From Data to Decisions: Machine Learning in Medical Informatics
DOI:
https://doi.org/10.5281/zenodo.14582709Keywords:
medical informatics, machine learning, artificial intelligenceAbstract
Objective: Medical informatics is an interdisciplinary domain that integrates information technology to enhance the quality and efficiency of healthcare services. Recent advancements in artificial intelligence (AI) and its subset, machine learning (ML), have significantly influenced this field. This article aims to examine the role of machine learning in medical informatics, focusing on its applications, benefits, limitations, and future potential, particularly in improving healthcare delivery and decision-making processes.
Methods: Machine learning, a branch of AI, enables computational systems to predict outcomes or make decisions based on data without explicit programming. Its implementation in medicine spans various domains, including disease diagnosis, personalized treatment planning, clinical decision support systems, epidemiological research, and clinical trials.
Results: Machine learning contributes to faster and more accurate diagnoses, facilitates early disease detection, alleviates healthcare professionals' workload, and optimizes resource utilization. Despite these benefits, its application faces challenges such as data privacy concerns, ethical dilemmas, and technical constraints.
Conclusion: The adoption of machine learning in medical informatics has the potential to revolutionize healthcare systems. While challenges remain, advancements in this technology are expected to lead to more widespread and impactful applications in the future.
References
1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine. 2019;25(1):44-56.
2. Shinde PP, Shah S, editors. A review of machine learning and deep learning applications. 2018 Fourth international conference on computing communication control and automation (ICCUBEA); 2018: IEEE.
3. Song C, Ristenpart T, Shmatikov V, editors. Machine learning models that remember too much. Proceedings of the 2017 ACM SIGSAC Conference on computer and communications security; 2017.
4. Jiang T, Gradus JL, Rosellini AJ. Supervised machine learning: a brief primer. Behavior therapy. 2020;51(5):675-87.
5. Burkart N, Huber MF. A survey on the explainability of supervised machine learning. Journal of Artificial Intelligence Research. 2021;70:245-317.
6. Chandra MA, Bedi S. Survey on SVM and their application in image classification. International Journal of Information Technology. 2021;13(5):1-11.
7. Kramer O, Kramer O. K-nearest neighbors. Dimensionality reduction with unsupervised nearest neighbors. 2013:13-23.
8. Vembandasamy K, Sasipriya R, Deepa E. Heart diseases detection using Naive Bayes algorithm. International Journal of Innovative Science, Engineering & Technology. 2015;2(9):441-4.
9. Amato F, López A, Peña-Méndez EM, Vaňhara P, Hampl A, Havel J. Artificial neural networks in medical diagnosis. Elsevier; 2013. p. 47-58.
10. Taud H, Mas J-F. Multilayer perceptron (MLP). Geomatic approaches for modeling land change scenarios. 2018:451-5.
11. Ahuja R, Chug A, Gupta S, Ahuja P, Kohli S. Classification and clustering algorithms of machine learning with their applications. Nature-inspired computation in data mining and machine learning. 2020:225-48.
12. Hahne F, Huber W, Gentleman R, Falcon S, Gentleman R, Carey V. Unsupervised machine learning. Bioconductor case studies. 2008:137-57.
13. Kurita T. Principal component analysis (PCA). Computer vision: a reference guide: Springer; 2021. p. 1013-6.
14. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94.
15. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nature medicine. 2019;25(1):24-9.
16. Shickel B, Tighe PJ, Bihorac A, Rashidi P. Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE journal of biomedical and health informatics. 2017;22(5):1589-604.
17. Hamilton AJ, Strauss AT, Martinez DA, Hinson JS, Levin S, Lin G, et al. Machine learning and artificial intelligence: applications in healthcare epidemiology. Antimicrobial Stewardship & Healthcare Epidemiology. 2021;1(1):e28.
18. Harrer S, Shah P, Antony B, Hu J. Artificial intelligence for clinical trial design. Trends in pharmacological sciences. 2019;40(8):577-91.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Furkan ŞAKİROĞLU
This work is licensed under a Creative Commons Attribution 4.0 International License.
