用AI分析病历预测神经外科术后重症入院，减少40%漏判。
Developing and Evaluating an AI-Assisted Prediction Model for Unplanned Intensive Care Admissions following Elective Neurosurgery using Natural Language Processing within an Electronic Healthcare Record System
- 用NLP从电子病历中提取临床概念，训练预测模型
- 决策树模型召回率达87%，漏判率从36%降至4%
- 适合临床风险预警与智能排班系统研发者参考
及时转入神经重症监护室可降低死亡率并缩短住院时间，但术后护理决策仍依赖主观判断。本研究利用人工智能（特别是自然语言处理，NLP）分析伦敦大学学院医院（UCLH）的电子健康记录（EHR），预测择期神经外科手术患者是否需转入重症监护室（ITU）。通过医学概念标注工具（MedCAT）识别临床笔记中的SNOMED-CT概念，构建并优化了基于认知的NLP模型。该模型经正常压力性脑积水（NPH）和前庭神经鞘瘤（VS）患者数据两次微调，概念识别F1得分为0.93。最终在2,268名符合条件的神经外科患者中提取特征，并整合至决策树与神经时间序列模型中。采用简化决策树模型后，对ITU入院的召回率达到0.87（95%置信区间0.82–0.91），使人类专家漏判的非计划性重症入院比例从36%降至4%。结果表明，经过优化的NLP模型能高效提取关键临床信息，为临床可用的预测模型提供可靠基础。
原文摘要 · Abstract (English)
Introduction: Timely care in a specialised neuro-intensive therapy unit (ITU) reduces mortality and hospital stays, with planned admissions being safer than unplanned ones. However, post-operative care decisions remain subjective. This study used artificial intelligence (AI), specifically natural language processing (NLP) to analyse electronic health records (EHRs) and predict ITU admissions for elective surgery patients. Methods: This study analysed the EHRs of elective neurosurgery patients from University College London Hospital (UCLH) using NLP. Patients were categorised into planned high dependency unit (HDU) or ITU admission; unplanned HDU or ITU admission; or ward / overnight recovery (ONR). The Medical Concept Annotation Tool (MedCAT) was used to identify SNOMED-CT concepts within the clinical notes. We then explored the utility of these identified concepts for a range of AI algorithms trained to predict ITU admission. Results: The CogStack-MedCAT NLP model, initially trained on hospital-wide EHRs, underwent two refinements: first with data from patients with Normal Pressure Hydrocephalus (NPH) and then with data from Vestibular Schwannoma (VS) patients, achieving a concept detection F1-score of 0.93. This refined model was then used to extract concepts from EHR notes of 2,268 eligible neurosurgical patients. We integrated the extracted concepts into AI models, including a decision tree model and a neural time-series model. Using the simpler decision tree model, we achieved a recall of 0.87 (CI 0.82 - 0.91) for ITU admissions, reducing the proportion of unplanned ITU cases missed by human experts from 36% to 4%. Conclusion: The NLP model, refined for accuracy, has proven its efficiency in extracting relevant concepts, providing a reliable basis for predictive AI models to use in clinically valid applications.
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