AIMC Topic: Cohort Studies
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Prediction of postoperative gait speed change after bilateral primary total knee arthroplasty in female patients using a machine learning algorithm.
Orthopaedics & traumatology, surgery & research : OTSR
Feb 19, 2024
BACKGROUND: An important aim of total knee arthroplasty is to achieve functional recovery, which includes post-operative increase in walking speed. Therefore, predicting whether a patient will walk faster or slower after surgery is important in TKA, ...
Ranking and filtering of neuropathology features in the machine learning evaluation of dementia studies.
Brain pathology (Zurich, Switzerland)
Feb 19, 2024
Early diagnosis of dementia diseases, such as Alzheimer's disease, is difficult because of the time and resources needed to perform neuropsychological and pathological assessments. Given the increasing use of machine learning methods to evaluate neur...
A machine-learning exploration of the exposome from preconception in early childhood atopic eczema, rhinitis and wheeze development.
Environmental research
Feb 19, 2024
BACKGROUND: Most previous research on the environmental epidemiology of childhood atopic eczema, rhinitis and wheeze is limited in the scope of risk factors studied. Our study adopted a machine learning approach to explore the role of the exposome st...
Machine learning decision support model for discharge planning in stroke patients.
Journal of clinical nursing
Feb 15, 2024
BACKGROUND/AIM: Efficient discharge for stroke patients is crucial but challenging. The study aimed to develop early predictive models to explore which patient characteristics and variables significantly influence the discharge planning of patients, ...
Incidence and risk factors of inguinal hernia after robot-assisted radical prostatectomy: a retrospective multicenter cohort study in Japan (the MSUG94 group).
Journal of robotic surgery
Jan 17, 2024
To investigate the incidence and risk factors of inguinal hernia (IH) after robot-assisted radical prostatectomy (RARP) using a multicentric database. The present study used a multicentric database (the MSUG94) containing data on 3,195 Japanese patie...
Prevalence of chronic kidney disease and metabolic related indicators in Mianzhu, Sichuan, China.
Frontiers in public health
Jan 17, 2024
BACKGROUND: Chronic kidney disease (CKD) is a major public health problem worldwide. Periodic surveys are essential for monitoring the prevalence of CKD and its risk factors. We assessed the prevalence of CKD and its risk factors in Mianzhu City in 2...
Prediction of prognosis and treatment response in ovarian cancer patients from histopathology images using graph deep learning: a multicenter retrospective study.
European journal of cancer (Oxford, England : 1990)
Jan 13, 2024
BACKGROUND: Ovarian cancer (OV) is a prevalent and deadly disease with high mortality rates. The development of accurate prognostic tools and personalized therapeutic strategies is crucial for improving patient outcomes.
A novel artificial intelligence-assisted "vascular healing" diagnosis for prediction of future clinical relapse in patients with ulcerative colitis: a prospective cohort study (with video).
Gastrointestinal endoscopy
Jan 10, 2024
BACKGROUND AND AIMS: Image-enhanced endoscopy has attracted attention as a method for detecting inflammation and predicting outcomes in patients with ulcerative colitis (UC); however, the procedure requires specialist endoscopists. Artificial intelli...
Prognostic Importance of Lymphovascular Invasion for Specific Subgroup of Patients with Prostate Cancer After Robot-Assisted Radical Prostatectomy (The MSUG94 Group).
Annals of surgical oncology
Jan 10, 2024
OBJECTIVE: This study aimed to investigate whether lymphovascular invasion (LVI) was associated with oncological outcomes in patients with prostate cancer (PCa) undergoing robotic-assisted radical prostatectomy (RARP).
Predicting low cognitive ability at age 5 years using perinatal data and machine learning.
Pediatric research
Jan 4, 2024
BACKGROUND: There are no early, accurate, scalable methods for identifying infants at high risk of poor cognitive outcomes in childhood. We aim to develop an explainable predictive model, using machine learning and population-based cohort data, for t...
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