How has natural language processing been applied in clinical decision support systems?
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Natural language processing (NLP) has been extensively utilized in clinical decision support systems (CDSS). Studies have shown the development of NLP-based CDSS for tasks like extracting information from electronic health records (EHRs) to support decision-making. These systems translate medical documents into structured data, enabling the identification of patient allergies, disease diagnosis, and risk assessment for conditions like venous thromboembolism (VTE). NLP algorithms have demonstrated high accuracy in detecting VTEs from EHRs, with sensitivity up to 89.9% and specificity up to 99.8%. The integration of unsupervised and supervised machine learning with rule-based algorithms enhances system performance, efficiency, and interpretability, leading to increased detection of patient allergies and improved patient safety during medical procedures.
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| Papers (4) | Insight |
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| Natural language processing (NLP) is utilized in a clinical decision support system for venous thromboembolism (VTE) detection, showing high sensitivity, specificity, and performance across different clinical settings. | |
| Clinical Natural Language Processing (NLP) in decision support systems mostly relies on rule-based or statistical methods, lacking widespread deep learning application unlike other domains, except for image tasks. | |
| Natural language processing is utilized in clinical decision support systems by translating referral letters into semantic matrices, enabling accurate classification of medical specialities to support clinical decision making. | |
26 Jun 2023 | Natural language processing is utilized in clinical decision support systems to extract and process unstructured clinical texts, enabling the development of predictive models for disease diagnosis. |
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