A high-quality corpus of Dutch ICU medical notes was created to help computers detect medication side effects, especially those impacting the kidneys. Two medical experts analyzed anonymized notes from 102 adult ICU patients suspected of having drug-related kidney issues. They labeled medications, medical problems, and their relationships using standardized, double-checked guidelines. Over 16,000 labels were added in total, including 614 for drug-related side effects. The resulting dataset will be used to train computer systems to identify side effects in Dutch electronic medical records automatically.

Rachel O'Sullivan-Murphy, et el DOI: 10.1007/s10579-025-09832-5

This study identified a chronic kidney disease (CKD) cohort from Amsterdam UMC using electronic health records based on six logical rules.

Pooling these six logical rules resulted in identifying 17 805 hospitalized CKD patients (16.4%). The proportions identified by each logical rule varied from 2.1% to 8.4%. Our findings highlight the need to formalize the CKD clinical definition into a computer-interpretable one, to avoid inconsistencies in EHR implementation across studies.

 Daniel Fernandez Llaneza, et el DOI: 10.1093/ckj/sfaf073

This study created two AI models to help detect lung cancer early by analyzing doctors’ notes and other health information.

One model was based on these notes alone and the other combined the notes with other health data. Both models worked well in spotting patients at high risk for lung cancer. The model that included both the notes and other data was slightly more accurate.

Iacopo Vagliano, et el DOI: 10.3390/cancers17071151

This study identified barriers to implementing the FAIR (Findable, Accessible, Interoperable, Reusable) principles in child and adolescent mental health research.

A total of 45 barriers were identified, including those related to organizational policies and software. Key recommendations include adding a FAIR data steward to the research team, providing accessible guides, and ensuring sustainable funding to overcome these barriers, ultimately enhancing data reusability.

 Rowdy de Groot, et el DOI: 10.2196/59113

Machine learning models often struggle to generalize to data distributions that differ from their training dataset, known as "out-of-distribution" (OOD) data. It is important to detect OOD because prediction on such data can be unreliable.

While OOD detection methods have been explored in various fields, their effectiveness for medical data remains unclear. This study aims to evaluate and compare different OOD detection methods in the context of medical tabular data.

 The visual summary and interview with Giovanni Cina explains OOD.

 Mohammad Azizmalayeri, et al DOI:10.1016/j,ijmedinf.2024.105762