Work

Enabling individualised health in learning healthcare systems

Year: 2019

Type: article

Abstract: The rising burden of healthcare costs suggests that the healthcare system could benefit from novel methods that allow for continuous learning to provide more data-driven, individualised care at lower costs and with improved outcomes. Here, we present our synergistic Learning approach for Prediction, Interpretation/Inference and Communication (Learning PIC) framework to address the challenges hindering the successful implementation of learning healthcare systems and to enable the effective delivery of evidence-based medicine.

Source: BMJ Evidence-Based Medicine

DOI: 10.1136/bmjebm-2019-111190

Authors: Shannon Wongvibulsin, Scott L. Zeger

Institutions: Johns Hopkins University, Johns Hopkins Medicine

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of General Medical Sciences, National Institute of Arthritis and Musculoskeletal and Skin Diseases