AIMC Topic: Electronic Health Records

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From Text to Knowledge: An End-To-End Extraction Pipeline for Clinical Information.

Studies in health technology and informatics
This study explores the use of Large Language Models (LLMs) in extracting and structuring allergic reaction data from non-English clinical free texts. Leveraging open-source models such as Llama 3.1, Qwen 2.5, and Mistral NeMo, the study utilizes 500...

A Computational Framework for Tailored Preventive Care Recommendations Using Electronic Health Records.

Studies in health technology and informatics
Most healthcare systems worldwide are designed to be reactive. According to the U.S. Centers for Disease Control and Prevention (CDC), 90% of the nation's $3.3 trillion annual healthcare expenditures are attributed to individuals with chronic and men...

Evaluation of Federated Learning Using Standardized EHR Data in Japan.

Studies in health technology and informatics
This study addresses privacy concerns in multi-institutional data sharing by applying federated learning (FL) to develop a predictive model for prolonged air leaks (PAL) following video-assisted thoracoscopic surgery (VATS). Utilizing standardized el...

LLM-Based Medical Document Evaluation: Integrating Human Expert Insights.

Studies in health technology and informatics
Large Language Models (LLMs) show potential in medical document generation, but ensuring reliability requires extensive expert involvement, limiting clinical applications. To address this challenge, we developed an LLM-based evaluation framework with...

Automated Detection of Invasive Fungal Infections in Clinical Reports Using Medical Language Models.

Studies in health technology and informatics
Invasive fungal infections (IFIs) pose significant risks to patients with weakened immune systems, requiring timely detection. To improve IFI detection from clinical reports, we explore the value of recent advances in NLP techniques for this task, in...

Development and Validation of Machine-Learning Algorithms to Predict the Onset of Depression Using Electronic Health Record Data: A Prognostic Modeling Study.

Studies in health technology and informatics
INTRODUCTION: Early detection and intervention are crucial for reducing the impacts of depression and associated healthcare costs. Few studies have used electronic health records (EHR) and machine learning (ML) with a longitudinal design to predict d...

Development of Multivariable Prediction Models for 30-Day Risk of Readmission After COPD Hospital Admission: A Retrospective Cohort Study Using Electronic Medical Record Data from 7 Hospitals.

Studies in health technology and informatics
BACKGROUND: Approximately 20% of patients who are discharged from hospital for an acute exacerbation of COPD (AECOPD) are readmitted within 30 days. Prediction scores are helpful to identify those who are at higher risk of readmission, such that they...

Structured LLM Augmentation for Clinical Information Extraction.

Studies in health technology and informatics
Information extraction tasks, such as Named Entity Recognition (NER) and Relation Extraction (RE), are essential for advancing clinical research and applications. However, these tasks are hindered by the scarcity of labeled clinical documents due to ...

Constrained Tensor Factorization for Cancer Phenotyping and Mortality Prediction.

Studies in health technology and informatics
Electronic health records (EHR) enable machine learning methods like tensor factorization to extract computational phenotypes. Using Northwestern Medicine data (2000-2015), we analyzed breast, prostate, colorectal, and lung cancer cohorts to predict ...

Predicting Nephrectomy Risk in Patients with Renal Cancer Using Real-World Electronic Health Records.

Studies in health technology and informatics
Nephrectomy, the surgical removal of a kidney, is a critical treatment for renal cancer, and predicting its likelihood can help guide clinical decision-making and optimize preoperative planning. This study utilized real-world electronic health record...