Bayesian intelligence for medical diagnosis: a pilot study on patient disposition for emergency medicine chest pain.

IF 2.2 Q2 MEDICINE, GENERAL & INTERNAL Diagnosis Pub Date : 2024-10-25 DOI:10.1515/dx-2024-0049
Mark W Perlin, Yves-Dany Accilien
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Abstract

Objectives: Clinicians can rapidly and accurately diagnose disease, learn from experience, and explain their reasoning. Computational Bayesian medical decision-making might replicate this expertise. This paper assesses a computer system for diagnosing cardiac chest pain in the emergency department (ED) that decides whether to admit or discharge a patient.

Methods: The system can learn likelihood functions by counting data frequency. The computer compares patient and disease data profiles using likelihood. It calculates a Bayesian probabilistic diagnosis and explains its reasoning. A utility function applies the probabilistic diagnosis to produce a numerical BAYES score for making a medical decision.

Results: We conducted a pilot study to assess BAYES efficacy in ED chest pain patient disposition. Binary BAYES decisions eliminated patient observation. We compared BAYES to the HEART score. On 100 patients, BAYES reduced HEART's false positive rate 18-fold from 58.7 to 3.3 %, and improved ROC AUC accuracy from 0.928 to 1.0.

Conclusions: The pilot study results were encouraging. The data-driven BAYES score approach could learn from frequency counting, make fast and accurate decisions, and explain its reasoning. The computer replicated these aspects of diagnostic expertise. More research is needed to reproduce and extend these finding to larger diverse patient populations.

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用于医疗诊断的贝叶斯智能:关于急诊胸痛患者处置的试点研究。
目标:临床医生可以快速准确地诊断疾病,从经验中学习,并解释他们的推理。计算贝叶斯医疗决策可以复制这种专业知识。本文对急诊科(ED)中诊断心脏胸痛的计算机系统进行了评估,该系统可决定患者入院还是出院:该系统可通过计算数据频率来学习似然函数。方法:该系统可通过计算数据频率来学习似然函数。计算机利用似然函数比较病人和疾病的数据资料。它能计算出贝叶斯概率诊断并解释其推理。效用函数应用概率诊断得出贝叶斯数字评分,用于做出医疗决策:我们进行了一项试点研究,以评估 BAYES 在急诊室胸痛患者处置中的功效。二进制 BAYES 决策无需对患者进行观察。我们将 BAYES 与 HEART 评分进行了比较。在 100 名患者中,BAYES 将 HEART 的误判率从 58.7% 降低到 3.3%,降低了 18 倍,并将 ROC AUC 准确率从 0.928 提高到 1.0:试点研究结果令人鼓舞。数据驱动的 BAYES 评分方法可以从频率计数中学习,做出快速准确的决定,并解释其推理。计算机复制了诊断专业知识的这些方面。还需要进行更多的研究,以便将这些发现推广到更多不同的患者群体中。
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来源期刊
Diagnosis
Diagnosis MEDICINE, GENERAL & INTERNAL-
CiteScore
7.20
自引率
5.70%
发文量
41
期刊介绍: Diagnosis focuses on how diagnosis can be advanced, how it is taught, and how and why it can fail, leading to diagnostic errors. The journal welcomes both fundamental and applied works, improvement initiatives, opinions, and debates to encourage new thinking on improving this critical aspect of healthcare quality.  Topics: -Factors that promote diagnostic quality and safety -Clinical reasoning -Diagnostic errors in medicine -The factors that contribute to diagnostic error: human factors, cognitive issues, and system-related breakdowns -Improving the value of diagnosis – eliminating waste and unnecessary testing -How culture and removing blame promote awareness of diagnostic errors -Training and education related to clinical reasoning and diagnostic skills -Advances in laboratory testing and imaging that improve diagnostic capability -Local, national and international initiatives to reduce diagnostic error
期刊最新文献
A decision support system to increase the compliance of diagnostic imaging examinations with imaging guidelines: focused on cerebrovascular diseases. Bayesian intelligence for medical diagnosis: a pilot study on patient disposition for emergency medicine chest pain. Bringing team science to the ambulatory diagnostic process: how do patients and clinicians develop shared mental models? From stable teamwork to dynamic teaming in the ambulatory care diagnostic process. Interventions to improve timely cancer diagnosis: an integrative review.
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