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Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient's chart.

Objectives: To determine whether a nuclear magnetic resonance-based metabolomics approach can be useful for the early diagnosis and prognosis of septic shock in ICUs. Design: Laboratory-based study. Setting: University research laboratory. Subjects: Serum samples from septic shock patients and ICU controls (ICU patients with systemic inflammatory response syndrome but not suspected of having an infection) were collected within 24 hours of admittance to the ICU. Interventions: None. Measurements and Main Results: 1H nuclear magnetic resonance spectra of septic shock and ICU control samples were analyzed and quantified using a targeted profiling approach. By applying multivariate statistical analysis (e.g., orthogonal partial least squares discriminant analysis), we were able to distinguish the patient groups and detect specific metabolic patterns. Some of the metabolites were found to have a significant impact on the separation between septic shock and control samples. These metabolites could be interpreted in terms of a biological human response to septic shock and they might serve as a biomarker pattern for septic shock in ICUs. Additionally, nuclear magnetic resonance-based metabolomics was evaluated in order to detect metabolic variation between septic shock survivors and nonsurvivors and to predict patient outcome. The area under the receiver operating characteristic curve indicated an excellent predictive ability for the constructed orthogonal partial least squares discriminant analysis models (septic shock vs ICU controls: area under the receiver operating characteristic curve = 0.98; nonsurvivors vs survivors: area under the receiver operating characteristic curve =1). Conclusions: Our results indicate that nuclear magnetic resonance-based metabolic profiling could be used for diagnosis and mortality prediction of septic shock in the ICU. [ABSTRACT FROM AUTHOR]