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Intensive care clinicians need reliable clinical practice tools to preemptunexpected critical events that might harm their patients in intensive careunits (ICU), to pre-plan timely interventions, and to keep the patient's familywell informed. The conventional statistical models are built by curating only alimited number of key variables, which means a vast unknown amount ofpotentially precious data remains unused. Deep learning models (DLMs) can beleveraged to learn from large complex datasets and construct predictiveclinical tools. This retrospective study was performed using 42,818 hospitaladmissions involving 35,348 patients, which is a subset of the MIMIC-IIIdataset. Natural language processing (NLP) techniques were applied to buildDLMs to predict in-hospital mortality (IHM) and length of stay >=7 days (LOS).Over 75 million events across multiple data sources were processed, resultingin over 355 million tokens. DLMs for predicting IHM using data from all sources(AS) and chart data (CS) achieved an AUC-ROC of 0.9178 and 0.9029,respectively, and PR-AUC of 0.6251 and 0.5701, respectively. DLMs forpredicting LOS using AS and CS achieved an AUC-ROC of 0.8806 and 0.8642,respectively, and PR-AUC of 0.6821 and 0.6575, respectively. The observedAUC-ROC difference between models was found to be significant for both IHM andLOS at p=0.05. The observed PR-AUC difference between the models was found tobe significant for IHM and statistically insignificant for LOS at p=0.05. Inthis study, deep learning models were constructed using data combined from avariety of sources in Electronic Health Records (EHRs) such as chart data,input and output events, laboratory values, microbiology events, procedures,notes, and prescriptions. It is possible to predict in-hospital mortality withmuch better confidence and higher reliability from models built using allsources of data.

All Data Inclusive, Deep Learning Models to Predict Critical Events in the Medical Information Mart for Intensive Care III Database (MIMIC III)