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Monitoring patients in ICU is a challenging and high-cost task. Hence,predicting the condition of patients during their ICU stay can help providebetter acute care and plan the hospital's resources. There has been continuousprogress in machine learning research for ICU management, and most of this workhas focused on using time series signals recorded by ICU instruments. In ourwork, we show that adding clinical notes as another modality improves theperformance of the model for three benchmark tasks: in-hospital mortalityprediction, modeling decompensation, and length of stay forecasting that playan important role in ICU management. While the time-series data is measured atregular intervals, doctor notes are charted at irregular times, making itchallenging to model them together. We propose a method to model them jointly,achieving considerable improvement across benchmark tasks over baselinetime-series model. Our implementation can be found at\url{https://github.com/kaggarwal/ClinicalNotesICU}.

Using Clinical Notes with Time Series Data for ICU Management