Our website uses essential cookies and, with your consent, additional cookies to measure performance and improve our services. Cookie Policy.
You can change your choice at any time.
Tag
The HLH-Risk-Calculator utilizes machine learning to predict initial disease severity and mortality at multiple time points in adult patients with secondary hemophagocytic lymphohistiocytosis, highlighting key biomarkers and advocating for
Machine learning analysis of brief patient-physician conversations using acoustic speech features can effectively screen for cognitive impairment in primary care settings, enabling scalable early detection.
A machine learning algorithm can generate synthetic CT images from temporal bone MRI, enabling visualization of bone and soft tissue simultaneously without radiation, useful for otologic surgical planning and localization.
Unsupervised machine learning identified three distinct preeclampsia phenotypes linked to timing of delivery, placental dysfunction, fetal growth, and complications, suggesting a path toward more personalized risk assessment and management.
Machine learning identified two distinct bilateral hepatocellular carcinoma phenotypes with different tumor burdens, surgical approaches, and outcomes, suggesting preoperative risk stratification may improve patient selection and treatment
A novel machine-learning model incorporating clinical, ultrasonographic, and molecular features predicts occult central lymph node metastasis in papillary thyroid cancer, with RET fusion positivity identified as a key risk factor.
A five-RNA extracellular vesicle signature showed strong diagnostic performance for chronic pancreatitis and may also reflect acinar-to-ductal metaplasia and disease-related metabolic features.
Secondary analysis of the MYRIAD trial reveals that while machine learning models can identify adolescents who may benefit from school-based mindfulness training, the clinical relevance of such predictions remains minimal. Both causal fores
A landmark multicohort study of 1,168 individuals reveals that gut microbiome diversity systematically declines across liver disease stages, with specific bacterial markers like Veillonella and functional metabolic shifts predicting disease
Researchers developed and validated ADAPT-CEC, an artificial intelligence algorithm that can adjudicate cardiovascular events across different trial definitions. A hybrid approach combining AI with selective human review achieved 95.6% accu
This review analyzes a landmark machine learning study identifying four distinct GDM phenotypic clusters, revealing that early-diagnosed, comorbidity-related GDM carries a four-fold higher risk of postpartum diabetes.
A groundbreaking machine learning study of over 37,000 patients identifies four distinct GDM phenotypic clusters. These findings demonstrate that early-diagnosed, high-comorbidity phenotypes carry a four-fold increased risk of postpartum di
A large-scale analysis of the MYRIAD trial indicates that universal school-based mindfulness training does not superiorly reduce depression risk in adolescents. While machine learning identified certain potential responders, the clinical be
This study reveals that combining stress hyperglycemia ratio and glucose variability significantly improves mortality prediction in sepsis, with risk patterns varying significantly based on a patient’s baseline metabolic status (Norma
A secondary analysis of the EMERGE trial reveals that Random Survival Forest models utilizing early glycemic data can accurately predict the time to insulin initiation in women with gestational diabetes, offering a roadmap for personalized
Researchers have developed a machine learning model using real-world and clinical trial data to predict distant recurrence risk and ribociclib’s therapeutic effect in HR+/HER2- early breast cancer, offering a potential tool for person
A nationwide target trial emulation study reveals that SGLT2 inhibitors provide cardiovascular benefits across a broader range of patients than previously recognized, with benefit more closely tied to individual metabolic traits than tradit
A large-scale UK Biobank study reveals that while visceral and liver fat accelerate cardiovascular ageing in both sexes, subcutaneous and android fat specifically impact males, whereas gynoid fat may offer protective benefits.
The novel PILOT machine learning architecture integrates time-phased perioperative data and regeneration-associated biomarkers to predict post-hepatectomy liver failure within six hours of surgery, significantly outperforming traditional cl
A secondary analysis of the TEAM trial using machine learning demonstrates that enhanced early mobilization in mechanically ventilated patients has highly individualized effects, ranging from a 34% mortality reduction to a 39% increase in r