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.
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.
Suicide accounted for nearly 45,000 deaths in the United States in 2016. Unfortunately, tools currently used to predict an individual’s risk of a suicide attempt or dying by suicide, such as brief self-report measures, have only moderate accuracy. Speaker Gregory Simon, MD, MPH, a senior investigator at the Kaiser Permanente Washington Health Research Institute, was the lead author on a paper published online on May 24, 2018, in the American Journal of Psychiatry in which Dr.
One of the major barriers to leveraging EHR data for clinical and translational science is the tremendous unstructured or semi-structured clinical narratives remain unparsed and underused. As demonstrated by large scale efforts such as ACT (Accrual of patients for Clinical Trials), eMERGE, and PCORnet, using EHR data for research rests on the capabilities of a robust data and informatics infrastructure that allows the structuring of clinical narratives and supports the extraction of clinical information for downstream applications.
The author Paul Auster once wrote, “The truth of the story lies in the details.” While medical informatics often focuses on the technical details of health information technology (IT) implementation, the actual story of implementing health IT is much deeper and more nuanced. People and process components are often critical drivers in the implementation and adoption of new health IT systems, as both research and experience demonstrates.
The purpose of this webinar is to learn about mutually beneficial intersections between health informatics and implementation science, a discipline that studies the translation of evidence into routine practice to achieve sustained improvements in health outcomes. Participants will learn about both sides, from the use of health informatics tools in implementation science, to implementation science strategies for better adoption of health information systems to improve health care.