arXiv:2607. 18431v1 Announce Type: cross Abstract: Gold-standard phenotype labels are often unavailable at scale in electronic health record (EHR) studies because they require manual chart review.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2511. 02340v3 Announce Type: replace Abstract: Chronic Kidney Disease (CKD) affects nearly 10\% of the global population and often progresses to end-stage renal failure.
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. 00935v1 Announce Type: new Abstract: Electronic Health Records (EHRs) are widely used for clinical risk prediction using machine learning.
arXiv:2607. 15394v1 Announce Type: new Abstract: Black-box models limit the adoption of artificial intelligence in medicine due to their lack of interpretability and reproducibility.
arXiv:2410. 15051v3 Announce Type: replace-cross Abstract: Identifying patient diagnoses from hospital discharge letters is essential for large-scale cohort selection and epidemiological research, but traditional supervised approaches require extensive manual annotation, which is often impractical for large textual datasets.
arXiv:2505. 16941v4 Announce Type: replace-cross Abstract: Foundation models (FMs) promise to address core limitations of traditional supervised machine learning: (i) reliance on large amounts of labeled data, (ii) task specificity, and (iii) poor transportability.