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Unsupervised SapBERT-based bi-encoders for medical concept annotation of clinical narratives with SNOMED CT
Clinical narratives provide comprehensive patient information. Achieving interoperability involves mapping relevant details to …
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Large Language Models for Clinical Text Cleansing Enhance Medical Concept Normalization
Most clinical information is only available as free text. Large language models (LLMs) are increasingly applied to clinical data to …
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Investigating the Impact of AI on Shared Decision-Making in Post-Kidney Transplant Care (PRIMA-AI): Protocol for a Randomized Controlled Trial
Background: Patients after kidney transplantation eventually face the risk of graft loss with the concomitant need for dialysis or …
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SapBERT-Based Medical Concept Normalization Using SNOMED CT
Word vector representations, known as embeddings, are commonly used for natural language processing. Particularly, contextualized …
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When performance is not enough—A multidisciplinary view on clinical decision support
Scientific publications about the application of machine learning models in healthcare often focus on improving performance metrics. …
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Automatic Extraction of Medication Mentions from Tweets—Overview of the BioCreative VII Shared Task 3 Competition
This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names …
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''Nothing works without the doctor:'' Physicians' perception of clinical decision-making and artificial intelligence
Introduction: Artificial intelligence–driven decision support systems (AI–DSS) have the potential to help physicians analyze data and …
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XAINES: Explaining AI with Narratives
Artificial Intelligence (AI) systems are increasingly pervasive: Internet of Things, in-car intelligent devices, robots, and virtual …
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Evaluation of a clinical decision support system for detection of patients at risk after kidney transplantation
Patient care after kidney transplantation requires integration of complex information to make informed decisions on risk …
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