AI Medical Compendium Topic:
Cohort Studies

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Preoperative Prediction of Value Metrics and a Patient-Specific Payment Model for Primary Total Hip Arthroplasty: Development and Validation of a Deep Learning Model.

The Journal of arthroplasty
BACKGROUND: The primary objective was to develop and test an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition for total hip arthroplasty. The secondary objective was to create...

Prediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models.

NeuroImage. Clinical
Magnetic resonance imaging (MRI) volumetric measures have become a standard tool for the detection of incipient Alzheimer's Disease (AD) dementia in mild cognitive impairment (MCI). Focused on providing an earlier and more accurate diagnosis, sophist...

Directional Relationship Between Vitamin D Status and Prediabetes: A New Approach from Artificial Neural Network in a Cohort of Workers with Overweight-Obesity.

Journal of the American College of Nutrition
Despite the increasing literature on the association of diabetes with inflammation, cardiovascular risk, and vitamin D (25(OH)D) concentrations, strong evidence on the direction of causality among these factors is still lacking. This gap could be ad...

Using Artificial Intelligence to Identify Factors Associated with Autism Spectrum Disorder in Adolescents with Cerebral Palsy.

Neuropediatrics
Autism spectrum disorder (ASD) is common in adolescents with cerebral palsy (CP) and there is a lack of studies applying artificial intelligence to investigate this field and this population in particular. The aim of this study is to develop and test...

Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study.

Journal of medical Internet research
BACKGROUND: Virtually, all organisms on Earth have their own circadian rhythm, and humans are no exception. Circadian rhythms are associated with various human states, especially mood disorders, and disturbance of the circadian rhythm is known to be ...

Generating automated kidney transplant biopsy reports combining molecular measurements with ensembles of machine learning classifiers.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
We previously reported a system for assessing rejection in kidney transplant biopsies using microarray-based gene expression data, the Molecular Microscope Diagnostic System (MMDx). The present study was designed to optimize the accuracy and stabilit...

Natural language processing of radiology reports for identification of skeletal site-specific fractures.

BMC medical informatics and decision making
BACKGROUND: Osteoporosis has become an important public health issue. Most of the population, particularly elderly people, are at some degree of risk of osteoporosis-related fractures. Accurate identification and surveillance of patient populations w...

Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis.

Scientific reports
Tumour budding has been described as an independent prognostic feature in several tumour types. We report for the first time the relationship between tumour budding and survival evaluated in patients with muscle invasive bladder cancer. A machine lea...

Non-invasive assessment of NAFLD as systemic disease-A machine learning perspective.

PloS one
BACKGROUND & AIMS: Current non-invasive scores for the assessment of severity of non-alcoholic fatty liver disease (NAFLD) and identification of patients with non-alcoholic steatohepatitis (NASH) have insufficient performance to be included in clinic...