AIMC Topic: Cohort Studies

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Topic evolution before fall incidents in new fallers through natural language processing of general practitioners' clinical notes.

Age and ageing
BACKGROUND: Falls involve dynamic risk factors that change over time, but most studies on fall-risk factors are cross-sectional and do not capture this temporal aspect. The longitudinal clinical notes within electronic health records (EHR) provide an...

Combining Rule-based NLP-lite with Rapid Iterative Chart Adjudication for Creation of a Large, Accurately Curated Cohort from EHR data: A Case Study in the Context of a Clinical Trial Emulation.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The aim of this work was to create a gold-standard curated cohort of 10,000+ cases from the Veteran Affairs (VA) corporate data warehouse (CDW) for virtual emulation of a randomized clinical trial (CSP#592). The trial had six inclusion/exclusion crit...

Does Cohort Selection Affect Machine Learning from Clinical Data?

AMIA ... Annual Symposium proceedings. AMIA Symposium
This study investigates cohort selection and its effects on the quality of machine learning (ML) models trained on clinical data, focusing on measurements taken within the first 48 hours of hospital admission. It discusses the potential repercussions...

Prognostic Power? Do the Plasma Biomarkers, Neurofilament Light and Phospho-Tau 181, Improve Prediction of Progression to Alzheimer's Disease Using a Machine Learning Approach in the ADNI Cohort?

Journal of Alzheimer's disease : JAD
With the advent of therapeutics with potential to slow Alzheimer's disease progression the necessity of understanding the diagnostic value of plasma biomarkers is critical, not only for understanding the etiology and progression of Alzheimer's diseas...

Long-term mortality burden trends attributed to black carbon and PM from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study.

The Lancet. Planetary health
BACKGROUND: Long-term improvements in air quality and public health in the continental USA were disrupted over the past decade by increased fire emissions that potentially offset the decrease in anthropogenic emissions. This study aims to estimate tr...

Machine Learning-Based Prediction of Elevated PTH Levels Among the US General Population.

The Journal of clinical endocrinology and metabolism
CONTEXT: Although elevated parathyroid hormone (PTH) levels are associated with higher mortality risks, the evidence is limited as to when PTH is expected to be elevated and thus should be measured among the general population.

Deep learning radiomics under multimodality explore association between muscle/fat and metastasis and survival in breast cancer patients.

Briefings in bioinformatics
Sarcopenia is correlated with poor clinical outcomes in breast cancer (BC) patients. However, there is no precise quantitative study on the correlation between body composition changes and BC metastasis and survival. The present study proposed a deep...

Artificial Intelligence-Based Identification of Normal Chest Radiographs: A Simulation Study in a Multicenter Health Screening Cohort.

Korean journal of radiology
OBJECTIVE: This study aimed to investigate the feasibility of using artificial intelligence (AI) to identify normal chest radiography (CXR) from the worklist of radiologists in a health-screening environment.

Development and Validation of a Deep Learning Model for Predicting Treatment Response in Patients With Newly Diagnosed Epilepsy.

JAMA neurology
IMPORTANCE: Selection of antiseizure medications (ASMs) for epilepsy remains largely a trial-and-error approach. Under this approach, many patients have to endure sequential trials of ineffective treatments until the "right drugs" are prescribed.