AIMC Topic: Electronic Health Records
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Evaluating generalizability of oncology trial results to real-world patients using machine learning-based trial emulations.
Nature medicine
Jan 3, 2025
Randomized controlled trials (RCTs) evaluating anti-cancer agents often lack generalizability to real-world oncology patients. Although restrictive eligibility criteria contribute to this issue, the role of selection bias related to prognostic risk r...
Using Machine Learning to Predict Weight Gain in Adults: an Observational Analysis From the All of Us Research Program.
The Journal of surgical research
Dec 31, 2024
INTRODUCTION: Obesity, defined as a body mass index ≥30 kg/m, is a major public health concern in the United States. Preventative approaches are essential, but they are limited by an inability to accurately predict individuals at highest risk of weig...
Machine learning-based forecast of Helmet-CPAP therapy failure in Acute Respiratory Distress Syndrome patients.
Computer methods and programs in biomedicine
Dec 30, 2024
BACKGROUND AND OBJECTIVE: Helmet-Continuous Positive Airway Pressure (H-CPAP) is a non-invasive respiratory support that is used for the treatment of Acute Respiratory Distress Syndrome (ARDS), a severe medical condition diagnosed when symptoms like ...
Applying AI to Structured Real-World Data for Pharmacovigilance Purposes: Scoping Review.
Journal of medical Internet research
Dec 30, 2024
BACKGROUND: Artificial intelligence (AI) applied to real-world data (RWD; eg, electronic health care records) has been identified as a potentially promising technical paradigm for the pharmacovigilance field. There are several instances of AI approac...
A topic modeling approach for analyzing and categorizing electronic healthcare documents in Afaan Oromo without label information.
Scientific reports
Dec 30, 2024
Afaan Oromo is a resource-scarce language with limited tools developed for its processing, posing significant challenges for natural language tasks. The tools designed for English do not work efficiently for Afaan Oromo due to the linguistic differen...
A machine learning-based clinical predictive tool to identify patients at high risk of medication errors.
Scientific reports
Dec 30, 2024
A medication error is an inadvertent failure in the drug therapy process that can cause serious harm to patients by increasing morbidity and mortality and are associated with significant economic costs to the healthcare system. Medication reconciliat...
Ascertaining provider-level implicit bias in electronic health records with rules-based natural language processing: A pilot study in the case of prostate cancer.
PloS one
Dec 30, 2024
PURPOSE: Implicit, unconscious biases in medicine are personal attitudes about race, ethnicity, gender, and other characteristics that may lead to discriminatory patterns of care. However, there is no consensus on whether implicit bias represents a t...
A Systematic Approach to Prioritise Diagnostically Useful Findings for Inclusion in Electronic Health Records as Discrete Data to Improve Clinical Artificial Intelligence Tools and Genomic Research.
Clinical oncology (Royal College of Radiologists (Great Britain))
Dec 28, 2024
AIMS: The recent widespread use of electronic health records (EHRs) has opened the possibility for innumerable artificial intelligence (AI) tools to aid in genomics, phenomics, and other research, as well as disease prevention, diagnosis, and therapy...
Predicting the likelihood of readmission in patients with ischemic stroke: An explainable machine learning approach using common data model data.
International journal of medical informatics
Dec 26, 2024
BACKGROUND: Ischemic stroke affects 15 million people worldwide, causing five million deaths annually. Despite declining mortality rates, stroke incidence and readmission risks remain high, highlighting the need for preventing readmission to improve ...
Predicting preterm birth using electronic medical records from multiple prenatal visits.
BMC pregnancy and childbirth
Dec 21, 2024
This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic net regularized logistic regression models were developed and evaluated using 5-fold cross-validation ...
Humans
Cervical Length Measurement
Predictive Value of Tests
Area Under Curve
Gestational Age
Young Adult
Adult
Sensitivity and Specificity
Risk Assessment
Logistic Models
Female
Infant, Newborn
Ultrasonography, Prenatal
Premature Birth
Prenatal Care
Machine Learning
Electronic Health Records
Pregnancy
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