Work

Neural Networks for Clinical Order Decision Support.

Year: 2019

Type: article

Abstract: Consistent and high quality medical decisions are difficult as the amount of literature, data, and treatment options grow. We developed a model to provide automated physician order decision support suggestions for inpatient care through a feed-forward neural network. Given a patient's current status based on information data-mined and extracted from the Electronic Health Record (EHR), our model predicts clinical orders a physician enters for a patient within 24 hours. As a reference benchmark of real-world standard-of-care clinical decision support, existing manually-curated order sets implemented in the hospital demonstrate precision: 0.21, recall: 0.48, AUROC: 0.75 relative to what clinicians actually order within 24 hours. Our feed-forward model provides an automated, scalable, and robust system that achieves precision: 0.41, recall: 0.61, AUROC: 0.80.

Source: PubMed Central

Authors: Jonathan X Wang, Delaney K Sullivan, Adam J Wells, Alex C Wells, Jonathan H Chen

Institutions: Stanford Medicine, Stanford University

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: green

Funder: National Institute of Environmental Health Sciences

Award: Data-Mining Clinical Decision Support from Electronic Health Records