AIMC Topic: Electronic Health Records

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Identifying protected health information by transformers-based deep learning approach in Chinese medical text.

Health informatics journal
In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for pri...

Current Use And Evaluation Of Artificial Intelligence And Predictive Models In US Hospitals.

Health affairs (Project Hope)
Effective evaluation and governance of predictive models used in health care, particularly those driven by artificial intelligence (AI) and machine learning, are needed to ensure that models are fair, appropriate, valid, effective, and safe, or FAVES...

Prediction of Cisplatin-Induced Acute Kidney Injury Using an Interpretable Machine Learning Model and Electronic Medical Record Information.

Clinical and translational science
Predicting cisplatin-induced acute kidney injury (Cis-AKI) before its onset is important. We aimed to develop a predictive model for Cis-AKI using patient clinical information based on an interpretable machine learning algorithm. This single-center r...

Natural Language Processing to Identify Infants Aged 90 Days and Younger With Fevers Prior to Presentation.

Hospital pediatrics
OBJECTIVE: Natural language processing (NLP) can enhance research studies for febrile infants by more comprehensive cohort identification. We aimed to refine and validate an NLP algorithm to identify and extract quantified temperature measurements fr...

A Dynamic Model for Early Prediction of Alzheimer's Disease by Leveraging Graph Convolutional Networks and Tensor Algebra.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Alzheimer's disease (AD) is a neurocognitive disorder that deteriorates memory and impairs cognitive functions. Mild Cognitive Impairment (MCI) is generally considered as an intermediate phase between normal cognitive aging and more severe conditions...

Uncovering Important Diagnostic Features for Alzheimer's, Parkinson's and Other Dementias Using Interpretable Association Mining Methods.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Alzheimer's Disease and Related Dementias (ADRD) afflict almost 7 million people in the USA alone. The majority of research in ADRD is conducted using post-mortem samples of brain tissue or carefully recruited clinical trial patients. While these res...

Investigating the Differential Impact of Psychosocial Factors by Patient Characteristics and Demographics on Veteran Suicide Risk Through Machine Learning Extraction of Cross-Modal Interactions.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Accurate prediction of suicide risk is crucial for identifying patients with elevated risk burden, helping ensure these patients receive targeted care. The US Department of Veteran Affairs' suicide prediction model primarily leverages structured elec...

Automated Evaluation of Antibiotic Prescribing Guideline Concordance in Pediatric Sinusitis Clinical Notes.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
BACKGROUND: Ensuring antibiotics are prescribed only when necessary is crucial for maintaining their effectiveness and is a key focus of public health initiatives worldwide. In cases of sinusitis, among the most common reasons for antibiotic prescrip...

Comparison of six natural language processing approaches to assessing firearm access in Veterans Health Administration electronic health records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Access to firearms is associated with increased suicide risk. Our aim was to develop a natural language processing approach to characterizing firearm access in clinical records.

Machine learning-based infection diagnostic and prognostic models in post-acute care settings: a systematic review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This study aims to (1) review machine learning (ML)-based models for early infection diagnostic and prognosis prediction in post-acute care (PAC) settings, (2) identify key risk predictors influencing infection-related outcomes, and (3) e...