{"product_id":"large-language-models-for-automatic-deidentification-of-electronic-health-record-notes-9789819779659","title":"Large Language Models for Automatic Deidentification of Electronic Health Record Notes","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e.- Deidentification And Temporal Normalization of The Electronic Health Record Notes Using Large Language Models: The 2023 SREDH\/AI-Cup Competition for Deidentification of Sensitive Health Information.\u003c\/p\u003e\u003cp\u003e.- Enhancing Automated De-identification of PathologyText Notes Using Pre-Trained Language Models.\u003c\/p\u003e\u003cp\u003e.- A Comparative Study of GPT3.5 Fine Tuning and Rule-Based Approaches for De-identification and Normalization of Sensitive Health Information in Electronic Medical Record Notes.\u003c\/p\u003e\u003cp\u003e.- Advancing Sensitive Health Data Recognition and Normalization through Large Language Model Driven Data Augmentation.\u003c\/p\u003e\u003cp\u003e.- Privacy Protection and Standardization of Electronic Medical Records Using Large Language Model.\u003c\/p\u003e\u003cp\u003e.- Applying Language Models for Recognizing and Normalizing Sensitive Information from Electronic Health Records Text Notes.\u003c\/p\u003e\u003cp\u003e.- Enhancing SHI Extraction and Time Normalization in Healthcare Records Using LLMs and Dual- Model Voting.\u003c\/p\u003e\u003cp\u003e.- Evaluation of OpenDeID Pipeline in the 2023 SREDH\/AI-Cup Competition for Deidentification of Sensitive Health Information.\u003c\/p\u003e\u003cp\u003e.- Sensitive Health Information Extraction from EMR Text Notes: A Rule-Based NER Approach Using Linguistic Contextual Analysis.\u003c\/p\u003e\u003cp\u003e.- A Hybrid Approach to the Recognition of Sensitive Health Information: LLM and Regular Expressions.\u003c\/p\u003e\u003cp\u003e.- Patient Privacy Information Retrieval with Longformer and CRF, Followed by Rule-Based Time Information Normalization: A Dual-Approach Study.\u003c\/p\u003e\u003cp\u003e.- A Deep Dive into the Application of Pythia for Enhancing Medical Information De-identification in the AI CUP 2023.\u003c\/p\u003e\u003cp\u003e.- Utilizing Large Language Models for Privacy Protection and Advancing Medical Digitization.\u003c\/p\u003e\u003cp\u003e.- Comprehensive Evaluation of Pythia Model Efficiency in De-identification and Normalization for Enhanced Medical Data Management.\u003c\/p\u003e\u003cp\u003e.- A Two-stage Fine-tuning Procedure to Improve the Performance of Language Models in Sensitive Health Information Recognition and Normalization Tasks.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53212903735639,"sku":"9789819779659","price":94.99,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/large-language-models-for-automatic-deidentification-of-electronic-health-record-notes-9789819779659","provider":"Book Curl","version":"1.0","type":"link"}