A pre-trained language model for emergency department intervention prediction using routine physiological data and clinical narratives

IF 3.7 2区 医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Medical Informatics Pub Date : 2024-08-03 DOI:10.1016/j.ijmedinf.2024.105564
Ting-Yun Huang , Chee-Fah Chong , Heng-Yu Lin , Tzu-Ying Chen , Yung-Chun Chang , Ming-Chin Lin
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Abstract

Introduction

The urgency and complexity of emergency room (ER) settings require precise and swift decision-making processes for patient care. Ensuring the timely execution of critical examinations and interventions is vital for reducing diagnostic errors, but the literature highlights a need for innovative approaches to optimize diagnostic accuracy and patient outcomes. In response, our study endeavors to create predictive models for timely examinations and interventions by leveraging the patient’s symptoms and vital signs recorded during triage, and in so doing, augment traditional diagnostic methodologies.

Methods

Focusing on four key areas—medication dispensing, vital interventions, laboratory testing, and emergency radiology exams, the study employed Natural Language Processing (NLP) and seven advanced machine learning techniques. The research was centered around the innovative use of BioClinicalBERT, a state-of-the-art NLP framework.

Results

BioClinicalBERT emerged as the superior model, outperforming others in predictive accuracy. The integration of physiological data with patient narrative symptoms demonstrated greater effectiveness compared to models based solely on textual data. The robustness of our approach was confirmed by an Area Under the Receiver Operating Characteristic curve (AUROC) score of 0.9.

Conclusion

The findings of our study underscore the feasibility of establishing a decision support system for emergency patients, targeting timely interventions and examinations based on a nuanced analysis of symptoms. By using an advanced natural language processing technique, our approach shows promise for enhancing diagnostic accuracy. However, the current model is not yet fully mature for direct implementation into daily clinical practice. Recognizing the imperative nature of precision in the ER environment, future research endeavors must focus on refining and expanding predictive models to include detailed timely examinations and interventions. Although the progress achieved in this study represents an encouraging step towards a more innovative and technology-driven paradigm in emergency care, full clinical integration warrants further exploration and validation.

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利用常规生理数据和临床叙述预训练急诊科干预预测语言模型。
导言:急诊室(ER)环境的紧迫性和复杂性要求患者护理决策过程精确迅速。确保及时进行关键检查和干预对减少诊断错误至关重要,但文献强调需要创新方法来优化诊断准确性和患者预后。为此,我们的研究致力于利用分诊过程中记录的患者症状和生命体征,创建及时检查和干预的预测模型,从而增强传统的诊断方法:方法:这项研究重点关注四个关键领域--配药、生命体征干预、实验室检测和急诊放射检查,采用了自然语言处理(NLP)和七种先进的机器学习技术。研究以 BioClinicalBERT(一种最先进的 NLP 框架)的创新使用为中心:结果:BioClinicalBERT 成为最优秀的模型,其预测准确性优于其他模型。与仅基于文本数据的模型相比,生理数据与患者叙述症状的整合显示出更大的有效性。接收者工作特征曲线下面积 (AUROC) 得分为 0.9,这证实了我们方法的稳健性:我们的研究结果强调了为急诊病人建立决策支持系统的可行性,该系统可根据对症状的细致分析,有针对性地进行及时干预和检查。通过使用先进的自然语言处理技术,我们的方法有望提高诊断准确性。然而,目前的模式尚未完全成熟,无法直接应用于日常临床实践。认识到急诊室环境中精确性的必要性,未来的研究工作必须侧重于完善和扩展预测模型,以包括详细的及时检查和干预措施。尽管本研究取得的进展代表着急诊护理向更具创新性和技术驱动型模式迈出了令人鼓舞的一步,但全面的临床整合还需要进一步的探索和验证。
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来源期刊
International Journal of Medical Informatics
International Journal of Medical Informatics 医学-计算机:信息系统
CiteScore
8.90
自引率
4.10%
发文量
217
审稿时长
42 days
期刊介绍: International Journal of Medical Informatics provides an international medium for dissemination of original results and interpretative reviews concerning the field of medical informatics. The Journal emphasizes the evaluation of systems in healthcare settings. The scope of journal covers: Information systems, including national or international registration systems, hospital information systems, departmental and/or physician''s office systems, document handling systems, electronic medical record systems, standardization, systems integration etc.; Computer-aided medical decision support systems using heuristic, algorithmic and/or statistical methods as exemplified in decision theory, protocol development, artificial intelligence, etc. Educational computer based programs pertaining to medical informatics or medicine in general; Organizational, economic, social, clinical impact, ethical and cost-benefit aspects of IT applications in health care.
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