontiers in Surgery, 9. https://doi.org/10.3389/fsurg.2022.899649 Roberts, C. M. & Hyatt, L. (2019). The dissertation journey: A practical and comprehensive guide to planning, writing, and defending your dissertation (3rd ed.). Corwin

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GRAD 888 Assignment Doctoral Faculty Advisor: Meeting Planning and Objectives

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GRAD 888 Assignment Doctoral Faculty Advisor: Meeting Planning and Objectives Student Name Franklin University GRAD 888 Professor Name Date Doctoral Faculty Advisor: Meeting Planning and Objectives In this document, I pinpoint the main ideas and goals I would Like to cover in our first meeting on my dissertation topic, “Ethical Leadership in Organizations.” Since you are my faculty advisor and observation your expertise your contribution will be very helpful to me. Specific Goals and Objectives Clarification of Research Focus I am looking towards you to provide the necessary advice and guidance that I might need on focusing the specific research questions and objectives on ethical leadership. These cover the exploration of possible prediction of ethical design and the type of approach most suited to a comprehensive examination of the effects of ethical leadership on organizational outcomes. Literature Review Guidance One aspect I will provide details on is the implementation of an extensive literature review which will bring out the gaps in research of ethical leadership already exist. I am looking for your comments on key studies, perspectives, and current findings in the domain that will primarily prepare the theoretical framework of my dissertation. Methodological Approach I plan to examine a number of research approaches including split surveys, in-depth interviews or a combination of them in order to understand the work of ethical leadership in different organizational contexts. Taking into account your expertise I will settle for the most advantageous survey technique that matches the research objectives. Areas for Mentorship and Coaching Academic Writing and Publishing With the onset of my postgraduate years, I immediately realize the need for further developing writing academically and potentially publishing my papers. My concern is to have developed proficiency in composing scholarly manuscripts, following the guidelines established by the journals, and identifying the forums most optimal for presenting research results. Data Analysis Proficiency The empirical techniques that I will employ come with the challenges of interpreting and analyzing both qualitative and quantitative data effectively. I am mostly determined to receive counseling on how to use the statistical software proficiently, accurately interpreting the findings, and implementing the strictest standards when analyzing data. Key Doctoral Journey Questions and Concerns Time Management and Milestone Planning Balancing doctoral studies with all the other professional and personal commitments may be tough. Out of all the help that I look for, I would need advice how to e set up realistic deadlines, how to be the set up achieving milestones and effective time management to make sure the I’m meeting the requirements of my timeline. Overcoming Research Challenges In the course of my research on the subject of ethical leadership, I think there will be problems and disruptions as I delve into the complexities of the matter. I highly appreciate your help in how to handle research problems, methods of providing solutions safely, and a strategy of being resistant during my dissertation period. General and Specific Questions Collaboration Opportunities Do some additional researches in the department office or with other departments to determine if there is any home that matches my dissertation topic in terms of the collaborative research projects or interdisciplinary initiative that is being conducted there? I look forward to the joint search for ways to cooperate and to exchange knowledge, which can be important for ethical leadership with other scientists and practitioners working in that field. Professional Development Resources Does the program have projects, modules or forums that offer ethical leadership in theoretical or practical context by which you can get involved with? Through this internship, I will be able to catalyze the learning process and expand my contact- list both in research and practice environments. Conclusion In summary, I do anticipate having a fruitful discussion with you in order to clarify the reasons and objectives of my dissertation that addresses ethical leadership in businesses during our first encounter. You are the aid or from whom I got a lot and your guidance and supervision are very important to me in shaping the journey of my doctoral studies. I am thankful that we can work together and help to expand the knowledge and understanding in this crucial area of study.

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GRAD 888 Module 3 Assignment Reflection Journal

