- NURS FPX 6025 Assessment 3 Practicum and Scholarly Article.
Practicum and Scholarly Article
This paper assesses the connection between what I did in school and what I did in my nursing training. Based on the piece “Artificial intelligence-based learning techniques for diabetes prediction: This paper entitled “Challenges and systematic review: Machine learning for diabetes control” which was peer reviewed by other scholars in the field emphasizes on the use of machine learning in controlling diabetes. Generally, the purpose of the summary is to prove that the writer has the right knowledge of theoretical and practical aspects of healthcare service.
Summary of Evidence-Based Journal Article
The peer-reviewed article that was chosen, “Artificial intelligence-based learning techniques for diabetes prediction: The article by Kaul and Kumar (2020) entitled “Challenges and Systematic Review” is related to healthcare informatics and the potential of AI in predicting diabetes. Remember, diabetes is a chronic disease that can lead to serious complications and can even cause death and therefore it is necessary to diagnose it and intervene (Kaula, 2023-52). The paper discusses the issues that are associated with the conventional approaches to diagnosis and complains about the need for new ones.
The writers discuss the advancements that have been made in the application of machine learning in health care particularly in the identification of diabetes. The goal of the study is to enhance the diabetes prediction by employing different machine learning classification techniques like genetic algorithm decision tree, Random Forest, logistic regression, SVM and naive Bayes. The experiments are performed on Pima Indians Diabetes Database (PIDD), which is available from the UCI machine learning repository and it has a large size of data.
NURS FPX 6025 Assessment 3 Practicum and Scholarly Article
The detailed analysis also employs precision, accuracy, F-measure, and memory to find out how each algorithm performs in the process. From the results it can be seen that the genetic program performs better than other methods of classification indicating its ability to predict diabetes. This knowledge is very helpful, especially to doctors who are in the process of searching for reliable ways to detect and cure diabetes in its early stages (Kaul & Kumar, 2020). The paper does not only describe the technological aspect of machine learning but also discusses on how it affects healthcare computing in general. It recognises the expansion of the field of health informatics in the last few years and states that the reason for this is the advancement in technology that enhance one to display, generate, and utilize clinical data.
Computer networks in the healthcare system and the use of cloud computing via healthcare informatics has ensured that clinical data is easily accessible and understandable. Most of the work done in the field of machine learning is to compare different methods and techniques that can be used in the analysis of data. Diabetes is still a health issue in the global level but if doctors utilize the AI based prediction models discussed in this paper to diagnose their patients then it can be helpful in improving the health of patients. As such, the authors’ systematic review contributes to the ongoing body of literature on the application of AI in health care. It offers a potential for further investigation and application in diabetes management.
Discussion on Article with a Peer
Talking with a friend about the article “Artificial intelligence-based learning techniques for diabetes prediction: The article “Challenges and systematic review” by Kaul and Kumar published in this year has helped me to gain many new insights regarding the application of AI in the healthcare sector. There was one particular topic that was discussed and that is how machine learning is now shifting the paradigm of traditional healthcare diagnosis particularly when it comes to diabetes. There is one main idea of the article and it is that AI can assist in dealing with the challenges that arise in the early diagnosis of diabetes. Applying different Machine learning methods such as Genetic algorithms, decision trees, SVM provides us with more than one way of predicting the likelihood of a person to be diabetic.
We also wanted to know whether the Pima Indians Diabetes Database (PIDD) is significant as a standard collection for testing. My coworker also pointed out that the use of data from various sources in the real world also ensures that the suggested machine learning models are good and useful as stated by Liu and Demosthenes in the year 2022. For this reason, we understood that this type of research is not simply an academic exercise, but has the potential for improving the quality of people’s health. The measures that were used in the evaluation such as precision, accuracy, F-measure and memory were also discussed. This work provides useful knowledge about the advantages and disadvantages of each measure when comparing these across numerous algorithms. This made us wonder what this implies for healthcare professionals, especially on how to select and apply prediction models.
When people discussed the idea of the article they learned about genetic code as a more precise way of categorizing the risk of diseases. We examined how the creation of models that can predict a person’s genes can revolutionise the way diseases are prevented and treated. Thus, sharing this article with a friend not only gave us a deeper understanding of how AI can be used in healthcare, but also made us aware of the fact that the usage of machine learning for identification of diabetes is not something out of the world, it is already being practiced. The study provides the foundation for the future work and makes one ponder on the broader role of the AI in the field of personalized medicine and disease prevention.
Reflection on Practicum Experience Including Accomplishments and Challenges
In totality, my experience has been a life changing experience that has had both the good and the bad times. I am quite proud of the fact that I was be exposed to real life healthcare scenarios and thus apply knowledge learned in class. It has made me more determined to become a nurse and also given me a first-hand experience of how healthcare workers work to ensure patients are well taken care of. In my training, I have found that handling challenging patient cases exciting yet rigorous. This is so because activities such as administering drugs and participation in treatment procedures has boosted my morale and confidence as a professional. It has been a success to be able to work as a team of different professionals and this has assisted in teams and communication. But, difficulties have always been there in the process of learning are they not?
I also had to come up with ways of acquiring knowledge and at the same time attend to the patients and document their details. The issue which was also recurrent was balancing on time management with good care. This proved that one has to be very flexible and strong to handle the rigorous nature of the health care industry. Nevertheless, the practice has provided me with many possibilities to reflect on myself and to change in some ways. It has helped me to know my weaknesses and strength and made me know that there is always room for improvement and learning from your mistakes. Overall, my practicum has been an interesting process of my individual and professional development which equipped me with the necessary knowledge and skills to navigate through the complexities of the health care system.
Completion of Practicum Hours
During this practice immersion experience I have documented more than 20 hours of the clinical activities that I have performed while under the supervision of my instructor. These hours include doing patient assessments, giving medications, developing care plans, and interacting with other professionals. I have also indicated that I commit ensuring that I meet the set standards of my practice and enhance on my practical knowledge by documenting these hours. The more time I spend in the clinical setting the more I become a better nurse because it enhances my knowledge on health care practices making it easier for me to deliver excellent patient care.
Conclusion
The use of artificial intelligence based learning models to attempt to predict diabetes has been one of the most impactful parts of my study. The article by Kaul and Kumar presents an interesting view of how the field of healthcare science is evolving with special emphasis on the role of machine learning in determining the morbidity of a disease. Going through the hurdles and opportunities of this constantly evolving profession has opened my eyes and made me more motivated to try new approaches to improve patients’ outcome. This internship has been very beneficial for me to learn, develop, and contribute to the improvement of the healthcare system. Read more about our sample NURS FPX 6025 Assessment 2 practicum and experience reflection for complete information about this class.
References
Kaul, S., & Kumar, Y. (2020). Artificial intelligence-based learning techniques for diabetes prediction: Challenges and systematic review. SN Computer Science, 1(6). https://doi.org/10.1007/s42979-020-00337-2
Kaula, V. K. (2023). A comprehensive review of gestational diabetes mellitus. A Comprehensive review of gestational diabetes mellitus, 01(12), 1–1. https://doi.org/10.5281/zenodo.10389724
Liu, F., & Demosthenes, P. (2022). Real-world data: A brief review of the methods, applications, challenges, and opportunities. BioMedCentral Medical Research Methodology, 22(1). https://doi.org/10.1186/s12874-022-01768-6