AI Medical Compendium Topic:
Cohort Studies

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Automated Gleason grading of prostate cancer tissue microarrays via deep learning.

Scientific reports
The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibili...

Clinical prediction of HBV and HCV related hepatic fibrosis using machine learning.

EBioMedicine
Clinical prediction of advanced hepatic fibrosis (HF) and cirrhosis has long been challenging due to the gold standard, liver biopsy, being an invasive approach with certain limitations. Less invasive blood test tandem with a cutting-edge machine lea...

Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images.

NeuroImage
The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is importan...

A user-guided tool for semi-automated cerebral microbleed detection and volume segmentation: Evaluating vascular injury and data labelling for machine learning.

NeuroImage. Clinical
BACKGROUND AND PURPOSE: With extensive research efforts in place to address the clinical relevance of cerebral microbleeds (CMBs), there remains a need for fast and accurate methods to detect and quantify CMB burden. Although some computer-aided dete...

Machine-learned selection of psychological questionnaire items relevant to the development of persistent pain after breast cancer surgery.

British journal of anaesthesia
BACKGROUND: Prevention of persistent pain after breast cancer surgery, via early identification of patients at high risk, is a clinical need. Psychological factors are among the most consistently proposed predictive parameters for the development of ...

Is there a symptomatic distinction between the affective psychoses and schizophrenia? A machine learning approach.

Schizophrenia research
Dubiety exists over whether clinical symptoms of schizophrenia can be distinguished from affective psychosis, the assumption being that absence of a "point of rarity" indicates lack of nosological distinction, based on prior group-level analyses. Adv...

Machine Learning for Better Prognostic Stratification and Driver Gene Identification Using Somatic Copy Number Variations in Anaplastic Oligodendroglioma.

The oncologist
BACKGROUND: 1p/19q-codeleted anaplastic gliomas have variable clinical behavior. We have recently shown that the common 9p21.3 allelic loss is an independent prognostic factor in this tumor type. The aim of this study is to identify less frequent gen...

Development and Application of a Machine Learning Approach to Assess Short-term Mortality Risk Among Patients With Cancer Starting Chemotherapy.

JAMA network open
IMPORTANCE: Patients with cancer who die soon after starting chemotherapy incur costs of treatment without the benefits. Accurately predicting mortality risk before administering chemotherapy is important, but few patient data-driven tools exist.

Presentation and diagnosis of patients with type 3 von Willebrand disease in resources-limited laboratory.

Hematology/oncology and stem cell therapy
Von Willebrand disease (VWD) is a bleeding disorder that results from decreased von Willebrand factor (VWF) activity <0.30 iu/mL. Therefore, the diagnosis of type 3 VWD in patients with bleeding requires finding a VWF:Ag and/or VWF:platelet ristoceti...