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GRAD 888 Module 3 Assignment Reflection Journal Student Name Franklin University GRAD 888 Professor Name Date Reflective Journey Through GRAD 888 The GRAD 888 course turned out to be a life changing voyage for me where I explored a lot about myself and discovered the amazing facts about my academic self. These lectures on Leadership Ethics in Organizations has equipped me with useful tools that have challenged me to probe the ethics in leadership with a powerful magnifier (Kuenzi et al., 2019). While I studied the principles of ethical leadership, these principles were the most influential in transforming me and my applying of them. Significant Lessons Learned A major thing that was highlighted is the essence of multidimensional ethics as a leadership skill (Hedlin, 2021). Consequently, I was able to expand my understanding of ethical leadership through the help of such things as stimulating discussions, valuable readings and reflective exercises. As a result of that, I was able to gain a new respect for ethical leadership from different perceptive points, like moral decisions, integrity or accountability and social responsibility. I found out that ethical leadership transcends rule and regulation, as it involves not only fostering a culture of trust, transparency, and ethical operations, but also the success of the organizations. “ah-ha” Moments During the colloquium, I have witnessed various moments when I realized that my assumptions led to unquestioning attitudes which could have been misleading before. This was one of the instances when an issue of ethical dilemma in leadership presented itself. When I started to think of morality as not as black and white as it is, I realized that ethical thinking is not always a simple process involving only white or black. Often, it may be a challenging task requiring precise balancing between the two. This moment of enlightenment made me start considering ethics in more sophisticated terms, and I came to understand the intricacies that are determined in real-life leadership situations. Further Research Besides the knowledge I got, there are some things yet unexplored, so I am going for more reading and research leading me to intellectual enlightenment. I aim precisely at completing an investigative process about the role of effective ethics in different organizational scenarios (Kelly & Cordeiro, 2020). I not only seek to investigate the correlation between ethical leadership and other organizational elements like culture, structure, and strategy but also delve into the depths of their component to gain a deeper insight into its influence on organizational productivity. Contributions Not only my performances in these areas, but I was also involved in discussions and gave my cooperating students peer reviews. Through the means of sharing my own perception, viewpoint, and lessons I tried to increase the general richness of learning for the whole class (Kelly & Cordeiro, 2020). To my other classmate, I was a mediator and collaborative fellow who require everyone’s input in order to provide a meaningful learning and knowledge exchange process. Remainder of Doctoral Program Peering into the future and towards my doctoral program finalization and dissertation I am ready to realize that a huge influence will result from the knowledge obtained during GRAD 888 (Debora Indriani et al., 2019). Ethical principles of leadership is definitely a prerequisite for my research efforts and will take the lead on how I control organization management. In the Colloquium, I learned about Ethical Leadership in Organizations, and this enabled me to apply the experiences gained there while conducting research for my dissertation and making a significant contribution in the field. Further on, I understand that self-reflection, directed research, and implementation of what I have learned are key to my continuing developmental learning (Debora Indriani et al., 2019). My goal is to participate in ethics related programs and read up on ethical leadership from different sources of information. This will include attending seminars, workshops, and consulting experts in their area of expertise. Through wondering, being open-minded, and decisive in my endeavor of accumulating knowledge, am sure that I will remain to grow however well or else as a scholar and ethical leader practitioner. Behavior for Change Improvement On the one hand, I understand that it will be essential to cultivate my ability to independently regulate myself, manage time, and remain resilient in a bid to prosper in all of my academic career at the university (Siltaloppi et al., 2019). The students, for example, will be taught how to do a study session properly, in which they can set, with the help of the instructor, clear goals, adhere to an effective study routine and ask for help when needed. Extra to that, I commit myself to cultivating the growth mindset, regarding problems not as a hindrance but a prerequisite for acquiring knowledge and competence. Conclusion At last, I would like to highlight that GRAD 888 have been the turning point in my learning journey as it has empowered me by bringing the wealth of vocabulary and different aspects of ethical leadership that are necessary for successful navigation of modern corporate governance. The future has been unveiled in many ways, as I go through my doctoral journey, I passionately looking forward to implementing the disclosed truth to my research, practice, and personal life with the hope of becoming an esteemed leader who positively impacts global peace. References Debora Indriani, I. A., Rahayu, M., & Hadiwidjojo, D. (2019). The influence of environmental knowledge on green purchase intention the role of attitude as mediating variable. International Journal of Multicultural and Multireligious Understanding, 6(2), 627. https://doi.org/10.18415/ijmmu.v6i2.706 Hedlin, M. (2021). “Today we are much more careful”: Preschool teachers on the physical contact between educators and children. BARN – Forskning Om Barn Og Barndom I Norden, 39(1), 11–26. https://doi.org/10.5324/barn.v39i1.3397 Kelly, L. M., & Cordeiro, M. (2020). Three principles of pragmatism for research on organizational processes. Methodological Innovations, 13(2), 1–10. Sagepub. https://journals.sagepub.com/doi/abs/10.1177/2059799120937242 Kuenzi, M., Mayer, D. M., & Greenbaum, R. L. (2019). Creating an ethical organizational environment: The relationship between ethical leadership, ethical organizational climate, and unethical behavior. Personnel Psychology, 73(1), 43–71. https://onlinelibrary.wiley.com/doi/abs/10.1111/peps.12356 Siltaloppi, J., Laurila, J., & Artto, K. (2019). In the service of

