arXiv:2606. 08311v1 Announce Type: new Abstract: Electronic health record (EHR) notes are dense medical documents containing large amounts of information, often filled with complex medical jargon.
arXiv:2609.15713v1 Announce Type: new Abstract: Recent approaches to 30-day hospital readmission prediction rely on pre-trained language models applied to discharge summaries. Although these methods...
arXiv:2609.22239v1 Announce Type: new Abstract: Ambient AI is increasingly adopted in healthcare to automatically generate clinical notes from patient-clinician conversations, with the potential to s...
arXiv:2606. 19183v1 Announce Type: cross Abstract: Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagnostic engines is limited by sensitivity to prompts, information order, and plausible but incorrect outputs.
arXiv:2605.20292v2 Announce Type: replace Abstract: Numerical time-series models effectively process irregular electronic health record (EHR) trajectories, but do not expose which temporal patterns s...
arXiv:2607. 00472v1 Announce Type: cross Abstract: Cardiovascular disease is still one of the main causes of death around the world.
arXiv:2606. 00031v1 Announce Type: cross Abstract: Coronary artery disease (CAD) remains one of the leading causes of death globally, highlighting the need for reliable predictive systems to support early diagnosis and risk assessment.
This thesis explores how to select and adapt NLP models for global health literature when annotated data and computational resources are scarce. It compares skip‑gram word2vec models trained on increasingly large specialized corpora with BioWordVec for semantic tag discovery, finding that larger coverage does not always yield more useful domain associations. The study also evaluates convolutional spaCy models versus a RoBERTa transformer for named entity recognition, noting a trade‑off between higher F1 scores and longer inference time, and investigates MiniLM few‑shot versus BART‑MNLI zero‑shot classification for multi‑label topic classification, highlighting practical constraints of inference cost. "whyItMatters":"The work provides empirical guidance on balancing model accuracy and resource demands for building knowledge systems in low‑resource global health settings."
arXiv:2605. 30295v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise for clinical reasoning and decision support, but evaluation in realistic, electronic health record-congruent settings remains limited.
arXiv:2605. 01189v2 Announce Type: replace Abstract: Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transparency required for professional-level explainability.
arXiv:2606. 12569v2 Announce Type: replace-cross Abstract: We present eCream-MedCorpus, a new and unique large-scale dataset of clinical notes produced in Emergency Departments of Italian hospitals.
arXiv:2606. 12569v3 Announce Type: replace-cross Abstract: We present eCREAM-MedCorpus, a new and unique large-scale dataset of clinical notes produced in Emergency Departments of Italian hospitals.
arXiv:2606. 16337v1 Announce Type: new Abstract: Predictive modeling for clinical tabular data is central to clinical decision support and therefore requires not only strong predictive performance but also transparent decision logic.