The paper introduces a unified pre‑training framework for medical representations that incorporates hierarchical sub‑token aggregation, partial masking, and cross‑reference mechanisms to better capture the structure of medical codes. The resulting model outperforms existing BERT‑based approaches on pre‑training tasks and downstream clinical predictions, such as dementia onset and hospitalization. An in‑silico drug repositioning study for Alzheimer’s disease demonstrates the framework’s ability to rediscover known drugs and prioritize new hypotheses without external literature, establishing a workflow for hypothesis generation and prioritization based on observational data.
arXiv:2606. 15447v1 Announce Type: new Abstract: Electronic health record foundation models typically treat ICD diagnosis codes as flat tokens, overlooking the clinically meaningful hierarchical structure that captures disease families, subcategories, and fine-grained diagnostic detail.
arXiv:2608. 20315v1 Announce Type: new Abstract: Predictive models over structured electronic health records (EHRs) remain central to machine learning for healthcare, but few have jointly emphasized quantitative laboratory information and interpretability with respect to input medical events.
Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model performance on clinical prediction tasks by teaching models not just what to predict but why. We test this assumption on five-year Alzheimer's disease and related dementias (ADRD) prediction from longitudinal health histories.
arXiv:2606. 10279v1 Announce Type: new Abstract: Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model performance on clinical prediction tasks by teaching models not just what to predict but why.
arXiv:2607. 22770v1 Announce Type: new Abstract: Although artificial intelligence (AI) has shown promising performance in several medical tasks, accurate dementia etiology diagnosis with AI remains challenging due to complex overlapping symptoms among diseases.
arXiv:2607. 15447v1 Announce Type: new Abstract: Recent research in clinical machine learning, focusing on outcome predictions in intensive care unit (ICU), has shifted from bespoke supervised models to foundation models, utilising modern representation learning methods.
arXiv:2602. 00541v2 Announce Type: replace Abstract: Clinical events captured in Electronic Health Records (EHR) are irregularly sampled and may consist of a mixture of discrete events and numerical measurements, such as laboratory values or treatment dosages.
arXiv:2602. 11177v2 Announce Type: replace-cross Abstract: Reliable early detection of Alzheimer's disease (AD) is challenging, particularly due to the limited availability of labeled data.
arXiv:2603. 13673v2 Announce Type: replace Abstract: Accurate extraction of Alzheimer's Disease and Related Dementias (ADRD) phenotypes from electronic health records (EHR) is critical for early-stage detection and disease staging.
arXiv:2608.20887v1 Announce Type: cross Abstract: Automatic Medical Coding (AMC), which assigns standardized International Classification of Diseases (ICD) codes to clinical notes, is essential for m...
arXiv:2608. 16507v1 Announce Type: new Abstract: Due to the limited amount of information, modeling longitudinal rare-disease data can benefit from integrating clinical knowledge.
The study re‑implements 12 AI algorithms for electronic health records within a unified framework and evaluates them on MIMIC‑IV and NWICU datasets. It compares expert‑authored clinically meaningful tasks with randomly generated tasks, finding that pairwise algorithm comparisons transfer well across task families and datasets, yet clinically meaningful tasks show stronger task‑method interactions. The results also reveal that newer algorithms do not consistently outperform older ones, with gradient‑boosted trees remaining highly competitive when combined with modern EHR representations.