Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence.
Journal:
Nature medicine
Published Date:
Feb 11, 2019
Abstract
Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs. In total, 101.6 million data points from 1,362,559 pediatric patient visits presenting to a major referral center were analyzed to train and validate the framework. Our model demonstrates high diagnostic accuracy across multiple organ systems and is comparable to experienced pediatricians in diagnosing common childhood diseases. Our study provides a proof of concept for implementing an AI-based system as a means to aid physicians in tackling large amounts of data, augmenting diagnostic evaluations, and to provide clinical decision support in cases of diagnostic uncertainty or complexity. Although this impact may be most evident in areas where healthcare providers are in relative shortage, the benefits of such an AI system are likely to be universal.
Authors
Keywords
Adolescent
Artificial Intelligence
Child
Child, Preschool
China
Deep Learning
Diagnosis, Computer-Assisted
Electronic Health Records
Female
Humans
Infant
Infant, Newborn
Machine Learning
Male
Natural Language Processing
Pediatrics
Proof of Concept Study
Reproducibility of Results
Retrospective Studies