The Longitudinal Medical Record & Natural Language Processing: The Challenge of Temporality скачать в хорошем качестве

The Longitudinal Medical Record & Natural Language Processing: The Challenge of Temporality

(Presented at American Medical Informatics Association 2022 Annual Symposium, November 7, 2022) Session: IPS03 "Longitudinal Medical Record and Natural Language Processing: The Challenge of Temporality" Presenter: Dr. Jonathan D. Gold MD MHA MSc FAMIA FHIMSS email to: drjgold@yahoo.com Precision medicine, artificial intelligence, data analytics, and predictive modeling hold great promise to advance healthcare, possibly as dramatically as the introduction of rigorous scientific research methodology to medicine in the last century. Incorporation of temporality fills a critical gap in the interpretation of health data, providing agency for deriving meaningful associations between observables, treatments and outcomes. Text fields appearing throughout the medical record, contain essential narratives describing health events. Natural language processing can highlight and codify these narratives including their temporal qualities and be used to construct a health timeline. Timelines, in turn, enable a synopsis view of key health events for a patient, comparison with peers and identification of highly similar patients. Temporal objects, following syntactic rules and semantic validation, serve as building blocks for defining the temporal aspects of events. Three temporal perspectives provide important vantages for understanding events—biographic, differential, and extrinsic. Additional Concepts: NLP, EMR, temporal reasoning, health timeline This presentation is part of the series: Exploring Time, Space, Medicine, and Beyond. Through these video presentations we jump into the significance of capturing and understanding temporospatial relationships in medical records, how we should approach the inference of time in unstructured text, and how we can employ these data points to enrich predictive modeling and advance healthcare using real world experience. 1) Temporality and Medicine--Overview (20 min view) Development of a critical domain to support natural language processing, data analytics and predictive modeling    • Exploring Time, Space, Medicine, and Beyon...   2) Temporality and Natural Language Processing (23 min view) Temporal text must permit quantified interpretation leading to a specific point (or range) in time    • Exploring Time, Space, Medicine, and Beyon...   3) Events and Temporal Alignment (29 min view) Three critical components (event type, precision, and source veracity) crucial in resolving record differences when building patient health timeline    • Exploring Time, Space, Medicine, and Beyon...   4) The Significance of the Longitudinal Electronic Medical Records (23 min view) Visual presentation of the longitudinal record graphically displays events emphasizing continuity and element relationships over time    • Exploring Time, Space, Medicine, and Beyon...   5) Beyond N of One (18 min view) Matching multiple patient characteristics enables patient-specific decision support and customized, precision medicine tailored to an individual    • Exploring Time, Space, Medicine, and Beyon...   6) The Longitudinal Medical Record & Natural Language Processing: The Challenge of Temporality (49 min view)    • The Longitudinal Medical Record & Natural ...   7) Rules for Temporality in Medicine: A Focused Approach to Guiding LLM Logic (29 min view)    • Rules for Temporality in Medicine: A Focus...   Blog Site for Exploring Time, Space, Medicine, and Beyond https://timespacemedicine.wixsite.com... Author's LinkedIn   / jonathangoldmd   Keywords: Temporality, Temporospatial Relationships, Predictive Modeling, Precision Medicine, Data Analytics, Population Health, Longitudinal Electronic Medical Record (LEMR), Data Visualization, Problem List Management, Data Quality, Data Normalization, Natural Language Processing (NLP), Machine Learning (ML), Artificial Intelligence (AI), Large Language Models (LLM), Unstructured Text, Health Information Exchange (HIE), Health, Medicine