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| http://dx.doi.org/10.30476/JAMP.2023.100264.1883 | DOI Listing |
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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 Med Inform
January 2025
Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
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 challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.
View Article and Find Full Text PDFETL: From the German Health Data Lab data formats to the OMOP Common Data Model.
PLoS One
January 2025
Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Bremen, Germany.
Objective: The German Health Data Lab is going to provide access to German statutory health insurance claims data ranging from 2009 to the present for research purposes. Due to evolving data formats within the German Health Data Lab, there is a need to standardize this data into a Common Data Model to facilitate collaborative health research and minimize the need for researchers to adapt to multiple data formats. For this purpose we selected transforming the data to the Observational Medical Outcomes Partnership Common Data Model.
View Article and Find Full Text PDFDiagnosis of approximal caries in children with convolutional neural networks based detection algorithms on radiographs: A pilot study.
Acta Odontol Scand
January 2025
Electronic and Department of Electronics and Automation, Tekirdag Namik Kemal University, Tekirdag, Turkey.
Objectives: Approximal caries diagnosis in children is difficult, and artificial intelligence-based research in pediatric dentistry is scarce. To create a convolutional neural network (CNN)-based diagnostic system for the prompt and efficient identification of approximal caries in pediatric patients aged 5-12 years.
Materials And Methods: Pediatric patients' digital periapical radiographic images were collected to create a unique dataset.
The clinical efficacy of a novel smartphone-based salivary self-test for the prediction of pre-eclampsia, pregnancy-induced hypertension and intrauterine growth restriction: a prospective cohort study.
Front Med (Lausanne)
December 2024
Department of Obstetrics and Gynecology and ReproHealth Consortium, Zealand University Hospital, Roskilde, Denmark.
Introduction: This study investigated the efficacy of a digital health solution utilizing smartphone images of colorimetric test-strips for home-based salivary uric acid (sUA) measurement to predict pre-eclampsia (PE), pregnancy-induced hypertension (PIH), and intrauterine growth restriction (IUGR).
Methods: 495 pregnant women were included prospectively at Zealand University Hospital, Denmark. They performed weekly self-tests from mid-pregnancy until delivery and referred these for analysis by a smartphone-app.
Virtual Primary Care-Improving Access, Efficiency, and Patient Experience.
Telemed Rep
December 2024
Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA.
The United States faces a growing scarcity of primary care resources within an already overstressed and poorly accessible health care system. Many health care organizations are evaluating novel models of care and adoption of digital technologies to improve primary care access and efficiency of health care delivery. This article describes a virtual primary care (VPC) model that expands access and increases the efficiency of the traditional primary care team by utilizing on-demand and asynchronous digital tools.
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