Explainable machine learning for predicting neurological outcome in hemorrhagic and ischemic stroke patients in critical care

 Just maybe you want to deliver recovery instead of this useless crapola of predicting failure to recover! I'd have you all fired!

Explainable machine learning for predicting neurological outcome in hemorrhagic and ischemic stroke patients in critical care

Huawei Wei^1†Xingshuai Huang^1†Yixuan Zhang^1†Guowei Jiang^1†Ruifeng Ding¹Mengqiu Deng¹Liangtian Wei²Hongbin Yuan^1*

• ¹Department of Anesthesiology, Changzheng Hospital, Second Affiliated Hospital of Naval Medical University, Shanghai, China
• ²Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical University, Xuzhou, China

Aim: The objective of this study is to develop accurate machine learning (ML) models for predicting the neurological status at hospital discharge of critically ill patients with hemorrhagic and ischemic stroke and identify the risk factors associated with the neurological outcome of stroke, thereby providing healthcare professionals with enhanced clinical decision-making guidance.

Materials and methods: Data of stroke patients were extracted from the eICU Collaborative Research Database (eICU-CRD) for training and testing sets and the Medical Information Mart for Intensive Care IV (MIMIC IV) database for external validation. Four machine learning models, namely gradient boosting classifier (GBC), logistic regression (LR), multi-layer perceptron (MLP), and random forest (RF), were used for prediction of neurological outcome. Furthermore, shapley additive explanations (SHAP) algorithm was applied to explain models visually.

Results: A total of 1,216 hemorrhagic stroke patients and 954 ischemic stroke patients from eICU-CRD and 921 hemorrhagic stroke patients 902 ischemic stroke patients from MIMIC IV were included in this study. In the hemorrhagic stroke cohort, the LR model achieved the highest area under curve (AUC) of 0.887 in the test cohort, while in the ischemic stroke cohort, the RF model demonstrated the best performance with an AUC of 0.867 in the test cohort. Further analysis of risk factors was conducted using SHAP analysis and the results of this study were converted into an online prediction tool.

Conclusion: ML models are reliable tools for predicting hemorrhagic and ischemic stroke neurological outcome and have the potential to improve critical care of stroke patients. The summarized risk factors obtained from SHAP enable a more nuanced understanding of the reasoning behind prediction outcomes and the optimization of the treatment strategy.

Introduction

Stroke encompasses a set of conditions characterized by the sudden rupture or occlusion of cerebral blood vessels, ultimately resulting in insufficient blood flow and subsequent damage to brain tissue. Clinically, stroke is broadly classified into two main types—ischemic and hemorrhagic—with the latter comprising intracerebral and subarachnoid hemorrhage forms (1). Stroke affects a staggering one in every four individuals over 25 years of age, rendering it the second most common cause of mortality and third leading cause of disability among adult populations worldwide (2). Approximately 16 million people worldwide suffer from various motor and cognitive impairments as a result of stroke, which are often unavoidable sequelae for stroke patients, and severely affects the mobility and quality of life of stroke victims (3).

Acute stroke patients often enter the intensive care unit (ICU) due to consciousness disorders, cardiopulmonary complications, circulatory instability, or acute thrombolytic therapy (4). Compared with patients admitted to a dedicated neurological ward or stroke unit, those with stroke who are admitted to the ICU exhibit heightened neurological severity, notable impairment of consciousness at a moderate to severe level, often necessitating mechanical ventilation, and encounter an elevated risk of hospital mortality (5, 6). ICU provides complex and resource-intensive treatment for hospitalized patients with severe conditions, but current medical resources are often insufficient to meet the needs of ICU patients, and hospitals face pressure to improve critical care efficiency and reduce costs (7). Early prediction of neurological outcome in critically ill stroke patients can provide important references for patients and their families, and can also guide clinicians to give the best intervention measures to patients.

In contrast to conventional predictive models that rely on established variables for computation, machine learning (ML) approaches offer the distinct advantage of incorporating a broader range of variables that more comprehensively capture the intricacies and inherent unpredictability of human physiology (8, 9). Consequently, ML has emerged as a promising tool in the medical field, with its capacity to integrate abundant variables, extract nuanced insights, and generalize acquired knowledge to novel cases with remarkable efficiency and precision (10, 11). Furthermore, interpretable machine learning is increasingly being applied in clinical research, demonstrating robust clinical applicability and guiding capabilities (12, 13).

In this work, we aimed to construct ML models for early and effective prediction of neurological outcome at hospital discharge in critically ill patients with hemorrhagic and ischemic stroke, and employed the shapley additive explanations (SHAP) methods to elucidate the underlying reasons and decision-making processes involved within the optimal algorithm.

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