AIMC Topic: Electronic Health Records
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Evaluating LLMs for Diagnosis Summarization.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
During a patient's hospitalization, extensive information is documented in clinical notes. The efficient summarization of this information is vital for keeping healthcare professionals abreast of the patient's status. This paper proposes a methodolog...
Predicting Diabetes in Canadian Adults Using Machine Learning.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Rising diabetes rates have led to increased healthcare costs and health complications. An estimated half of diabetes cases remain undiagnosed. Early and accurate diagnosis is crucial to mitigate disease progression and associated risks. This study ad...
Deep STI: Deep Stochastic Time-series Imputation on Electronic Health Records.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Electronic Health Records (EHRs) are a cornerstone of modern healthcare analytics, offering rich datasets for various disease analyses through advanced deep learning algorithms. However, the pervasive issue of missing values in EHRs significantly ham...
Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.
JCO clinical cancer informatics
Jul 1, 2024
PURPOSE: Stage in multiple myeloma (MM) is an essential measure of disease risk, but its measurement in large databases is often lacking. We aimed to develop and validate a natural language processing (NLP) algorithm to extract oncologists' documenta...
Deep Learning-Based Dynamic Risk Prediction of Venous Thromboembolism for Patients With Ovarian Cancer in Real-World Settings From Electronic Health Records.
JCO clinical cancer informatics
Jul 1, 2024
PURPOSE: Patients with epithelial ovarian cancer (EOC) have an elevated risk for venous thromboembolism (VTE). To assess the risk of VTE, models were developed by statistical or machine learning algorithms. However, few models have accommodated deep ...
Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.
CNS neuroscience & therapeutics
Jul 1, 2024
AIMS: To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals, collected within 24 h of ICU admission, for traumatic brain injury (TBI) patient outcomes.
A Comparison of Five Algorithmic Methods and Machine Learning Pattern Recognition for Artifact Detection in Electronic Records of Five Different Vital Signs: A Retrospective Analysis.
Anesthesiology
Jul 1, 2024
BACKGROUND: Research on electronic health record physiologic data is common, invariably including artifacts. Traditionally, these artifacts have been handled using simple filter techniques. The authors hypothesized that different artifact detection a...
TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records.
Bioinformatics (Oxford, England)
Jun 28, 2024
MOTIVATION: Electronic health records (EHRs) represent a comprehensive resource of a patient's medical history. EHRs are essential for utilizing advanced technologies such as deep learning (DL), enabling healthcare providers to analyze extensive data...
Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.
Blood advances
Jun 25, 2024
Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE cases is limited by the challenges of manual medical record review and diagnosis code interpretation. Natural language processing (NLP) can automate ...
Comparing the quality of ChatGPT- and physician-generated responses to patients' dermatology questions in the electronic medical record.
Clinical and experimental dermatology
Jun 25, 2024
BACKGROUND: ChatGPT is a free artificial intelligence (AI)-based natural language processing tool that generates complex responses to inputs from users.
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