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The objective was to develop a fully automated algorithm for abdominal fat segmentation and to deploy this method at scale in an academic biobank.

Artificial intelligence (AI) is increasingly being proposed for use in medicine, including breast cancer screening (BCS). Little is known, however, about referring primary care providers' (PCPs') preferences for this technology.

To develop a computer model to predict patients with nonalcoholic steatohepatitis (NASH) using machine learning (ML).

The Office of National Coordinator for Health Information Technology final rule implementing the interoperability and information blocking provisions of the 21st Century Cures Act requires support for two SMART (Substitutable Medical Applications, Reusable Technologies) application programming interfaces (APIs) and instantiates Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR) as a lingua franca for health data. We sought to assess the current state and near-term plans for the SMART/HL7 [...]

The COVID-19 pandemic swept across the world rapidly, infecting millions of people. An efficient tool that can accurately recognize important clinical concepts of COVID-19 from free text in electronic health records (EHRs) will be valuable to accelerate COVID-19 clinical research. To this end, this study aims at adapting the existing CLAMP natural language processing tool to quickly build COVID-19 SignSym, which can extract COVID-19 signs/symptoms and their 8 attributes (body [...]

We aimed to develop a model for accurate prediction of general care inpatient deterioration.

Access to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address this problem by building a predictive model into a comprehensive clinical framework with the aims to (i) identify in-hospital patients likely to benefit from a PC consult, and (ii) intervene [...]

Drawing causal estimates from observational data is problematic, because datasets often contain underlying bias (eg, discrimination in treatment assignment). To examine causal effects, it is important to evaluate what-if scenarios-the so-called "counterfactuals." We propose a novel deep learning architecture for propensity score matching and counterfactual prediction-the deep propensity network using a sparse autoencoder (DPN-SA)-to tackle the problems of high dimensionality, nonlinear/nonparallel treatment assignment, and residual confounding when estimating treatment effects.

Substance use disorder is a critical public health issue. Discovering the synergies among factors impacting treatment program success can help governments and treatment facilities develop effective policies. In this work, we propose a novel data analytics approach using machine learning models to discover interaction effects that might be neglected by traditional hypothesis-generating approaches.