AIMC Topic: Confounding Factors, Epidemiologic

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Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI).

Artificial intelligence in medicine Jan 13, 2020
Over the years, there has been growing interest in using machine learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biomedical data depends on a variety of characteristics, such as demographi...
Confounding Factors, Epidemiologic Humans Machine Learning Biomedical Research
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How Confounder Strength Can Affect Allocation of Resources in Electronic Health Records.

Perspectives in health information management Jan 1, 2018
When electronic health record (EHR) data are used, multiple approaches may be available for measuring the same variable, introducing potentially confounding factors. While additional information may be gleaned and residual confounding reduced through...
Humans Algorithms Electronic Health Records Natural Language Processing Patient Readmission Data Accuracy Smoking Confounding Factors, Epidemiologic
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Natural language processing to ascertain two key variables from operative reports in ophthalmology.

Pharmacoepidemiology and drug safety Jan 3, 2017
PURPOSE: Antibiotic prophylaxis is critical to ophthalmology and other surgical specialties. We performed natural language processing (NLP) of 743 838 operative notes recorded for 315 246 surgeries to ascertain two variables needed to study the compa...
Confounding Factors, Epidemiologic Comparative Effectiveness Research Posterior Capsular Rupture, Ocular Humans Surgical Wound Infection Natural Language Processing Injections, Intraocular Anti-Bacterial Agents Cataract Extraction Antibiotic Prophylaxis Predictive Value of Tests Feasibility Studies Intraoperative Complications
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Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies.

American journal of epidemiology Dec 9, 2016
Estimation of causal effects using observational data continues to grow in popularity in the epidemiologic literature. While many applications of causal effect estimation use propensity score methods or G-computation, targeted maximum likelihood esti...
Depression Causality Observational Studies as Topic Data Interpretation, Statistical Machine Learning Computer Simulation Exercise Epidemiologic Research Design Bias Humans Propensity Score Likelihood Functions Confounding Factors, Epidemiologic
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Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets.

Statistics in medicine Oct 15, 2014
Inverse probability weights used to fit marginal structural models are typically estimated using logistic regression. However, a data-adaptive procedure may be able to better exploit information available in measured covariates. By combining predicti...
Bias Humans HIV Infections Antiretroviral Therapy, Highly Active Spain Machine Learning Logistic Models Probability Mortality Computer Simulation Models, Statistical Confidence Intervals Confounding Factors, Epidemiologic Data Interpretation, Statistical
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Use of Machine Learning to Compare Disease Risk Scores and Propensity Scores Across Complex Confounding Scenarios: A Simulation Study.

Pharmacoepidemiology and drug safety Jun 1, 2025
PURPOSE: The surge of treatments for COVID-19 in the second quarter of 2020 had a low prevalence of treatment and high outcome risk. Motivated by that, we conducted a simulation study comparing disease risk scores (DRS) and propensity scores (PS) usi...
Propensity Score Humans Monte Carlo Method Machine Learning Computer Simulation Confounding Factors, Epidemiologic Logistic Models COVID-19 SARS-CoV-2 Bias Risk Assessment COVID-19 Drug Treatment Prevalence
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How Effective Are Machine Learning and Doubly Robust Estimators in Incorporating High-Dimensional Proxies to Reduce Residual Confounding?

Pharmacoepidemiology and drug safety May 1, 2025
BACKGROUND: Residual confounding presents a persistent challenge in observational studies, particularly in high-dimensional settings. High-dimensional proxy adjustment methods, such as the high-dimensional propensity score (hdPS), are widely used to ...
Humans Bias Propensity Score Likelihood Functions Confounding Factors, Epidemiologic Machine Learning Computer Simulation Logistic Models Observational Studies as Topic
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Rise of Robot Radiologists.

Nature Dec 1, 2019
United States Mammography Triage Robotics Cardiovascular Diseases Diagnosis, Computer-Assisted Deep Learning Retina Tuberculosis United States Food and Drug Administration Datasets as Topic Radiology Bias Radiologists Healthcare Disparities Cell Phone Humans Diabetic Retinopathy Tomography, X-Ray Computed Practice Guidelines as Topic Uncertainty Confounding Factors, Epidemiologic Female Sensitivity and Specificity Diagnostic Errors Breast Neoplasms Clinical Decision-Making
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Signals Among Signals: Prioritizing Nongenetic Associations in Massive Data Sets.

American journal of epidemiology May 1, 2019
Massive data sets are often regarded as a panacea to the underpowered studies of the past. At the same time, it is becoming clear that in many of these data sets in which thousands of variables are measured across hundreds of thousands or millions of...
Data Interpretation, Statistical Confounding Factors, Epidemiologic Biostatistics Epidemiologic Research Design Machine Learning Big Data Models, Statistical Humans
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Causal models adjusting for time-varying confounding-a systematic review of the literature.

International journal of epidemiology Feb 1, 2019
BACKGROUND: Obtaining unbiased causal estimates from longitudinal observational data can be difficult due to exposure-affected time-varying confounding. The past decade has seen considerable development in methods for analysing such complex longitudi...
Health Status Humans Bias Causality Machine Learning Probability Models, Statistical Computer Simulation Confounding Factors, Epidemiologic Data Interpretation, Statistical Mental Health
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