AIMC Topic: Retrospective Studies

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Machine learning prediction model for functional prognosis of acute ischemic stroke based on MRI radiomics of white matter hyperintensities.

BMC medical imaging
OBJECTIVE: The purpose of the current study is to explore the value of a nomogram that integrates clinical factors and MRI white matter hyperintensities (WMH) radiomics features in predicting the prognosis at 90 days for patients with acute ischemic ...

Predictive modeling of pregnancy outcomes utilizing multiple machine learning techniques for in vitro fertilization-embryo transfer.

BMC pregnancy and childbirth
OBJECTIVE: This study aims to investigate the influencing factors of pregnancy outcomes during in vitro fertilization and embryo transfer (IVF-ET) procedures in clinical practice. Several prediction models were constructed to predict pregnancy outcom...

Predicting intra-abdominal hypertension using anthropometric measurements and machine learning.

Scientific reports
Almost one in four critically ill patients suffer from intra-abdominal hypertension (IAH). Currently, the gold standard for measuring intra-abdominal pressure (IAP) is via the bladder. Measurement of IAP is important to identify IAH early and thus im...

Machine Learning-Based Explainable Automated Nonlinear Computation Scoring System for Health Score and an Application for Prediction of Perioperative Stroke: Retrospective Study.

Journal of medical Internet research
BACKGROUND: Machine learning (ML) has the potential to enhance performance by capturing nonlinear interactions. However, ML-based models have some limitations in terms of interpretability.

The impact of multi-modality fusion and deep learning on adult age estimation based on bone mineral density.

International journal of legal medicine
INTRODUCTION: Age estimation, especially in adults, presents substantial challenges in different contexts ranging from forensic to clinical applications. Bone mineral density (BMD), with its distinct age-related variations, has emerged as a critical ...

Utilising Natural Language Processing to Identify Brain Tumor Patients for Clinical Trials: Development and Initial Evaluation.

World neurosurgery
BACKGROUND: Identifying patients eligible for clinical trials through eligibility screening is time and resource-intensive. Natural Language Processing (NLP) models may enhance clinical trial screening by extracting data from Electronic Health Record...

Deep learning by Vision Transformer to classify bacterial and fungal keratitis using different types of anterior segment images.

Computers in biology and medicine
PURPOSE: To develop three novel Vision Transformer (ViT) frameworks for the specific diagnosis of bacterial and fungal keratitis using different types of anterior segment images and compare their performances.

GPT for RCTs? Using AI to determine adherence to clinical trial reporting guidelines.

BMJ open
OBJECTIVES: Adherence to established reporting guidelines can improve clinical trial reporting standards, but attempts to improve adherence have produced mixed results. This exploratory study aimed to determine how accurate a large language model gen...

Interpretable machine learning models for prolonged Emergency Department wait time prediction.

BMC health services research
OBJECTIVE: Prolonged Emergency Department (ED) wait times lead to diminished healthcare quality. Utilizing machine learning (ML) to predict patient wait times could aid in ED operational management. Our aim is to perform a comprehensive analysis of M...