IEEE journal of biomedical and health informatics
Jun 1, 2025
Computational phenotyping uses data mining methods to extract clusters of clinical descriptors, known as phenotypes, from electronic health records (EHR). Tensor factorization methods are very effective in extracting meaningful patterns and have beco...
IEEE journal of biomedical and health informatics
Jun 1, 2025
Multilabel pathological tissue segmentation is a vital task in computational pathology that aims to semantically segment different tissues within pathological images. Fully and weakly supervised models have demonstrated impressive performances in thi...
IEEE journal of biomedical and health informatics
Jun 1, 2025
The medical literature and records on diabetes provide crucial resources for diabetes prevention and treatment. However, extracting entities from these textual diabetes data is crucial but challenging. Named entity recognition (NER) - an important co...
IEEE journal of biomedical and health informatics
Jun 1, 2025
Accurately diagnosing chronic kidney disease requires pathologists to assess the structure of multiple tissues under different stains, a process that is time-consuming and labor-intensive. Current AI-based methods for automatic structure assessment, ...
Recurrence prediction in well-differentiated thyroid cancer remains a clinical challenge, necessitating more accurate and interpretable predictive models. This study investigates the use of a supervised CatBoost classifier to predict recurrence in we...
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
May 29, 2025
Abiotic stresses such as drought, heat, cold, salinity and flooding significantly impact plant growth, development and productivity. As the planet has warmed, these abiotic stresses have increased in frequency and intensity, affecting the global food...
BACKGROUND: Sepsis-associated liver injury (SALI) is a severe complication of sepsis that contributes to increased mortality and morbidity. Early identification of SALI can improve patient outcomes; however, sepsis heterogeneity makes timely diagnosi...
In principle, ML/AI-based algorithms should enable rapid and accurate cell segmentation in high-throughput settings. However, reliance on large training datasets, human input, computational expertise, and limited generalizability has prevented this g...
BACKGROUND: Basal cell carcinoma (BCC) is the most common skin cancer, placing a significant burden on healthcare systems globally. Developing high-precision automated diagnostics requires large annotated datasets, which are costly and difficult to o...
The application of deep learning using dental models is crucial for automated computer-aided treatment planning. However, developing highly accurate models requires a substantial amount of accurately labeled data. Obtaining this data is challenging, ...
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