BACKGROUND & AIMS: Addressing many clinical questions, such as estimating survival differences between living donor (LDLT) and deceased donor liver transplantation (DDLT), relies on observational studies, as randomized-controlled trials (RCTs) are of...
Journal of the Academy of Nutrition and Dietetics
Nov 1, 2025
BACKGROUND: Methods for modeling the relationship between self-reported 24-hour dietary recalls and health outcomes are traditionally based on nutrients and/or dietary patterns. Machine learning (ML), combined with hierarchical representations of die...
BACKGROUND AND OBJECTIVES: Aneurysm risk prediction remains an imprecise science that places patients at risk for either over or undertreatment. Machine learning (ML) models may improve clinical practice by adding precision to risk assessment. This s...
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for major depression disorder (MDD), particularly for treatment-resistant cases. However, identifying translatable biomarkers predictive of treatment outcomes re...
OBJECTIVES: This work aimed to develop an automated method for quantifying the distribution and severity of perfusion changes on CT pulmonary angiography (CTPA) in patients with chronic thromboembolic pulmonary hypertension (CTEPH) and to assess thei...
With rapid upsurge in technology and digital tools, the existing systems including the healthcare systems, especially the pharmaceutical sector is experiencing the revolution in the flow and management of data. Use of digital tools in pharmaceutical ...
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Nov 1, 2025
OBJECTIVE: To develop and validate advanced machine learning (ML) models for predicting unplanned intrapartum cesarean deliveries in women with no previous cesarean delivery, using both static and dynamic clinical data.
International journal of radiation oncology, biology, physics
Nov 1, 2025
PURPOSE: The study aimed to develop machine learning (ML) models for pretherapy prediction of absorbed doses (ADs) in kidneys and tumoral lesions for patients with metastatic castration-resistant prostate cancer (mCRPC) undergoing [Lu]Lu-PSMA-617 (Lu...
BACKGROUND: Recent evidence has shown that machine learning (ML) techniques can accurately forecast adverse cardiovascular and limb events in patients with intermittent claudication. This is the first study to compare the predictive performance of ML...
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