AIMC Topic: Cohort Studies

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A machine-learning exploration of the exposome from preconception in early childhood atopic eczema, rhinitis and wheeze development.

Environmental research
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
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
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
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)
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
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
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
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...

Symptomatic Lymphocele After Robot-Assisted Pelvic Lymphadenectomy as Part of the Primary Surgical Treatment for Cervical and Endometrial Cancer: A Retrospective Cohort Study.

Journal of minimally invasive gynecology
STUDY OBJECTIVES: Pelvic lymph node dissection (PLND) is part of the primary treatment for early-stage cervical cancer and high-intermediate risk or high-risk endometrial cancer. Pelvic lymphocele is a postoperative complication of PLND, and when sym...

Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT.

European radiology
OBJECTIVES: To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity.