AIMC Topic: Electronic Health Records
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AI-based models to predict decompensation on traumatic brain injury patients.
Computers in biology and medicine
Jan 8, 2025
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
Jan 8, 2025
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
Jan 8, 2025
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
Jan 8, 2025
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
Jan 8, 2025
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...
Predicting and Ranking Diabetic Ketoacidosis Risk Among Youth with Type 1 Diabetes with a Clinic-to-Clinic Transferrable Machine Learning Model.
Diabetes technology & therapeutics
Jan 6, 2025
To use electronic health record (EHR) data to develop a scalable and transferrable model to predict 6-month risk for diabetic ketoacidosis (DKA)-related hospitalization or emergency care in youth with type 1 diabetes (T1D). To achieve a sharable pr...
Interpretable machine learning for predicting sepsis risk in emergency triage patients.
Scientific reports
Jan 6, 2025
The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods in emergency triage. The goal was to enhance early sepsis screening by integrating comprehensive tr...
Autonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Using Medical Text.
JMIR medical informatics
Jan 6, 2025
BACKGROUND: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces ...
Assessing the feasibility and external validity of natural language processing-extracted data for advanced lung cancer patients.
Lung cancer (Amsterdam, Netherlands)
Jan 4, 2025
BACKGROUND: Manual extraction of real-world clinical data for research can be time-consuming and prone to error. We assessed the feasibility of using natural language processing (NLP), an AI technique, to automate data extraction for patients with ad...
Leveraging Transformers-based models and linked data for deep phenotyping in radiology.
Computer methods and programs in biomedicine
Jan 3, 2025
BACKGROUND AND OBJECTIVE: Despite significant investments in the normalization and the standardization of Electronic Health Records (EHRs), free text is still the rule rather than the exception in clinical notes. The use of free text has implications...
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