Kidney Disease (KD) may hide complex causes and is associated with a tremendous socio-economic impact. A 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 is able to (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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半监督的多任务学习方法，用于预测短期肾脏疾病的发展。

肾脏疾病（KD）可能掩盖了复杂的原因，并且与巨大的社会经济影响相关。从第一级医疗服务中及时识别和管理代表了可持续解决日益增长的全球负担的最有效策略。临床实践指南建议在筛查目的范围内利用估计的肾小球滤过率（eGFR）进行常规评估。因此，使用机器学习技术对电子病历（EHR）进行分析为监测和预测eGFR随时间变化的趋势提供了巨大的机会。本文旨在提出一种新颖的半监督多任务学习（SS-MTL）方法，用于基于多个全科医生EHR数据预测短期KD演变。我们证明了SS-MTL方法能够（i）通过在连续的时间窗口之间施加时间相关性来捕获eGFR的时间演变，以及（ii）当标记患者数量较少且获益最多时，利用未标记患者的有用信息召回率达到4.1％。这种情况反映了实际情况，在这种情况下，可用标签的样本有限，但未标签的样本要丰富得多。SS-MTL方法也具有较高的可解释性，它可能是一般实践中用于KD筛选目的而集成到决策支持系统中的理想候选方法。这种情况反映了实际情况，在这种情况下，可用标记的样本数量有限，但未标记的样本数量更多。SS-MTL方法也具有较高的可解释性，它可能是一般实践中用于KD筛选目的而集成到决策支持系统中的理想候选方法。这种情况反映了实际情况，在这种情况下，可用标签的样本有限，但未标签的样本要丰富得多。SS-MTL方法也具有较高的可解释性，它可能是一般实践中用于KD筛选目的而集成到决策支持系统中的理想候选方法。