Pennestrì F, Cabitza F, Picerno N, Banfi G
Direzione Scientifica, IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso 173, 20157, Milano, MI, Italy.
Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi Milano-Bicocca, Viale Sarca 126, 20125, Milano, MI, Italy.
BMC Med Inform Decis Mak. 2025 Feb 4;25(1):56. doi: 10.1186/s12911-025-02883-2.
Training machine learning models using data from severe COVID-19 patients admitted to a central hospital, where entire wards are specifically dedicated to COVID-19, may yield predictions that differ significantly from those generated using data collected from patients admitted to a high-volume specialized hospital for orthopedic surgery, where COVID-19 is only a secondary diagnosis. This disparity arises despite the two hospitals being geographically close (within20 kilometers). While machine learning can facilitate rapid public health responses, rigorous external validation and continuous monitoring are essential to ensure reliability and safety.
使用来自一家中心医院收治的重症新冠肺炎患者的数据来训练机器学习模型，该医院有专门用于新冠肺炎的整个病房，其产生的预测结果可能与使用从一家大型骨科专科医院收治的患者收集的数据所产生的预测结果有显著差异，在那家骨科专科医院，新冠肺炎只是次要诊断。尽管两家医院地理位置相近（在20公里范围内），但仍存在这种差异。虽然机器学习可以促进快速的公共卫生应对，但严格的外部验证和持续监测对于确保可靠性和安全性至关重要。
