# Predicting Decompensation Risk in Intensive Care Unit Patients Using Machine Learning
###### Abstract  
Patients in Intensive Care Units (ICU) face the threat of decompensation, a rapid decline in health associated with a high risk of death. This study focuses on creating and evaluating machine learning (ML) models to predict decompensation risk in ICU patients. It proposes a novel approach using patient vitals and clinical data within a specified timeframe to forecast decompensation risk sequences. The study implemented and assessed long short-term memory (LSTM) and hybrid convolutional neural network (CNN)-LSTM architectures, along with traditional ML algorithms as baselines. Additionally, it introduced a novel decompensation score based on the predicted risk, validated through principal component analysis (PCA) and k-means analysis for risk stratification. The results showed that, with PPV = 0.80, NPV = 0.96 and AUC-ROC = 0.90, CNN-LSTM had the best performance when predicting decompensation risk sequences. The decompensation score's effectiveness was also confirmed (PPV = 0.83 and NPV = 0.96). SHAP plots were generated for the overall model and two risk strata, illustrating variations in feature importance and their associations with the predicted risk. Notably, this study represents the first attempt to predict a sequence of decompensation risks rather than single events, a critical advancement given the challenge of early decompensation detection. Predicting a sequence facilitates early detection of increased decompensation risk and pace, potentially leading to saving more lives.  
decompensation; risk prediction; intensive care unit; machine learning; deep learning; feature engineering; temporal data analysis; explainable artificial intelligence; clinical decision support +
Footnote †: journal: _Algorithms_  
on static data and physiological time-series data sourced from the Danish National Patient Registry, obtaining equivalent performance results. The use of DL is attractive as it can efficiently capture dynamic fluctuations in vitals and other clinical characteristics, thereby enhancing model performance.  
One major criticism of DL models has traditionally been their difficulty in explaining predictions, which is an essential requirement in medical research and healthcare applications. In recent years, there has been an increased effort to develop Explainable AI (xAI) algorithms specifically for DL models, particularly in health research. [PERSON] et al. [5] combined Learned Binary Masks (LBM) with Kernel Shapley Additive exPlanations (KernelSHAP) values to explain Recurrent Neural Network (RNN) mortality risk prediction models in critically ill children using electronic medical records (EMR). Another approach, named Windowed Feature Importance in Time (WinIT) [6], encapsulates the changing importance of a feature over time, providing an aggregated understanding of its significance by cumulatively assessing feature importance over a window of preceding time steps.  
This study aims to develop and assess machine learning (ML) models for predicting the risk of decompensation events in patients admitted to ICUs. Specifically, we propose a novel methodological approach that implements a sequence-to-sequence risk prediction task. It utilises a sequence of patient's vitals and other clinical characteristics within a specified time window to forecast a decompensation risk sequence in a subsequent time window (i.e., forecast window). For this purpose, we considered two DL architectures: the many-to-many long short-term memory (LSTM) [7] and the hybrid convolutional neural network and LSTM (CNN-LSTM) [8].  
Our approach reflects the dynamic nature of patient decompensation, rather than treating it as a single event. We used the predicted sequence to propose a novel decompensation score. Additionally, predicting a sequence could enable earlier detection of decompensation, facilitating prompt intervention and potentially leading to improved patient outcomes.