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LSTM for septic shock: Adding unreliable labels to reliable predictions

Sepsis is a leading cause of death over the world and septic shock, the most severe complication of sepsis, reaches a mortality rate as high as 50%. Early diagnosis and treatment can prevent most morbidity and mortality. Nowadays, the increasing availability of the electronic health records (EHRs) has generated great interests in developing models to predict acute medical conditions such as septic shock. However, septic shock prediction faces two major challenges : 1) how to capture the informative progression of septic shock in a long visit to hospital of a patient; and 2) how to obtain reliable predictions without well-established moment-by-moment ground-truth labels for septic shock. In this work, we proposed a generic framework to predict septic shock based on Long-Short Term Memory (LSTM) model, which is capable of memorizing temporal dependencies over a long period. The framework integrates two levels of imperfect yet informative labels to jointly learn the discriminative patterns of septic shock: ICD-9 code as the visit-level label and the clinical criteria designed by domain experts as the moment-by-moment event-level label. We evaluate our method on a real-world data extracted from an EHR system constituted by 12,954 visits and 1,348,625 events, and compare it against multiple baselines. The robustness of the method is validated using three sets of clinician-proposed adjusted ground-truth labels. Also, we explore whether the framework is effective for the early prediction of the patients developing septic shock. The experimental results demonstrate the superiority of our proposed method in the task of septic shock prediction.