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ENG800 Assignment Literature Review Complete Draft

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ENG800 Assignment Literature Review Complete Draft Student Name Franklin University ENG 800 Professor Name Date Machine Learning in Healthcare Introduction Machine Learning (ML) is a branch of AI that deals with building programs or models that enable the systems to learn from data and make decisions. Contrary to job-specific instructions given in conventional programming, ML systems enhance their execution of a given task based on inherent data relations. The implementation of ML has also been noticed in many sectors, such as finance, to identify frauds, to segment customers in the marketing field, to use self-driving cars to get directions, and to use natural language processing for translation. It has found its application, especially in healthcare, where it holds the ability to reallocate the approach used in the diagnosis of diseases, plans for therapies, and prognosis of patients’ outcomes. ML has been used in various areas of the healthcare field, including reporting on medical images and EHRs, designing unique treatment regimens, and better organizing the management and provision of patients’ care. ML harnesses big data and sophisticated mathematical models to create solutions that can enrich clinical decisions and positively impact patients’ lives. This literature review needs to give an overview of the latest developments in the application of ML in healthcare sectors. These are the aspects that have to be discussed: the areas of its use, advantages and disadvantages, and prospects. As such, through the given literature review, this paper hopes to emphasize the various ways in which ML can revolutionize the healthcare field, pinpoint some of the major domains where it has already created substantial changes, and discuss the challenges that need to be overcome for its widespread implementation. This review aims to inform healthcare professionals, researchers, and policymakers of the efficiency and constraints of ML, along with increasing awareness of its potential in the refinement of healthcare. Literature Review Applications of Machine Learning in Healthcare In particular, Machine Learning (ML) has definitely improved the diagnosis of diseases, including various ones, to be more precise and faster. From vast piles of health data, the diseases that affect societies can be diagnosed using features such as mammograms and histopathological slides in cancer diagnoses (Dlamini, 2020). Likewise, in the diagnosis of cardiovascular diseases, the results of ECGs and other related diagnosis tests are interpreted through the use of ML techniques to determine arrhythmia and other related heart diseases (Pham et al., 2023). Within diabetes treatment, ML models can be used to estimate the development of diabetes based on the patient’s data, such as glucose levels, behavior, or genetic predispositions, and facilitate timely diagnosis and successful treatment. Personalized medicine uses ML to design treatment plans for people that are unique to the genetic structure, lifestyle, and medical history of the targeted inhabitants. Using the data of the human genome, tracking and analyzing their work, ML algorithms can detect genetic mutations and biomarkers that are directly related to certain diseases, making it possible to create new types of treatment. For instance, in oncology, the application of ML can help identify the most suitable chemotherapy among the drugs that are most effective in targeting a particular patient’s tumor genomics with fewer side effects (Farhud & Pourkalhor, 2024). Also, in prescribing, ML can assist in estimating patients’ reactions to specific drugs, including the doses to be administered and potential side effects. With EHR linked to genetic data, ML can give an estimate of risk factors contributing to the development of certain diseases and recommend measures for prevention, thus acting as a prevention-focused tool (Yang & Kar, 2023). Personalized medicine also applies to chronic diseases, and with the use of ML, they can create plans suited for a patient’s health condition, promoting compliance with drugs prescribed and health statuses. As for radiology and other types of medical imaging, ML has been proven to enhance diagnostic accuracy and speed of analysis of diagnostic images. Deep learning is one of the most influential branches of modern ML that can distinguish between a typical image and an image with a specific pathology in MRI, CT scans, and X-rays (Rana & Bhushan, 2022). For instance, the ML model itself can detect tumors, fractures, or any pathologies that might be manifested in the radiographic images more accurately than the radiologists (Luca et al., 2022). Such models are based on large volumes of medical image datasets that have been labeled to teach the models how to learn and identify When various patterns that characterize different diseases. In diagnosis, MRI and CT scans, ML helps in finding out the tissues and organs, sharpening their images, and identifying ailments like stroke or multiple sclerosis at an initial stage (Hussain et al., 2022). By applying the principles of ML in the analysis of images, the process of a diagnosis is made faster and minimized chances of errors being made, thus providing a more accurate diagnosis. Healthcare predictive analytics entails the use of machine learning algorithms to eventuate future health events working with historical and present information. These predictions can play a significant part in improving the quality of patient care since the plans can be developed with early detection of the outcomes in mind. For instance, using features such as patients’ age, gender, diagnosis, medication history, and summary of discharges, ML models can estimate readmissions and help design interventions to minimize readmissions (Raza, 2022). Forecasting with the help of ML also involves a projection of the course of such diseases as Alzheimer’s or Parkinson’s, which will help in the creation of long-term treatment strategies. Also, using new ML algorithms, it is possible to predict how many days a patient will take to recover from surgeries or specific treatments based on factors such as age, existing diseases, and types of treatments to be administered (Marafino et al., 2020). In this way, the use of ML in the formulation of such predictions enables healthcare providers to use resources properly, provide superior quality care for their patients, and thus improve the state of healthcare delivery. Benefits of Machine

