BioDash: A semantic web dashboard for drug development
Eric K. Neumann, Dennis Quan
PSB 2006
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.
Eric K. Neumann, Dennis Quan
PSB 2006
Prasanna Sattigeri, Jayaraman J. Thiagarajan, et al.
ACSSC 2014
Gowri Nayar, Ignacio Terrizzano, et al.
Frontiers in Genetics
Jesus J. Caban, Noah Lee, et al.
ISBI 2009