Suresh, B.M.; Herzog, N.J. Empowering Community Clinical Triage through Innovative Data-Driven Machine Learning. Preprints2024, 2024031432. https://doi.org/10.20944/preprints202403.1432.v1
APA Style
Suresh, B.M., & Herzog, N.J. (2024). Empowering Community Clinical Triage through Innovative Data-Driven Machine Learning. Preprints. https://doi.org/10.20944/preprints202403.1432.v1
Chicago/Turabian Style
Suresh, B.M. and Nitsa J. Herzog. 2024 "Empowering Community Clinical Triage through Innovative Data-Driven Machine Learning" Preprints. https://doi.org/10.20944/preprints202403.1432.v1
Abstract
Efficient triaging and referral assessment are critical in ensuring prompt medical intervention in the Community Health Care (CHC) system. However, the existing triaging systems in many Community Health Services are an intensive, time-consuming process and often lack accuracy, particularly for various symptoms that might represent heart failure or other health-threatening conditions. There is a noticeable limit of research papers describing AI technologies for triaging patients. This paper proposes a novel quantitative data-driven approach using machine learning (ML) modelling to improve the community clinical triaging process. Furthermore, this study aims to employ the feature selection process and machine learning power to reduce the triaging process’s waiting time and increase accuracy in clinical decision-making. The model was trained on medical records of “Heart Failure” patients’ dataset, which included demographics, past medical history, vital signs, medications, and clinical symptoms. A comparative study was conducted using the list of machine learning algorithms where XGBoost demonstrated the best performance among other ML models. The triage levels of 2,35,982 patients achieved an accuracy of 99.94%, precision of 0.9986, recall of 0.9958, and f1-score of 0.9972 within 0.059 seconds. The proposed diagnostic model can be implemented for the CHC decision system and be developed further for other medical conditions.
Keywords
clinical triaging; machine learning; community health care triaging; prediction; classification; artificial intelligence
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