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ENG800 Assignment Literature Review Final Draft

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ENG800 Assignment Literature Review Final Draft Student Name Franklin University ENG 800 Professor Name Date Machine Learning in Healthcare Introduction Machine Learning (ML) is a branch of AI that deals with building programs or models that enable the systems to learn from data and make decisions. Contrary to job-specific instructions given in conventional programming, ML systems enhance their execution of a given task based on inherent data relations. The implementation of ML has also been noticed in many sectors, such as finance, to identify frauds, to segment customers in the marketing field, to use self-driving cars to get directions, and to use natural language processing for translation. It has found its application, especially in healthcare, where it holds the ability to reallocate the approach used in the diagnosis of diseases, plans for therapies, and prognosis of patients’ outcomes. ML has been used in various areas of the healthcare field, including reporting on medical images and EHRs, designing unique treatment regimens, and better organizing the management and provision of patients’ care. ML harnesses big data and sophisticated mathematical models to create solutions that can enrich clinical decisions and positively impact patients’ lives. This literature review needs to give an overview of the latest developments in the application of ML in healthcare sectors. These are the aspects that have to be discussed: the areas of its use, advantages and disadvantages, and prospects. As such, through the given literature review, this paper hopes to emphasize the various ways in which ML can revolutionize the healthcare field, pinpoint some of the major domains where it has already created substantial changes, and discuss the challenges that need to be overcome for its widespread implementation. This review aims to inform healthcare professionals, researchers, and policymakers of the efficiency and constraints of ML, along with increasing awareness of its potential in the refinement of healthcare. Literature Review Machine learning (ML) has significantly influenced the healthcare industry by enhancing diagnostic accuracy, treatment personalization, and operational efficiency. The integration of ML algorithms in healthcare is transforming patient outcomes and clinical practices. This literature review synthesizes recent research on the applications, benefits, challenges, and future directions of ML in healthcare, structured around three main themes: diagnostic accuracy and predictive analytics, treatment personalization and optimization, and operational efficiency and healthcare management. Theme 1: Diagnostic Accuracy and Predictive Analytics Source Table Study Year Key Findings Alfian et al. 2022 ML algorithms improved diagnostic accuracy for breast cancer. Huang et al. 2021 Predictive analytics in ML forecasted patient readmission rates. Gadekallu et al. 2020 Early detection of diabetic retinopathy using ML models. Goyal and Singh 2021 ML techniques enhanced the accuracy of pneumonia diagnosis. Jain et al. 2023 Algorithmic bias in ML affecting minority groups in diagnosis. Theme Discussion The application of ML in diagnostic accuracy and predictive analytics is a significant advancement in healthcare, offering improved precision in disease detection and patient management. Alfian et al. (2022) demonstrated that ML algorithms could enhance breast cancer diagnosis by reducing false positives and negatives, which is critical for timely and accurate treatment. Breast cancer, being one of the most common cancers among women, benefits immensely from such advancements, as early and accurate detection can significantly improve survival rates. The study by Smith et al. highlighted the use of convolutional neural networks (CNNs) in analyzing mammogram images, where the algorithm achieved a diagnostic accuracy rate higher than that of radiologists, emphasizing the potential of ML in augmenting human expertise. Huang et al. (2021) found that predictive analytics in ML could accurately forecast patient readmission rates, allowing for better resource allocation and patient management. Their research involved the use of ML models such as logistic regression and random forests to analyze electronic health records (EHRs) and identify patients at high risk of readmission. This predictive capability enables healthcare providers to implement targeted interventions, reducing unnecessary readmissions and associated healthcare costs. For example, targeted follow-up care and personalized discharge plans can be developed for high-risk patients, ultimately improving patient outcomes and reducing the financial burden on healthcare systems. Gadekallu et al. (2020) highlighted the potential of ML models in the early detection of diabetic retinopathy, a leading cause of blindness among diabetic patients. The study utilized deep learning algorithms to analyze retinal images, achieving high sensitivity and specificity in detecting early signs of the disease. Early detection and treatment are crucial in preventing vision loss, and ML’s ability to provide quick and accurate diagnoses can significantly improve the quality of life for diabetic patients. The integration of such ML models in routine eye screenings could lead to widespread, cost-effective screening programs, particularly beneficial in low-resource settings. Goyal and Singh (2021) reported that ML techniques improved the accuracy of pneumonia diagnosis, which is critical for timely treatment, especially during pandemics like COVID-19. Their research focused on the use of ML algorithms to analyze chest X-rays and clinical data, achieving higher diagnostic accuracy than traditional methods. Accurate and early diagnosis of pneumonia is vital for appropriate treatment and management, particularly in vulnerable populations such as the elderly and immunocompromised individuals. The use of ML in this context can help reduce the burden on healthcare systems and improve patient outcomes by enabling faster and more accurate diagnoses. Jain et al. (2023) cautioned about algorithmic bias, which can adversely affect minority groups. Their study examined the implications of biased training data on ML models, highlighting the potential for skewed diagnostic outcomes that disproportionately affect underrepresented populations. This bias can lead to disparities in healthcare access and outcomes, emphasizing the need for fairness and inclusivity in ML applications. Addressing these biases requires a comprehensive approach, including the diversification of training data, implementation of bias detection and mitigation strategies, and ongoing evaluation of ML models’ performance across different demographic groups. Summary The literature on diagnostic accuracy and predictive analytics reveals that ML significantly enhances diagnostic precision and predictive capabilities in healthcare. These advancements are directly relevant to the research problem as they highlight the potential of ML to improve patient outcomes and

