Predictive Model

Predictive modeling aims to build computational models that accurately forecast future outcomes based on available data. Current research emphasizes enhancing model accuracy and interpretability, particularly in data-scarce domains, by incorporating latent features (e.g., using large language models), weighting samples based on sub-cohort characteristics, and integrating domain expertise (e.g., physics-informed learning or expert knowledge encoded via LLMs). These advancements are significantly impacting diverse fields, from healthcare (e.g., disease prediction using EHRs and imaging) and environmental science (e.g., weather and solar activity forecasting) to manufacturing (e.g., fault detection) and social sciences (e.g., disinformation network mapping). The focus is on developing robust, reliable models that provide not only accurate predictions but also quantifiable uncertainty estimates.