AIMC Topic: Electronic Health Records

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Integrity of clinical information in radiology reports documenting pulmonary nodules.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Quantify the integrity, measured as completeness and concordance with a thoracic radiologist, of documenting pulmonary nodule characteristics in CT reports and assess impact on making follow-up recommendations.

Colorectal Cancer Detected by Machine Learning Models Using Conventional Laboratory Test Data.

Technology in cancer research & treatment
Current diagnostic methods for colorectal cancer (CRC) are colonoscopy and sigmoidoscopy, which are invasive and complex procedures with possible complications. This study aimed to determine models for CRC identification that involve minimally invas...

Artificial Intelligence in Acute Kidney Injury: From Static to Dynamic Models.

Advances in chronic kidney disease
Artificial intelligence (AI) is the development of computer systems that normally require human intelligence. In the field of acute kidney injury (AKI) AI has led to an evolution of risk prediction models. In the past, static prediction models were d...

Structuring electronic dental records through deep learning for a clinical decision support system.

Health informatics journal
Extracting information from unstructured clinical text is a fundamental and challenging task in medical informatics. Our study aims to construct a natural language processing (NLP) workflow to extract information from Chinese electronic dental record...

Applied natural language processing in mental health big data.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology

Clinical concept extraction using transformers.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The goal of this study is to explore transformer-based models (eg, Bidirectional Encoder Representations from Transformers [BERT]) for clinical concept extraction and develop an open-source package with pretrained clinical models to facili...

An approach to predicting patient experience through machine learning and social network analysis.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Improving the patient experience has become an essential component of any healthcare system's performance metrics portfolio. In this study, we developed a machine learning model to predict a patient's response to the Hospital Consumer Asse...

Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: In applying machine learning (ML) to electronic health record (EHR) data, many decisions must be made before any ML is applied; such preprocessing requires substantial effort and can be labor-intensive. As the role of ML in health care gro...

Reporting of demographic data and representativeness in machine learning models using electronic health records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The development of machine learning (ML) algorithms to address a variety of issues faced in clinical practice has increased rapidly. However, questions have arisen regarding biases in their development that can affect their applicability i...

MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care.

Journal of the American Medical Informatics Association : JAMIA
The rise of digital data and computing power have contributed to significant advancements in artificial intelligence (AI), leading to the use of classification and prediction models in health care to enhance clinical decision-making for diagnosis, tr...