In the rapidly evolving online display advertising market, conversion rate (CVR) prediction models are typically updated daily using datasets enriched with recent conversion logs. However, a significant challenge is the time gap, often spanning days or weeks, between ad clicks and conversions. This issue, known as delayed feedback, results in false negatives in training data, creating a dilemma between label accuracy and data freshness. Existing methods for mitigating delayed feedback are limited, due to strong underlying assumptions, insufficient use of recent data without observed conversions, or implicit control over false negatives. To address this, we propose a simple framework that redefines CVR prediction under delayed feedback as an unsupervised domain adaptation (UDA) problem. Our method learns from fresh data while minimizing the impact of inaccurate labels, by integrating existing click-through rate (CTR) or CVR models with UDA algorithms. A customized pretraining step is also incorporated to effectively utilize recent observed conversions. Comprehensive experiments on three datasets showcase the proposed method's superiority over state-of-the-art approaches and its potential to benefit from advancements in CTR modeling. The code is available at https://github.com/ThunderbornSakana/DelayAdapter.

Personalized diagnosis prediction based on electronic health records (EHR) of patients is a promising yet challenging task for AI in healthcare. Existing studies typically ignore the heterogeneity of diseases across different patients. For example, diabetes can have different complications across different patients (e.g., hyperlipidemia and circulatory disorder), which requires personalized diagnoses and treatments. Specifically, existing models fail to consider 1) varying severity of the same diseases for different patients, 2) complex interactions among syndromic diseases, and 3) dynamic progression of chronic diseases. In this work, we propose to perform personalized diagnosis prediction based on EHR data via capturing disease severity, interaction, and progression. In particular, we enable personalized disease representations via severity-driven embeddings at the disease level. Then, at the visit level, we propose to capture higher-order interactions among diseases that can collectively affect patients’ health status via hypergraph-based aggregation; at the patient level, we devise a personalized generative model based on neural ordinary differential equations to capture the continuous-time disease progressions underlying discrete and incomplete visits. Extensive experiments on two real-world EHR datasets show significant performance gains brought by our approach, yielding average improvements of 10.70% for diagnosis prediction over state-of-the-art competitors.

Drug recommendation is an important task of AI for healthcare. To recommend proper drugs, existing methods rely on various clinical records (e.g., diagnosis and procedures), which are commonly found in data such as electronic health records (EHRs). However, detailed records as such are often not available and the inputs might merely include a set of symptoms provided by doctors. Moreover, existing drug recommender systems usually treat drugs as individual items, ignoring the unique requirements that drug recommendation has to be done on a set of items (drugs), which should be as small as possible and safe without harmful drug-drug interactions (DDIs).