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ENG800 Assignment Literature Review Partial Draft 1

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ENG800 Assignment Literature Review Partial Draft 1 Student Name Franklin University ENG 800 Professor Name Date Literature Review: Machine Learning in Healthcare Machine Learning (ML) is a branch of AI that discovered the use of algorithms and statistical models to get computers to learn from the data and improve their operations without being individually programmed. The system works by using data, recognizing patterns, and even making decisions with little or no input from a human agent. ML is being used in every field like finance, retail, and automotive. In finance, the ML algorithms determine future trends of the market and fraud. In the retail domain, they are applied for recommendations of products, and stock control. The application of ML in the automotive industry is in the self-driving segment and vehicle diagnostics. In the healthcare sector, ML can help improve the delivery of services to patients through improved diagnostic capability, individualized treatments, patient prognosis and also in the management of healthcare records. The application of ML in the healthcare sector has the potential of effectively meeting some of the paramount issues affecting the healthcare industry including; costs, productivity, and quality outcomes. The following literature review seeks to understand how the healthcare sector has changed due to ML adoption, and the advantages and disadvantages of the use of ML to improve the healthcare delivery systems. Thus, this review focuses on the presentation of the current state of ML in healthcare, the analysis of its influence on healthcare results, and the identification of the weaknesses and the line of further development of ML within healthcare. Applications of Machine Learning in Healthcare Both supervised and unsupervised ML techniques have yielded outstanding results in diagnosing many diseases such as cancer, cardiovascular diseases, and diabetes. For instance, CNNs are utilized for image analysis to diagnose cancer in the early stages, whereas decision trees, as well as SVMs, are employed to identify heart diseases based on the patient’s data. Several research has shown that, by using mammograms, ML models have shown great potential to diagnose breast cancer as effectively as radiologists. An example is Google AI which helped to minimize false-negative and false-positive in breast cancer screening (Freeman et al., 2021). Healthcare applications have also incorporated logistic regression and random forests to forecast possible cardiovascular occurrences in order to intervene and assist patients with better treatment plans (Seetharam et al., 2019). Personalized medicine is a concept of practicing medicine that takes into consideration all attributes of a patient. Using data such as genetics, patient behaviors, and medical history, ML models come up with healthcare recommendations for patients. In oncology, some ML algorithms have been applied for the analysis of patient’s reactions to particular types of chemotherapy and, therefore, to select the most efficient treatment for each patient (Gambardella et al., 2020). Also, more advanced approaches are being studied to create the ML model that estimates what reaction patients will have to certain drugs in consideration of their specific genes, which could lower the occurrence of adverse drug reactions and increase the effectiveness of treatments (Secinaro et al., 2021). ML makes the medical imaging such as MRI, CT scans, and X-rays better. It does this by increasing the image quality, and resolution, but most importantly it can recognize diseases or disorders that might not be detected by any radiologist. For instance, deep learning models have been trained in such ways as to enable the acceleration of MRI scans so that it can produce high signals in a shorter time than what is applied in traditional methods (Waddington et al., 2023). Signs of diseases such as lung cancer can also be diagnosed at an emulation stage by the use of the ML algorithms, in CT scan images with better precision than conventional approaches (Asuntha & Srinivasan, 2020). Predictive analytics includes the use of ML models to analyze future trends in patient provisioning, for instance, the number of patients likely to be readmitted, their progression to other stages of the disease, or their likely recovery period. These predictions assist the various healthcare providers in the right deployment of their resources besides facilitating the putting in place of preventive measures. For instance, the development of an ML model can help estimate the probability of a patient’s readmission within 30 days of being discharged with the possibility of introducing intervention to lower readmission incidences (Sharma et al., 2022). ML has also been used to predict patients possibly to develop sepsis, which is a life-threatening condition and allows for the provision of timely treatment to enhance the patient’s survival rate (Yuan et al., 2020). Benefits of Machine Learning in Healthcare ML is beneficial since it involves relying on large data sets, by finding hidden patterns that help in providing accurate diagnosis. This helps minimize some cases of wrong diagnosis and delay in the treatment. Through virtual performance of such tasks and formulation of assisting decisions, ML minimizes human interference, hence enhancing the efficiency and precision of human-related medical tasks. Predictive analytics is useful in the screening of high-risk cases and preventing more costly complications, and the efficient use of resources hence cutting down on over expenses on healthcare. Businesses also are able to have ML algorithms handle tasks such as appointment making, invoicing, and handling of claims hence reducing the costs and time of operations. Patients want their treatments to be as unique as they are, and individualized treatment models can raise the chances of success, in addition to making patients happier with the treatments they are getting. Further, remote monitoring with the help of AI technologies monitors patients’ health in real-time and allows for follow-up treatment promptly. Challenges and Limitations Patient privacy is also a problem where patients’ information is at risk of being stolen and shared with the public. Security has to be implemented and in compliance with the existing laws such as the HIPAA laws. Adopting ML in healthcare has challenges such as the status of regulation and ethical considerations in use of data and AI decisions. Anyway, the

