AIMC Topic: Retrospective Studies

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Development and Evaluation of Machine Learning Models for the Identification of Surgical Site Infection in Electronic Health Records.

Surgical infections
Surgical site infection (SSI) affects 160,000-300,000 patients per year in the United States, adversely impacting a wide range of patient- and health-system outcomes. Surveillance programs for SSI are essential to quality improvement and public heal...

AI-ECG Supported Decision-Making for Coronary Angiography in Acute Chest Pain: The QCG-AID Study.

Journal of Korean medical science
This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively...

Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study.

BMC medical imaging
BACKGROUND: Microvascular invasion (MVI) is an important risk factor for early postoperative recurrence of hepatocellular carcinoma (HCC). Based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance ...

AI-Based Identification Method for Cervical Transformation Zone Within Digital Colposcopy: Development and Multicenter Validation Study.

JMIR cancer
BACKGROUND: In low- and middle-income countries, cervical cancer remains a leading cause of death and morbidity for women. Early detection and treatment of precancerous lesions are critical in cervical cancer prevention, and colposcopy is a primary d...

Optimizing Initial Vancomycin Dosing in Hospitalized Patients Using Machine Learning Approach for Enhanced Therapeutic Outcomes: Algorithm Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Vancomycin is commonly dosed using standard weight-based methods before dose adjustments are made through therapeutic drug monitoring (TDM). However, variability in initial dosing can lead to suboptimal therapeutic outcomes. A predictive ...

Deep Learning and Radiomics Discrimination of Coronary Chronic Total Occlusion and Subtotal Occlusion using CTA.

Academic radiology
RATIONALE AND OBJECTIVES: Coronary chronic total occlusion (CTO) and subtotal occlusion (STO) pose diagnostic challenges, differing in treatment strategies. Artificial intelligence and radiomics are promising tools for accurate discrimination. This s...

Natural language processing for identifying major bleeding risk in hospitalised medical patients.

Computers in biology and medicine
BACKGROUND: Major bleeding is a severe complication in critically ill medical patients, resulting in significant morbidity, mortality, and healthcare costs. This study aims to assess the incidence and risk factors for major bleeding in hospitalised m...

Multimodal machine learning for predicting perioperative safety indicators in spinal surgery.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Machine learning (ML) algorithms can utilize the large amount of tabular data in electronic health records (EHRs) to predict perioperative safety indicators. Integrating unstructured free-text inputs via natural language processin...

Ultrasound-based deep learning to differentiate salivary gland tumors.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: Accurate preoperative diagnosis is essential for selecting appropriate surgical interventions. This study aims to develop a deep learning model based on ultrasound (US) imaging to accurately differentiate between benign and malignant saliv...