Working Group

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.

Interactive Natural Language Processing for Clinical Text

Clinicians use free-text to conveniently capture rich information about patients. Care providers are likely to continue using narratives and first-person stories in Electronic Medical Records (EMRs) due to their convenience and utility, which complicates information extraction for computation and analysis. Despite advances in Natural Language Processing (NLP) techniques, building models is often expensive and time-consuming. Current approaches require a long collaboration between clinicians and data-scientists.

A Trilogy on Using Machine Learning to Impute Laboratory Test Results

Clinical decision support that integrates multiple elements of laboratory data could be highly useful in enhancing laboratory diagnosis. Using the analyte ferritin in a proof-of-concept, we extracted clinical laboratory data from patient testing and applied a variety of machine learning algorithms to predict ferritin test results using the results from other tests. We show that predicted ferritin results may sometimes better reflect underlying iron status than measured ferritin. We next integrate temporality into predicting multi-variate analytes.

Dental Informatics Workgroup in Review

This webinar will contain an overview of the future activities that will be conducted within the Dental Informatics Working Group (DI-WG).
The presentation includes introductions of the incoming Chair, Vice Chair, members of DI-WG, and the results of a survey that was conducted to understand the opinions of DI-WG members. We will also update the members with future plans and involve the members in discussion.
This webinar serves to help in increasing the participation and activity of DI-WG members in AMIA.

Analyzing Massive Healthcare Datasets using Apache Spark

The rapid decrease in the cost of sequencing a human genome has made "big data" sequencing studies (>10,000 sample) the new norm. However, working with datasets of this scale requires a new approach to analytics. Bioinformaticians need to harness the power of distributed computing to process these massive datasets for important clinical use cases. Recently, several prominent libraries like GATK4, ADAM, and Hail have taken advantage of Apache Spark, the leading analytics engine for big data processing, to achieve this goal.

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