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.
Free text makes it convenient for clinicians to capture rich information about a patient in narratives and first-person stories. But this makes EMRs hard to analyze for big data applications. Despite advances in Natural Language Processing methods, building models is expensive and time-consuming. It requires a long collaboration between clinicians and data-scientists. Clinicians provide annotations and training data, while data-scientists build the models. Further, the current approaches do not provide for clinicians to inspect these models and give feedback.
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.
OpenMRS is a leading open source EHR system implemented in more than 60 low or middle income countries, providing support for the treatment of HIV, MDR-TB, oncology, heart disease and primary and inpatient care. OpenMRS grew out of a collaboration between AMIA members starting at Medinfo 2004. In this talk, presenter Hamish Fraser will outline briefly the history of the project, the current scale of the use of OpenMRS and the range of health care areas supported. He will also discuss the evidence base on clinical impact from OpenMRS, and future directions.
This webinar will present a general approach, mathematical model and computational method to predict clinical efficacy of genetic discoveries using 'clinical avatars' to conduct simulations of the effect of genotypes on risk, diagnosis and treatment. Clinical avatars are individual medical data records produced from a stochastic model and statistical parameters developed to reflect actual patient populations. Clinical variables (clinical, prescription, and genetic) used in the model were derived from examination of published warfarin prediction and decision support algorithms.