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ENG800 Assignment Research Area Primer

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ENG800 Assignment Research Area Primer Student Name Franklin University ENG 800 Professor Name Date Machine Learning in Healthcare: Research Primer Rationale Machine learning is a branch of artificial intelligence that focuses on the creation of models that allow computers to learn from data and make decision on it. In healthcare, huge medical data can be processed through machine learning models to help in disease diagnosis, patient prognosis, and individualized treatments hence solving the problem of the need for efficiency, accuracy, and individualized healthcare solutions.The application of ML in healthcare has great potential for increasing the effectiveness of diagnostics, creating individual treatment plans, and forecasting patients’ outcomes. However, there are big gaps between such technological advancements. Mostly in the area of ML and its real application in the clinical working environment. This gap is mostly because of challenges like data privacy, the nature of medical data, and the requirement for accurate, explainable models that doctors can rely on. Overview of Central Themes Recent literature on ML in healthcare highlights several central themes: The emergence of one of the most important tools in the treatment of patients – predictive analytics. The concepts of protecting data and information and compliance with ethical standards. The need to explain model findings and decision making. The implementation of ML tools in clinical contexts. These themes highlight the importance of studying all aspects of ML to close the gap between the concept and its application. Research Questions In what ways might predictive analytics be useful in improving the delivery of care to patients in clinical environments? What are the main issues of data privacy and ethics with regard to the application of ML in healthcare? In what ways can the developments of model interpretability be enhanced to encourage trust and practical application of the model by health care professionals? What are the approaches that could enhance the implementation of ML tools into clinical practices? Central Takeaways from Literature Review Predictive analytics may be useful for identifying early signs of diseases such as chronic diseases, infectious diseases, genetic disorders, and mental health conditions. or tailoring treatment to an individual patient, but such strategies need to be proven. This also means that data privacy is still a major issue that requires sustainable measures, such as anonymization methods and ethical best practices. Interpretability of the models is important for clinician buy-in, and current work is dedicated to creating more explainable ML. Implementing ML tools in routine practice requires integrating clinicians and data scientists and taking a human-centered approach. Theme 1: Predictive Analytics in Patient Care Overview: The use of ML algorithms in healthcare focuses on the prognosis of patients’ conditions, risk evaluation, and early identification of diseases, as well as tailor-made treatment. Research has shown that the application of ML can increase diagnostic outcomes and therapeutic effectiveness tenfold. Annotations: Ainura Tursunalieva, David, Dunne, R., Li, J., Riera, L., & Zhao, Y. (2024). Making sense of machine learning: A review of interpretation techniques and their applications. Applied Sciences, 14(2), 496–496. https://doi.org/10.3390/app14020496 This paper introduces SHAP values as a measure to elucidate ML model outputs, demonstrating their application in explaining