Kidney Disease (KD) may hide complex causes and is associated with a tremendous socio-economic impact. Timely identification and management from the first level of medical care represent the most effective strategy to address the growing global burden sustainably. Clinical practice guidelines suggest utilizing estimated Glomerular Filtration Rate (eGFR) for routine evaluation within a screening purpose. Accordingly, the analysis of Electronic Health Records (EHRs) using Machine Learning techniques offers great opportunities to monitor and predict the eGFR trend over time. This paper aims to propose a novel Semi-Supervised Multi-Task Learning (SS-MTL) approach for predicting short-term KD evolution on multiple General Practitioners’ EHR data. We demonstrated that the SS-MTL approach can (i) capture the eGFR temporal evolution by imposing a temporal relatedness between consecutive time windows and (ii) exploit useful information from unlabeled patients when labeled patients are less numerous with a gain of up to 4.1% in terms of Recall . This situation reflects the real-case scenario, where available labeled samples are limited, but those unlabeled much more abundant. The SS-MTL approach, also given the high level of interpretability, might be the ideal candidate in general practice to get integrated within a decision support system for KD screening purposes.

中文翻译：

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一种预测短期肾脏疾病演变的半监督多任务学习方法

肾脏疾病 (KD) 可能隐藏复杂的原因，并与巨大的社会经济影响有关。从第一级医疗保健及时识别和管理是可持续解决日益增长的全球负担的最有效策略。临床实践指南建议使用估计的肾小球滤过率 (eGFR) 进行筛查目的的常规评估。因此，使用机器学习技术对电子健康记录 (EHR) 的分析提供了监测和预测 eGFR 随时间变化趋势的绝佳机会。本文旨在提出一种新的半监督多任务学习 (SS-MTL) 方法，用于预测多个全科医生 EHR 数据的短期 KD 演变。记起 。这种情况反映了真实情况，可用的标记样本有限，但未标记的样本要丰富得多。SS-MTL 方法也具有高度的可解释性，可能是一般实践中整合到决策支持系统中以进行 KD 筛查的理想选择。