Increasingly, vast amounts of health-related data are being generated by a diverse array of sources, such as EHRs, medical claims, product and disease registries, laboratory test results and even consumer mobile devices.
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.
Routinely collected patient electronic medical record (EMR) data are approaching the genomic scale in volume and complexity and is increasingly recognized as a valuable resource for clinical research to answer questions for broader populations than would have ever been possible with a specialized research environment.
This is a webinar on applications of NLP to clinical psychology. It is novel and has been primarily tackled in open domain NLP. This webinar brings together open domain NLP with clinical NLP and is expected to unify two otherwise fairly separate communities.
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.
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.
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.
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.
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.