用生物标志物预训练模型，提升心脏病筛查准确率
Biomarker-Based Pretraining for Chagas Disease Screening in Electrocardiograms
- 先用血检指标训练心电图特征提取器，解决标签少的问题
- 在巴西数据集上微调后，检测挑战赛得分0.269，排名第五
- 适合做医疗信号分析、低资源疾病筛查的研究者参考
基于心电图（ECG）的查加斯病筛查受限于现有数据集中的稀缺且噪声较大的标签。本文提出一种基于生物标志物的预训练方法：首先在MIMIC-IV-ECG数据集上训练一个心电图特征提取器，使其预测分位数分组的血液生物标志物；随后将该预训练模型在巴西数据集上进行微调，用于查加斯病检测。由Ahus AIM团队开发的五模型集成系统在隐测试集上获得0.269的挑战赛评分，在2025年乔治·莫迪生理学网挑战赛中排名第5。源代码和模型已公开于GitHub：github.com/Ahus-AIM/physionet-challenge-2025。
原文摘要 · Abstract (English)
Chagas disease screening via ECGs is limited by scarce and noisy labels in existing datasets. We propose a biomarker-based pretraining approach, where an ECG feature extractor is first trained to predict percentile-binned blood biomarkers from the MIMIC-IV-ECG dataset. The pretrained model is then fine-tuned on Brazilian datasets for Chagas detection. Our 5-model ensemble, developed by the Ahus AIM team, achieved a challenge score of 0.269 on the hidden test set, ranking 5th in Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025. Source code and the model are shared on GitHub: github.com/Ahus-AIM/physionet-challenge-2025
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