AI-Techniques Loss-Based Algorithm for Severity Classification (ATLAS): a novel approach for continuous quantification of exertional symptoms during incremental exercise testing.

IF 4.7 2区医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of the American Medical Informatics Association Pub Date : 2025-03-27 DOI:10.1093/jamia/ocaf051

Abed A Hijleh, Sophia Wang, Danilo C Berton, Igor Neder-Serafini, Sandra Vincent, Matthew James, Nicolle Domnik, Devin Phillips, Luiz E Nery, Denis E O'Donnell, J Alberto Neder

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引用次数: 0

Abstract

Objective: Heightened muscular effort and breathlessness (dyspnea) are disabling sensory experiences. We sought to improve the current approach of assessing these symptoms only at the maximal effort to new paradigms based on their continuous quantification throughout cardiopulmonary exercise testing (CPET).

Materials and methods: After establishing sex- and age-adjusted reference centiles (0-10 Borg scale), we developed a novel algorithm (AI-Techniques Loss-Based Algorithm for Severity Classification [ATLAS]) based on reciprocal exponential loss for CPET data from patients with chronic obstructive lung disease of varied severity.

Results: Categories of dyspnea intensity by ATLAS-but not dyspnea at peak exercise-correctly discriminated patients in progressively higher resting and exercise impairment (P < .05).

Discussion: This new AI-techniques approach will be translated to the care of disabled patients to uncover the seeds and consequences of their activity-related symptoms.

Conclusions: We used innovative informatics research to change paradigms in displaying, quantifying, and analyzing effort-related symptoms in patient populations.

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基于损失的严重程度分类算法（ATLAS）：一种在增量运动试验中连续量化运动症状的新方法。

目的：增强的肌肉用力和呼吸困难（呼吸困难）是致残的感觉体验。我们试图改进目前仅在最大程度上评估这些症状的方法，以在心肺运动试验（CPET）中持续量化这些症状的新范式。材料和方法：在建立了性别和年龄调整的参考百分位数（0-10 Borg量表）后，我们基于不同严重程度慢性阻塞性肺病患者CPET数据的倒数指数损失，开发了一种新的算法（AI-Techniques loss - based algorithm for Severity Classification [ATLAS]）。结果：atlas的呼吸困难强度分类——但不是运动高峰时的呼吸困难——正确地区分了渐进式高静息和运动障碍患者(P讨论：这种新的人工智能技术方法将被转化为残疾患者的护理，以揭示其活动相关症状的根源和后果。结论：我们使用创新的信息学研究来改变患者群体中与努力相关的症状的显示、量化和分析范式。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the American Medical Informatics Association 医学-计算机：跨学科应用

CiteScore

14.50

自引率

7.80%

发文量

230

审稿时长

3-8 weeks

期刊介绍： JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.