Monitoring patients in ICU is a challenging and high-cost task. Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources. There has been continuous progress in machine learning research for ICU management, and most of this work has focused on using time series signals recorded by ICU instruments. In our work, we show that adding clinical notes as another modality improves the performance of the model for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting that play an important role in ICU management. While the time-series data is measured at regular intervals, doctor notes are charted at irregular times, making it challenging to model them together. We propose a method to model them jointly, achieving considerable improvement across benchmark tasks over baseline time-series model. With the advancement of medical technology, patients admitted into the intensive care unit (ICU) are monitored by different instruments on their bedside, which measure different vital signals about patient's health. During their stay, doctors visit the patient intermittently for check-ups and make clinical notes about the patient's health and physiological progress. These notes can be perceived as summarized expert knowledge about the patient's state. All these data about instrument readings, procedures, lab events, and clinical notes are recorded for reference. Availability of ICU data and enormous progress in machine learning have opened up new possibilities for health care research. Monitoring patients in ICU is a challenging and high-cost task. Hence, predicting the condition of patients during their ICU stay can help plan better resource usage for patients that need it most in a cost-effective way. Prior works (Harutyunyan et al., 2017; BID4 BID18 BID16 BID1 have focused exclusively on modeling the problem using the time series signals from medical instruments. Expert knowledge from doctor's notes has been ignored in the literature.In this work, we use clinical notes in addition to the time-series data for improved prediction on benchmark ICU management tasks (Harutyunyan et al., 2017) . While the time-series data is measured continuously, the doctor notes are charted at intermittent times. This creates a new challenge to model continuous time series and discrete time note events jointly. We propose such a multi-modal deep neural network that comprises of recurrent units for the time-series and convolution network for the clinical notes. We demonstrate that adding clinical notes improves the AUC-PR scores on in-hospital mortality prediction (+7.8%) and modeling decompensation (+6.1%), and kappa score on length of stay forecasting (+3.4%). Identifying the patient's condition in advance is of critical importance for acute care and ICU management. Literature has exclusively focused on using time-series measurements from ICU instruments to this end. In this work, we demonstrate that utilizing clinical notes along with time-series data can improve the prediction performance significantly. In the future, we expect to improve more using advanced models for the clinical notes since text summarizes expert knowledge about a patient's condition.
We demostarte that using clinical notes in conjuntion with ICU instruments data improves the perfomance on ICU management benchmark tasks
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