Explainable AI method uses records from 77 million U.S. health visits to generate “multimorbidity networks” to explore cardiovascular disease

Article URL: https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000004 

Article Title: An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records

Author Countries: U.S.A.

Funding: This research was supported by the AHA Children’s Strategically Focused Research Network grant (17SFRN33630041) (https://professional.heart.org/en/research-programs/strategically-focused-research/strategically-focused-research-networks) and the Nora Eccles Treadwell Foundation. RD’s effort was supported by the National Institutes of Health under Ruth L. Kirschstein National Research Service Award T32 HL007576 from the National Heart, Lung, and Blood Institute (https://grants.nih.gov/grants/oer.htm). GL was supported by NRSA training grant T32H757632 (https://researchtraining.nih.gov/programs/training-grants/T32). SW was supported by NRSA training grant T32DK110966-04 (https://researchtraining.nih.gov/programs/training-grants/T32). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: GL, VD, MY own shares in Backdrop Health, there are no financial ties regarding this research.