AIMC Topic: Registries

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Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models.

BMC psychiatry
BACKGROUND: Suicidal behaviors, which may lead to death (suicide) or survival (suicide attempt), are influenced by various factors. Identifying the specific risk factors for suicidal behavior mortality is critical for improving prevention strategies ...

Using machine learning to identify pediatric ophthalmologists.

Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus
This cross-sectional study used data from the American Academy of Ophthalmology IRIS Registry (Intelligent Research in Sight) and machine learning algorithms to identify pediatric ophthalmologists based on physician coding patterns. A random forest m...

Classifying Tumor Reportability Status From Unstructured Electronic Pathology Reports Using Language Models in a Population-Based Cancer Registry Setting.

JCO clinical cancer informatics
PURPOSE: Population-based cancer registries (PBCRs) collect data on all new cancer diagnoses in a defined population. Data are sourced from pathology reports, and the PBCRs rely on manual and rule-based solutions. This study presents a state-of-the-a...

Using Artificial Intelligence to Identify Three Presenting Phenotypes of Chiari Type-1 Malformation and Syringomyelia.

Neurosurgery
BACKGROUND AND OBJECTIVES: Chiari type-1 malformation (CM1) and syringomyelia (SM) are common related pediatric neurosurgical conditions with heterogeneous clinical and radiological presentations that offer challenges related to diagnosis and managem...

Predicting stroke severity of patients using interpretable machine learning algorithms.

European journal of medical research
BACKGROUND: Stroke is a significant global health concern, ranking as the second leading cause of death and placing a substantial financial burden on healthcare systems, particularly in low- and middle-income countries. Timely evaluation of stroke se...

Machine learning for outcome prediction in patients with non-valvular atrial fibrillation from the GLORIA-AF registry.

Scientific reports
Clinical risk scores that predict outcomes in patients with atrial fibrillation (AF) have modest predictive value. Machine learning (ML) may achieve greater results when predicting adverse outcomes in patients with recently diagnosed AF. Several ML m...

Automated real-world data integration improves cancer outcome prediction.

Nature
The digitization of health records and growing availability of tumour DNA sequencing provide an opportunity to study the determinants of cancer outcomes with unprecedented richness. Patient data are often stored in unstructured text and siloed datase...

Machine learning models for predicting dysphonia following anterior cervical discectomy and fusion: a Swedish Registry Study.

The spine journal : official journal of the North American Spine Society
BACKGROUND: Dysphonia is one of the more common complications following anterior cervical discectomy and fusion (ACDF). ACDF is the gold standard for treating degenerative cervical spine disorders, and identifying high-risk patients is therefore cruc...

A Machine Learning-derived Risk Score Improves Prediction of Outcomes After LVAD Implantation: An Analysis of the INTERMACS Database.

Journal of cardiac failure
BACKGROUND: Significant variability in outcomes after left ventricular assist device (LVAD) implantation emphasize the importance of accurately assessing patients' risk before surgery. This study assesses the Machine Learning Assessment of Risk and E...

Uncertainty-Aware Deep Learning Characterization of Knee Radiographs for Large-Scale Registry Creation.

The Journal of arthroplasty
BACKGROUND: We present an automated image ingestion pipeline for a knee radiography registry, integrating a multilabel image-semantic classifier with conformal prediction-based uncertainty quantification and an object detection model for knee hardwar...