Abstract
The goal of this work is to utilize Electronic Medical Record (EMR) data for real-time Clinical Decision Support (CDS). We present a deep learning approach to combining in real time available diagnosis codes (ICD codes) and free-text notes: Patient Context Vectors. Patient Context Vectors are created by averaging ICD code embeddings, and by predicting the same from free-text notes via a Convolutional Neural Network. The Patient Context Vectors were then simply appended to available structured data (vital signs and lab results) to build prediction models for a specific condition. Experiments on predicting ARDS, a rare and complex condition, demonstrate the utility of Patient Context Vectors as a means of summarizing the patient history and overall condition, and improve significantly the prediction model results.
Highlights
A key goal in critical care medicine is the early identification and timely treatment of rapidly progressive, life-threatening conditions, such as Sepsis, Septic Shock, and Acute Respiratory Distress Syndrome (ARDS)
The information needed for a reliable risk evaluation of such rare and complex conditions is typically dispersed across the patient Electronic Medical Record (EMR), and available at different times throughout the patient stay
Patient visit EMR data is used to look up recorded up-to-date ICD codes, clinical notes, vital signs, and lab results
Summary
A key goal in critical care medicine is the early identification and timely treatment of rapidly progressive, life-threatening conditions, such as Sepsis, Septic Shock, and Acute Respiratory Distress Syndrome (ARDS). Such life-threatening conditions, are both rare, and at the same time, complex and heterogeneous, involving the interaction of multiple risk factors, comorbidities, and current symptoms. Hospital alert systems typically rely on screening of structured data such as vital signs and lab results, and, in the case of such rare conditions, are often associated with “alert fatigue” and require manually entered clinical judgement. The challenge of real-time CDS systems is the variability and the availability of real-time EMR data, resulting from different charting behaviors, health care delivery models, hospital settings, etc
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