Machine Learning Can Predict Deaths in Patients with Diverticulitis During their Hospital Stay

Abstract

ABSTRACTIntroductionDiverticulitis is the inflammation and/or infection of small pouches known as diverticula that develop along the walls of the intestines. Patients with diverticulitis are at risk of mortality as high as 17% with abscess formation and 45% with secondary perforation, especially patients that get admitted to the inpatient services are at risk of complications including mortality. We developed a deep neural networks (DNN) based machine learning framework that could predict premature death in patients that are admitted with diverticulitis using electronic health records (EHR) to calculate the statistically significant risk factors first and then to apply deep neural network.MethodsOur proposed framework (Deep FLAIM) is a two-phase hybrid works framework. In the first phase, we used National In-patient Sample 2014 dataset to extract patients with diverticulitis patients with and without hemorrhage with the ICD-9 codes 562.11 and 562.13 respectively and analyzed these patients for different risk factors for statistical significance with univariate and multivariate analyses to generate hazard ratios, to rank the diverticulitis associated risk factors. In the second phase, we applied deep neural network model to predict death. Additionally, we have compared the performance of our proposed system by using the popular machine learning models such as DNN and Logistic Regression (LR).ResultsA total of 128,258 patients were used, we tested 64 different variables for using univariate and multivariate (age, gender and ethnicity) cox-regression for significance only 16 factors were statistically significant for both univariate and multivariate analysis. The mortality prediction for our DNN out-performed the conventional machine learning (logistic regression) in terms of AUC (0.977 vs 0.904), training accuracy (0.931 vs 0.900), testing accuracy (0.930 vs 0.910), sensitivity (90% vs 88%) and specificity (95% vs 93%).ConclusionOur Deep FLAIM Framework can predict mortality in patients admitted to the hospital with diverticulitis with high accuracy. The proposed framework can be expanded to predict premature death for other disease.