Machine Learning and Health Science Research: Tutorial
Hunyong Cho, Jane She, Daniel de Marchi, Helal El-Zaatari, Edward L. Barnes, Anna R. Kahkoska, Michael R. Kosorok, Arti Virkud
Abstract
Machine learning (ML) has seen impressive growth in health science research due to its capacity for handling complex data to perform a range of tasks, including unsupervised learning, supervised learning, and reinforcement learning. To aid health science researchers in understanding the strengths and limitations of ML and to facilitate its integration into their studies, we present here a guideline for integrating ML into an analysis through a structured framework, covering steps from framing a research question to study design and analysis techniques for specialized data types.
Topics & Concepts
PreprintComputer scienceHealth scienceArtificial intelligenceData scienceMachine learningLearning sciencesPsychologyMathematics educationWorld Wide WebMedical educationMedicineExperiential learningMachine Learning in HealthcareHealth, Environment, Cognitive AgingArtificial Intelligence in Healthcare