Predicting and Understanding Unexpected Respiratory Decompensation in Critical Care Using Sparse and Heterogeneous Clinical Data

Abstract

Hospital intensive care units (ICUs) care for severely ill patients, many of whom require some form of organ support. Clinicians in ICUs are often challenged with integrating large volumes of continuously recorded physiological and clinical data in order to diagnose and treat patients. In this work, we focus on developing interpretable models for predicting unexpected respiratory decompensation requiring intubation in ICU patients. Predicting need for intubation could have important implications for the patient and medical staff and potentially enable timely interventions for improved patient outcome. Using data from adult ICU patients from the Medical Information Mart for Intensive Care (MIMIC)-III database, we developed gradient boosting models for predicting intubation onset. In a cohort of 12,470 patients, of whom 1,067 were intubated (8.55%), we achieved an area under the receiver operating characteristic curve (AUROC) of 0.89, with 95% confidence interval (CI) 0.87-0.91, when predicting intubation 3 hours ahead of time, a significant increase (p<0.001) over the AUROC achieved using several baselines, including logistic regression (0.81, 95% CI 0.78-0.84) and neural networks (0.80, 95% CI 0.77-0.83]). Finally, we conducted feature importance analysis using gradient boosting and derived useful insights in understanding the relative importance of clinical vs. biological variables in predicting impending respiratory decompensation in ICUs.

Secure and Robust Machine Learning for Healthcare: A Survey

“…by integrating clinical notes with multivariate and time-series measurements data [76]. In a similar study, the problem of unexpected respiratory decompensation using ML techniques is investigated in [77]. e) Clinical Natural Language Processing: Clinical notes are a widely used tool by the clinicians to communicate patient state.…”

Benchmarking machine learning models on multi-centre eICU critical care dataset

“…Recently machine learning researchers have started to apply various machine learning methods in order to predict the decompensation incident. Recent study by Ren et al [34] applied gradient boosting models (GBM) to predict required intubation 3 hours ahead of time, in this work they used a cohort of 12,470 patients to predict unexpected respiratory decompensation. Differently, Xu et al [35] proposed a deep learning model to predict the decompensation event.…”

Early prediction of circulatory failure in the intensive care unit using machine learning

“…With close to 5,000 variables collected and continuously monitored parameters updated every 2 minutes, this dataset contains over 3 billion data points. It has higher temporal resolution compared to the two publicly available ICU datasets (MIMIC-III 39 contains around 312 million data points and eICU 48 contains around 827 million) and non-ICU datasets 19,20,22 , allowing better characterization of patient states 49 . We have developed a comprehensive analysis framework including data pre-processing and cleaning, feature extraction and interpretation, and a selection of large-scale supervised machine learning techniques to construct circEWS.…”