AIMC Topic: Registries

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Comparison of spatial prediction models from Machine Learning of cholangiocarcinoma incidence in Thailand.

BMC public health
BACKGROUND: Cholangiocarcinoma (CCA) poses a significant public health challenge in Thailand, with notably high incidence rates. This study aimed to compare the performance of spatial prediction models using Machine Learning techniques to analyze the...

Clinical Trial Design Approach to Auditing Language Models in Health Care Setting.

JCO clinical cancer informatics
PURPOSE: Rapid advancements in natural language processing have led to the development of sophisticated language models. Inspired by their success, these models are now used in health care for tasks such as clinical documentation and medical record c...

Integrating large language models with human expertise for disease detection in electronic health records.

Computers in biology and medicine
OBJECTIVE: Electronic health records (EHR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EHR is labour-intensive and requires extensive manual labelli...

Using Machine Learning for the Fusion of Tumor Records on a Real-World Dataset.

Studies in health technology and informatics
Cancer registries collect multiple reports describing the same tumor, potentially leading to duplicate or conflicting values across different records. This complicates further use of cancer data. Data fusion addresses this issue by consolidating mult...

Predictors and associations of complications in ureteroscopy for stone disease using AI: outcomes from the FLEXOR registry.

Urolithiasis
We aimed to develop machine learning(ML) algorithms to evaluate complications of flexible ureteroscopy and laser lithotripsy(fURSL), providing a valid predictive model. 15 ML algorithms were trained on a large number fURSL data from > 6500 patients f...

Identifying Mild-to-Moderate Atopic Dermatitis Using a Generic Machine Learning Approach: A Danish National Health Register Study.

Acta dermato-venereologica
Atopic dermatitis is a chronic skin disease, causing itching and recurrent eczematous lesions. In Danish national register data, adults with atopic dermatitis can only be identified if they have a hospital-diagnosed atopic dermatitis. The purpose of ...

Improving prediction accuracy of hospital arrival vital signs using a multi-output machine learning model: a retrospective study of JSAS-registry data.

BMC emergency medicine
BACKGROUND: Critically ill patients can deteriorate rapidly; therefore, prompt prehospital interventions and seamless transition to in-hospital care upon arrival are crucial for improving survival. In Japan, helicopter emergency medical services (HEM...

Machine learning-based prediction of 90-day prognosis and in-hospital mortality in hemorrhagic stroke patients.

Scientific reports
This study aims to predict hemorrhagic stroke outcomes, including 90-day prognosis and in-hospital mortality, using machine learning models and SHapley Additive exPlanations (SHAP) analysis. Data were collected from a national Stroke Registry from Ja...

Prediction of hypertension and diabetes in twin pregnancy using machine learning model based on characteristics at first prenatal visit: national registry study.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
OBJECTIVE: To develop a prediction model for hypertensive disorders of pregnancy (HDP) and gestational diabetes mellitus (GDM) in twin pregnancy using characteristics obtained at the first prenatal visit.

Evaluating robustly standardized explainable anomaly detection of implausible variables in cancer data.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Explanations help to understand why anomaly detection algorithms identify data as anomalous. This study evaluates whether robustly standardized explanation scores correctly identify the implausible variables that make cancer data anomalou...