Predicting clinical outcomes for patients in the intensive care units (ICUs) is crucial for physicians to assess clinical risk and provide timely and appropriate interventions. Current models often fail to predict multiple clinical outcomes simultaneously, and patient similarity has not been fully utilized to improve prediction accuracy. Additionally, electronic health record (EHR) data in the ICUs often contain observations recorded at irregular time intervals, which existing prediction methods do not effectively model. To address these challenges, we propose the ICU-TGNN model. This model combines time attention-based Transformer and graph neural network (GNN) architectures to leverage temporal and relational patient data. The Transformer component analyzes time series EHR data to capture dynamic patient states over time, while the GNN component exploits the relational structure among patients based on comorbidities to enhance model generalizability. We evaluated the ICU-TGNN model on the eICU-CRD dataset, demonstrating its superior capability in predicting general clinical outcomes such as ICU mortality and length of ICU stay. Our findings highlight ICU-TGNN's capability to provide accurate outcome predictions by effectively handling the complexity of ICU data, thereby holding great potential to optimize patient management and improve clinical outcomes.

We propose to employ evidential reasoning (ER) rule to construct a clinical decision support system (CDSS) to aid physicians to predict the probability of intensive care unit (ICU) admission and in-hospital death for trauma patients once they arrive at a hospital. A generalized Bayesian rule is used to mine evidence from historical data. Evidence is profiled using a format of belief distribution, where the belief degrees of different trauma outcomes are assigned with derived probabilities linked to the corresponding outcomes. Inputs to the CDSS are clinical data of a patient, and output from the system is predicted belief degree of severe trauma, including ICU admission and in-hospital death. The inner logic of the CDSS is that pieces of evidence that match the clinical data of a patient are identified from the evidence base first, and then the ER rule-based evidence aggregation mechanism is utilized to combine the matched evidences to arrive at a prediction. The reliability, weight, and interdependence of clinical evidence are taken into account. Moreover, an evidence weight training module is constructed. The ER rule-based prediction model has superior performance compared with logistic regression and artificial neural network models. An innovative and pragmatic ER rule-based CDSS for trauma outcome prediction is contributed by this article. In the era of big data, this CDSS helps predict patient outcomes based on historical data and helps physicians in emergency departments make proper trauma management decisions.

Abstract: A critical issue in the clinical decision support system (CDSS) research area is how to represent and reason with both uncertain medical domain knowledge and clinical symptoms to arrive at accurate conclusions. Although a number of methods and tools have been developed in the past two decades for modelling clinical guidelines, few of those modelling methods have capabilities of handling the uncertainties that exist in almost every stage of a clinical decision‐making process. This paper describes how to apply a recently developed generic rule‐base inference methodology using the evidential reasoning approach (RIMER) to model clinical guidelines and the clinical inference process in a CDSS. In RIMER, a rule base is designed with belief degrees embedded in all possible consequents of a rule. Such a rule base is capable of capturing vagueness, incompleteness and non‐linear causal relationships, while traditional IF–THEN rules can be represented as a special case. Inference in such a rule base is implemented using the evidential reasoning approach which has the capability of handling different types and degrees of uncertainty in both medical domain knowledge and clinical symptoms. A case study demonstrates that employing RIMER in developing a guideline‐based CDSS is a valid novel approach.