Working Group

Boosting rule-based clinical natural language processing using trie-based structure

Notwithstanding the popularity of machine learning in natural language processing (NLP), rule-based systems have their advantages: distinctive transparency, ease of incorporating external knowledge, and demanding fewer annotations. However, processing efficiency and rule complexity remain major obstacles for adopting rule-based NLP solutions in large clinical data analyses. This talk will introduce a new rule processing engine that allows fast rule execution and structured rule construction for clinical NLP that helps to reduce the rule complexity.

A natural language processing method to identify social determinants of health in electronic health records

Understanding how to identify the social determinants of health from electronic health records (EHRs) could provide important insights to understand health or disease outcomes. We developed a methodology to capture 2 rare and severe social determinants of health, homelessness and adverse childhood experiences (ACEs), from a large EHR repository.

Delineating Clinical Informatics Subspecialty Practice: Results and Implications of AMIA’s Practice Analysis

This presentation will describe the process and results of a recently completed formal practice analysis of Clinical Informatics conducted by the American Medical Informatics Association in collaboration with the American Board of Preventive Medicine and with the support of the American Board of Pathology. The aim of the practice analysis was to develop a comprehensive and current description of what Clinical Informatics Subspecialty physician diplomates do and what they need to know.

Teledentistry: Current Status and Future Trends for Improved Patient Care

Enormous potential exists to improve oral health services throughout the world by using information and communication technologies, such as teledentistry, to expand access to primary, secondary and tertiary care. Comparison of teledentistry procedures with standard clinical procedures can demonstrate the relative effectiveness and cost of each approach. However, due to insufficient evidence, it is unclear how these strategies compare for improving and maintaining oral health, quality of life, and reducing healthcare costs.

Data-driven Discovery in Pharmacovigilance

The aim of pharmacovigilance is to identify and describe adverse events related to the use of medicines and to support wise therapeutic decisions. By nature, pharmacovigilance focuses on the unexpected and relies on effective methods for data-driven discovery. In this talk I will highlight initiatives seeking to improve our ability to do exploratory analysis in observational medical data, using statistical pattern discovery, latent class cluster analysis and natural language processing.


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