… risk prediction system or ARPS, which considers these changing characteristics and automatically
deploys updated risk … and secondly a probabilistic risk modelling system that adapts to …

… In this work, we formulate the learning task in the target domain as stroke risk prediction in
a local hospital (see Fig. 1). As the stroke data in the target domain is usually insufficient, the …

… the prediction analysis of whole-genome sequencing data. We present here an explainable
deep transfer learning … Our proposed method can detect predictive genes that harbor genetic …

… to improve the learning performance of … transfer learning (Li et al., 2022), we propose a
novel statistical method called TL-Multi to enhance the transferability of polygenic risk prediction …

… models, thereby limiting the clinical utility of these prediction models. … transfer-learning
paradigm with ensemble learning algorithms to develop explainable personalised risk prediction …

… Since eMERGE adopts a centralized data sharing model where all individual-level data
can be pooled, we evaluate the centralized transfer learning approach, which is the same as …

… Using LIBLINEAR,16 we learn three different risk prediction models based on the approaches
described in the ‘Risk stratification’ section. We apply each classifier to the same hold-out …

… learning–based risk scoring systems. In this study, we implement a statistical transfer learning
… We test this transfer technique with AutoTriage, a mortality prediction algorithm, on patient …

… prediction with limited training data for each patient. In this paper, we conduct a systematic
study of transfer learning in … Our results show that transfer learning yields promising results in …

… predictions for three of the four metrics. As pre-trained language models improve, we are
optimistic that transfer learning will accelerate progress in early risk prediction on the internet. …