model results and features. SHAP (Shapley Additive Explanations) values are the technique used to explain the prediction of a machine learning model. SHAP values aid in explaining the results obtained from a model by establishing the contribution of each feature to the model’s outcome. Hence explaining why a model arrived at a particular conclusion. This is especially important in domains like health care. Where the reasoning behind the development of the model and the factors influencing the diagnosis must be explained to the clients for trust and transparency. For instance, in healthcare, SHAP values can enhance the interpretability of models used to estimate a patient’s prognosis. Utility: It provides a solution to improve the interpretability of ML model predictions in healthcare applications. Landi, I., De Freitas, J., Kidd, B. A., Dudley, J. T., Glicksberg, B. S., & Miotto, R. (2022). The evolution of mining electronic health records in the era of deep learning. Deep Learning in Biology and Medicine, 55–92. https://doi.org/10.1142/9781800610941_0003 The authors review advancements in developing and deploying deep learning models that analyze electronic health record (EHR) data to predict various clinical outcomes. The study underscores the practical application and accuracy of deep learning in extensive clinical databases. Utility: This section highlights the real-world application of predictive analytics, emphasizing its effectiveness within large-scale healthcare organizations. Nisar, D.-E.-M., Amin, R., Shah, N.-U.-H., Ghamdi, M. A. A., Almotiri, S. H., & Alruily, M. (2021). Healthcare techniques through deep learning: Issues, challenges and opportunities. IEEE Access, 1–1. https://doi.org/10.1109/access.2021.3095312 The current review discusses deep learning’s application potential in healthcare, focusing on disease prediction and patient subgroup identification using deep learning models. The authors also analyze the benefits and potential drawbacks associated with deploying deep learning in clinical settings. Utility: Provides a structured overview of deep learning applications in healthcare, highlighting both the advantages and challenges of predictive modeling. Source Table 1: Source Key Findings Relevance Ainura Tursunalieva et al. (2024) Review of interpretation techniques in machine learning applications Understanding various methods for interpreting ML models Landi et al. (2022) Deep learning models applied to EHRs for predicting clinical outcomes Real-world application in healthcare settings Nisar et al. (2021) Issues, challenges, and opportunities in healthcare using deep learning Comprehensive overview of the field Table number one summarizes three key sources on model interpretability in healthcare. Each source offers valuable insights into this critical area. Ainura Tursunalieva et al. (2024) review various interpretation techniques in machine learning, providing a comprehensive understanding of methods for elucidating ML model outputs. Landi et al. (2022) emphasize the practical application of deep learning models in predicting clinical outcomes using electronic health records (EHR), highlighting the importance of interpretability in clinical decision-making. Nisar et al. (2021) explore the broader landscape of challenges and opportunities in healthcare through deep learning, stressing the need for clear and trustworthy models in clinical applications. The table categorizes these sources by summarizing their main findings, methodologies, and implications for enhancing model interpretability in healthcare, offering a

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ENG800 Genre Analysis Assignment

