Routine patient data in electronic patient records are only partly structured, and an even smaller segment is coded, mainly for administrative purposes. Large parts are only available as free text. Transforming this content into a structured and semantically explicit form is a prerequisite for querying and information extraction. The core of the system architecture presented in this paper is based on SAP HANA in-memory database technology using the SAP Connected Health platform for data integration as well as for clinical data warehousing. A natural language processing pipeline analyses unstructured content and maps it to a standardized vocabulary within a well-defined information model. The resulting semantically standardized patient profiles are used for a broad range of clinical and research application scenarios.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Unsupervised domain adaptation teacher-student network for retinal vessel segmentation via full-resolution refined model.
Sci Rep
January 2025
School of Computer Science, Hunan First Normal University, Changsha, 410205, China.
Retinal blood vessels are the only blood vessels in the human body that can be observed non-invasively. Changes in vessel morphology are closely associated with hypertension, diabetes, cardiovascular disease and other systemic diseases, and computers can help doctors identify these changes by automatically segmenting blood vessels in fundus images. If we train a highly accurate segmentation model on one dataset (source domain) and apply it to another dataset (target domain) with a different data distribution, the segmentation accuracy will drop sharply, which is called the domain shift problem.
View Article and Find Full Text PDFHeterogeneous entity representation for medicinal synergy prediction.
Bioinformatics
January 2025
School of Data Science and Society, University of North Carolina at Chapel Hill, NC 27599, United States.
Motivation: Forecasting the synergistic effects of drug combinations facilitates drug discovery and development, especially regarding cancer therapeutics. While numerous computational methods have emerged, most of them fall short in fully modeling the relationships among clinical entities including drugs, cell lines, and diseases, which hampers their ability to generalize to drug combinations involving unseen drugs. These relationships are complex and multidimensional, requiring sophisticated modeling to capture nuanced interplay that can significantly influence therapeutic efficacy.
View Article and Find Full Text PDFTrends and gaps in hydroxychloroquine and COVID-19 research (2020-2023): Performance and conceptual mapping.
J Infect Public Health
December 2024
Health Research Center, Jazan University, Jazan, Saudi Arabia. Electronic address:
Background: Hydroxychloroquine and Chloroquine (CQ) and Hydroxychloroquine (HCQ) are antimalarial drugs with well-known anti-inflammatory and antiviral effects used to treat various diseases, with few side effects. After COVID-19 emergence, numerous researches from around the world have examined the potential of using CQ or HCQ as potential treatment of COVID-19. However, conflicting outcomes have been found in COVID-19 clinical trials after treatment with CQ or HCQ.
View Article and Find Full Text PDF[Implementation and evaluation of a solution for automated documentation of the doctor-patient dialogue (ADAPI) in ophthalmology using the example of the IVI routine].
Ophthalmologie
January 2025
Augenklinik Sulzbach, Knappschaftsklinikum Saar, An der Klinik 10, 66280, Sulzbach/Saar, Deutschland.
Background: The increasing bureaucratic burden in everyday clinical practice impairs doctor-patient communication (DPC). Effective use of digital technologies, such as automated semantic speech recognition (ASR) with automated extraction of diagnostically relevant information can provide a solution.
Objective: The aim was to determine the extent to which ASR in conjunction with semantic information extraction for automated documentation of the doctor-patient dialogue (ADAPI) can be integrated into everyday clinical practice using the IVI routine as an example and whether patient care can be improved through process optimization.
MVCL-DTI: Predicting Drug-Target Interactions Using a Multiview Contrastive Learning Model on a Heterogeneous Graph.
J Chem Inf Model
January 2025
School of Computer Science and Technology, Soochow University, Jiangsu 215006, China.
Accurate prediction of drug-target interactions (DTIs) is pivotal for accelerating the processes of drug discovery and drug repurposing. MVCL-DTI, a novel model leveraging heterogeneous graphs for predicting DTIs, tackles the challenge of synthesizing information from varied biological subnetworks. It integrates neighbor view, meta-path view, and diffusion view to capture semantic features and employs an attention-based contrastive learning approach, along with a multiview attention-weighted fusion module, to effectively integrate and adaptively weight the information from the different views.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!