DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms

Jayaraman J. Thiagarajan, Deepta Rajan, Sameeksha Katoch, Andreas Spanias

Research output: Contribution to journalArticlepeer-review

Abstract

Effective patient care mandates rapid, yet accurate, diagnosis. With the abundance of non-invasive diagnostic measurements and electronic health records (EHR), manual interpretation for differential diagnosis has become time-consuming and challenging. This has led to wide-spread adoption of AI-powered tools, in pursuit of improving accuracy and efficiency of this process. While the unique challenges presented by each modality and clinical task demand customized tools, the cumbersome process of making problem-specific choices has triggered the critical need for a generic solution to enable rapid development of models in practice. In this spirit, we develop DDxNet, a deep architecture for time-varying clinical data, which we demonstrate to be well-suited for diagnostic tasks involving different modalities (ECG/EEG/EHR), required level of characterization (abnormality detection/phenotyping) and data fidelity (single-lead ECG/22-channel EEG). Using multiple benchmark problems, we show that DDxNet produces high-fidelity predictive models, and sometimes even provides significant performance gains over problem-specific solutions.

Original languageEnglish (US)
Article number16428
JournalScientific reports
Volume10
Issue number1
DOIs
StatePublished - Dec 1 2020

ASJC Scopus subject areas

  • General

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