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Electronic Health Records

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Identification of an ANCA-associated vasculitis cohort using deep learning and electronic health records.

International journal of medical informatics
BACKGROUND: ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electro...

Artificial intelligence-based biomarkers for treatment decisions in oncology.

Trends in cancer
The development of new therapeutic strategies such as immune checkpoint inhibitors (ICIs) and targeted therapies has increased the complexity of the treatment landscape for solid tumors. At the current rate of annual FDA approvals, the potential trea...

Natural language processing to identify suicidal ideation and anhedonia in major depressive disorder.

BMC medical informatics and decision making
BACKGROUND: Anhedonia and suicidal ideation are symptoms of major depressive disorder (MDD) that are not regularly captured in structured scales but may be captured in unstructured clinical notes. Natural language processing (NLP) techniques may be u...

Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.

Drug safety
BACKGROUND: Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (EHR) data to detect adverse drug events (ADEs) and thus improve pharmacovigilance. However, evidence ...

Multimodal deep learning for predicting in-hospital mortality in heart failure patients using longitudinal chest X-rays and electronic health records.

The international journal of cardiovascular imaging
Amid an aging global population, heart failure has become a leading cause of hospitalization among older people. Its high prevalence and mortality rates underscore the importance of accurate mortality prediction for swift disease progression assessme...

AI-based models to predict decompensation on traumatic brain injury patients.

Computers in biology and medicine
Traumatic Brain Injury (TBI) is a form of brain injury caused by external forces, resulting in temporary or permanent impairment of brain function. Despite advancements in healthcare, TBI mortality rates can reach 30%-40% in severe cases. This study ...

Assessment of Real-Time Natural Language Processing for Improving Diagnostic Specificity: A Prospective, Crossover Exploratory Study.

Applied clinical informatics
BACKGROUND:  Reliable, precise, timely, and clear documentation of diagnoses is difficult. Poor specificity or the absence of diagnostic documentation can lead to decreased revenue and increased payor denials, audits, and queries to providers. Nuance...

Utilizing natural language processing to identify pediatric patients experiencing status epilepticus.

Seizure
PURPOSE: Compare the identification of patients with established status epilepticus (ESE) and refractory status epilepticus (RSE) in electronic health records (EHR) using human review versus natural language processing (NLP) assisted review.

Hybrid natural language processing tool for semantic annotation of medical texts in Spanish.

BMC bioinformatics
BACKGROUND: Natural language processing (NLP) enables the extraction of information embedded within unstructured texts, such as clinical case reports and trial eligibility criteria. By identifying relevant medical concepts, NLP facilitates the genera...

Predicting cardiovascular disease in patients with mental illness using machine learning.

European psychiatry : the journal of the Association of European Psychiatrists
BACKGROUND: Cardiovascular disease (CVD) is twice as prevalent among individuals with mental illness compared to the general population. Prevention strategies exist but require accurate risk prediction. This study aimed to develop and validate a mach...