Abstract
Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care. This review provides an overview of ML in healthcare epidemiology and practical examples of ML tools used to support healthcare decision making at 4stages of hospital-based care: triage, diagnosis, treatment, and discharge. Examples include model-building efforts to assist emergency department triage, predicting time before septic shock onset, detecting community-acquired pneumonia, and classifying COVID-19 disposition risk level. Increasing availability and quality of electronic health record (EHR) data as well as computing power provides opportunities for ML to increase patient safety, improve the efficiency of clinical management, and reduce healthcare costs.
Highlights
Machine learning builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care
We summarize Machine learning (ML) in healthcare epidemiology and provide practical examples of ML tools used to support decision making at 4 stages of hospital-based care: triage, diagnosis, treatment, and discharge
The increasing availability of electronic health record (EHR) data and other sources provides ML opportunities to learn more about disease prevention, classification, and trajectory and to develop earlier and more targeted interventions.[44]
Summary
Machine-learning algorithms can identify relationships between patient attributes and outcomes to construct models that can make predictions for new and unseen patients and can group patients based on similar attributes.[9] there is overlap, the simplified difference between statistical methods and ML is that statistics is generally associated with drawing inferences from data, whereas ML is more concerned with finding generalizable predictive patterns.[10] while statistics uses algorithms to learn about a model’s attributes from the data assuming the model’s structure, ML harnesses computing power and uses algorithms to learn about the model’s structure and attributes directly. Fed structured data for patient attributes (ie, the independent variables or ‘features’), the algorithm attempts to find the corresponding model that predicts patient outcomes with the highest precision, accuracy, or recall. Machine learning (ML) A type of artificial intelligence in which computers draw conclusions from data without being directly programmed
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