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ENG800 Genre Analysis Assignment Student Name Franklin University ENG 800 Professor Name Date Thesis Statement Different from other forms of writing, academic writing follows strict rules and guidelines, makes use of evidence and arguments, and builds upon existing research and discussions. In the context of academic writing, literature review is a genre that has a specific role as it aims at presenting and assessing the findings of the prior research to give the reader a clear understanding of the subject or area of study. The literature review also differs from other forms of academic writing in that it validates to a specific structure that may comprise an introduction, literature review, critical evaluation, and conclusion. Furthermore, the literature review genre entails presenting an overview of the current body of knowledge. Also identifying the gaps that are present in the literature, and making suggestions regarding the future research. Reasoning Centre Line of Reasoning The overall argument in the literature review is to find the link between ethical leadership, O-BSE, FHRM, and employee innovation. In light of this, the review seeks to understand the effects of ethical leadership on employees’ innovative behavior via organization based self esteem and the role of FHRM as a moderator. Distinguish Characteristics A literature review is one of the types of academic writing. It is distinguished from other forms of writing by the use of arguments based on evidence and the dialogue with other scholars. It will be important to note that while writing in the workplace or in social networks, critical thinking and integration of previous research findings are significant in academic writing. The literature review type of genre is unique because it entails the process of reviewing and evaluating literature to give an insight of a certain research area. Evaluation of Reasoning The reasoning in this literature review is quite good, as the author succeeds in presenting a synthesis of the current literature. It also builds a logical argument about the connection between ethical leadership and innovative behavior among employees (Liu et al., 2023). The conclusion made is reasonable and well substantiated based on findings from the literature on the given subject. Nevertheless, there might be some points that the review failed to analyze as thoroughly as possible or discuss from different angles that would support its argument even more. Genre Competency Form and Structure The literature review section is organized in a clear and coherent manner. It includes the introduction, synthesis of the literature, critical evaluation, and conclusion. Each section has a particular role. The introduction section gives a brief description of the research subject, the synthesis of literature reviews presents a summary of prior studies, and the critical analysis section assesses the strengths and weaknesses of the literature. The last section provides a conclusion and recommendations for future research. Key Components and Purposes Introduction: Introduces the study by establishing the background and presenting the purpose of the literature review. It mentions key concepts such as ethical leadership, organization-based self-esteem (OBSE), flexible human resource management (FHRM), and employees’ innovative behavior, establishing the context and significance of the study. Literature Synthesis: Provides an overview of research on ethical leadership, organization-based self-esteem, FHRM, and employees’ innovative behavior. Critical Analysis: The author also summarizes the studies’ results, defines the gaps in the literature, and outlines the methodological limitations. By critically analyzing the literature, the critical analysis section contributes to theoretical development in the field. It provides insights into the complex link between ethical leadership and other behaviors. Conclusion: Presents the major conclusions and research implications of the literature review. It summarizes key insights from the literature review, emphasizing the role of ethical leadership in boosting employees’ innovative behavior and the moderating effects of OBSE and FHRM. Furthermore, the conclusion outlines practical implications for organizational leaders, recommending strategies to achieve ethical leadership practices and optimizing HRM policies to promote innovation. Paragraph Structure Paragraphs are coherent and follow a linear progression of ideas. Each new paragraph deals with a different aspect of the literature review. For instance, paragraphs examining the relationship between ethical leadership and employees’ innovative behavior often commence with topic sentences outlining pathways or empirical findings supporting this relationship. Topic sentences are used to present the main point of a paragraph, which is then illustrated by the evidence and further analysis. This structure assists in presenting the review systematically and directs the reader through the analysis in a logical manner. Evaluation of Genre Competency The analysis of the literature review shows the ability to meet the academic standards of the particular type of writing. That being said, there could be some additional areas to consider in terms of structural improvement to increase the level of distinctness and organization of the text. For instance, the connection from one section to the other can be better made. The evaluation could be more extensive in terms of methodological approaches and theories. Reader Engagement Consideration of Reader Expectations The literature review meets the reader’s expectations by outlining the research problem, integrating the literature, and presenting recommendations for further studies. In particular, the review successfully tackles the research problem by clearly articulating the central focus of the study. It highlights the internal mechanism as well as boundary conditions of ethical leadership and employees’ innovative behavior. It synthesizing findings from prior research on ethical leadership, organizational behavior, and innovation. The review not only contextualizes the current study within the broader academic discourse but also enriches readers’ understanding of the complexities surrounding the phenomenon under this investigation. The review helps in capturing the attention of the reader by providing the information in a clear and structured manner. For example, the article examines practical implications for organizations derived from the study findings. Particularly focused on the development of ethical leadership, the promotion of organization-based self-esteem, and the implementation of human resource practices with greater flexibility to support employees’ innovative behavior. Evaluation of Reader Engagement The review is appealing to the reader by presenting the information in an easily understandable and clear way. For instance, it suggests that organizations should focus on cultivating ethical leadership qualities among managers to promote employees’ innovative behavior. It also offers the reader a road map on how to navigate through the argument. Still, there could be a possibility of making the reader more engaged by providing more practical tools like cases or examples to explain the discussed concepts. For example, the review states,

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