AIMC Topic: Electronic Health Records
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Automated Detection of Invasive Fungal Infections in Clinical Reports Using Medical Language Models.
Studies in health technology and informatics
Aug 7, 2025
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
Aug 7, 2025
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
Aug 7, 2025
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
Aug 7, 2025
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
Aug 7, 2025
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
Aug 7, 2025
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...
Automate Creating, Customizing, and Optimizing Comorbidity Indices Using a Data-Driven AI/ML Approach.
Studies in health technology and informatics
Aug 7, 2025
Due to individual differences in severity of illness, clinical studies typically use a comorbidity index to adjust outcomes. With the increasing use of electronic health records (EHRs) to assess the quality of care, a key question arises: how to adju...
AI Bias and Confounding Risk in Health Feature Engineering for Machine Learning Classification Task.
Studies in health technology and informatics
Aug 7, 2025
Recent advancements in machine learning bring unique opportunities in health fields but also pose considerable challenges. Due to stringent ethical considerations and resource constraints, health data can vary in scope, population coverage, and colle...
Leveraging Retrieval Augmented Generation-Driven Large Language Models to Extract Dementia Agitation Symptoms and Triggers from Free-Text Nursing Notes.
Studies in health technology and informatics
Aug 7, 2025
Unstructured electronic health records are a rich source of patient-specific information but are challenging for analysis due to inconsistent terminology, diverse data formats, and extensive free-text content. To address this, we developed a named en...
Beyond GPT-NER: ChatGPT as Ensemble Arbitrator for Discontinuous Named Entity Recognition in Health Corpora.
Studies in health technology and informatics
Aug 7, 2025
In medicine and healthcare, NER (Named Entity Recognition) involves identifying clinically relevant entities such as medications, symptoms, and adverse drug events (ADEs). This task is particularly challenging due to discontinuous NER (DNER), fragmen...
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