OHDSI MEETINGS THIS WEEK
CDM and vocabulary development workgroup meeting - Tuesday at 1pm EThttps://imshealth.webex.com/mw3000/mywebex/default.do?siteurl=imshealth
Population-Level Estimation (Western hemisphere) workgroup meeting - Thursday at 12pm EThttps://meetings.webex.com/collabs/#/meetings/detail?uuid=M3T9BZV9RSB6YNDM8WDDZMI19D-D1JL&occurrenceID=1501776000000
Patient-level prediction (Eastern hemisphere) workgroup meeting - Wednesday at 3pm Hong Kong/Taiwan timehttps://global.gotomeeting.com/join/9729176611
Hadoop WG meeting - Friday at 11am EThttp://cloudera.webex.com/meet/sdolley
ANNOUNCEMENTS
OHDSI in China The China WG will be meeting on August 21st in Hangzhou, China (during MedInfo). More details: http://www.ohdsi.org/web/wiki/doku.php?id=projects:workgroups:china-wg
2017 OHDSI Symposium Registration - Registration for the 2017 OHDSI symposium is now open! Please register here: https://www.ohdsi.org/symposium-registration/
2017 OHDSI Tutorials Registration - Registration for OHDSI tutorials is now open! Information about each tutorial, including topics covered, faculty and prerequisites can be found here: https://www.ohdsi.org/about-event-tutorials/ https://www.ohdsi.org/tutorial-registration/
2017 OHDSI Symposium Accommodation - This year’s symposium will take place at the Bethesda North Marriott, just outside Washington DC. If you’d like to stay at the Marriott during the symposium we suggest booking your stay as soon as possible. October is a busy month in DC.http://www.marriott.com/hotels/travel/wasbn-bethesda-north-marriott-hotel-and-conference-center/ @krfeeney ’s forum post: 2017 OHDSI Symposium - Resources for hotel bookings
Call for Sponsorship - To fund the 2017 symposium we need to raise $200K to cover operating costs of venue rental, lunch, audio/visuals and recordings fees. We’re reaching out to the entire community for sponsors who want to support this important event and allow us to build upon the success of past symposia. More details: OHDSI Symposium 2017 - call for sponsorship
Updating OHDSI.org - Over the next couple months we’ll be updating collaborator profiles and our data network list. If you have updates you’d like made to your OHDSI profile, or have updated information about your database, please email beaton@ohdsi.org with the changes.
To enjoy freedom we have to control ourselves.
COMMUNITY PUBLICATIONS
Birth/birth-death processes and their computable transition probabilities with biological applications.
LST Ho, J Xu, FW Crawford, VN Minin and MA Suchard,
Journal of mathematical biology , Mar 2018
Birth-death processes track the size of a univariate population, but many biological systems involve interaction between populations, necessitating models for two or more populations simultaneously. A lack of efficient methods for evaluating finite-time transition probabilities of bivariate processes, however, has restricted statistical inference in these models. Researchers rely on computationally expensive methods such as matrix exponentiation or Monte Carlo approximation, restricting likelihood-based inference to small systems, or indirect methods such as approximate Bayesian computation. In this paper, we introduce the birth/birth-death process, a tractable bivariate extension of the birth-death process, where rates are allowed to be nonlinear. We develop an efficient algorithm to calculate its transition probabilities using a continued fraction representation of their Laplace transforms. Next, we identify several exemplary models arising in molecular epidemiology, macro-parasite evolution, and infectious disease modeling that fall within this class, and demonstrate advantages of our proposed method over existing approaches to inference in these models. Notably, the ubiquitous stochastic susceptible-infectious-removed (SIR) model falls within this class, and we emphasize that computable transition probabilities newly enable direct inference of parameters in the SIR model. We also propose a very fast method for approximating the transition probabilities under the SIR model via a novel branching process simplification, and compare it to the continued fraction representation method with application to the 17th century plague in Eyam. Although the two methods produce similar maximum a posteriori estimates, the branching process approximation fails to capture the correlation structure in the joint posterior distribution.
The novel use of an Extreme learning machines for clinical decision support systems.
Identifying Cases of Type 2 Diabetes in Heterogeneous Data Sources: Strategy from the EMIF Project.
G Roberto, I Leal, N Sattar, AK Loomis, P Avillach, P Egger, R van Wijngaarden, D Ansell, S Reisberg, ML Tammesoo, H Alavere, A Pasqua, L Pedersen, J Cunningham, L Tramontan, MA Mayer, R Herings, P Coloma, F Lapi, M Sturkenboom, J van der Lei, MJ Schuemie, P Rijnbeek and R Gini,
PloS one , 2016
Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sources (PCDs), three record linkage of administrative and/or registry data sources (RLDs), one hospital and one biobank. Overall, data from 12 million subjects from six European countries were extracted. Based on a shared event definition, sixteeen standard algorithms (components) useful to identify T2DM cases were generated through a top-down/bottom-up iterative approach. Each component was based on one single data domain among diagnoses, drugs, diagnostic test utilization and laboratory results. Diagnoses-based components were subclassified considering the healthcare setting (primary, secondary, inpatient care). The Unified Medical Language System was used for semantic harmonization within data domains. Individual components were extracted and proportion of population identified was compared across data sources. Drug-based components performed similarly in RLDs and PCDs, unlike diagnoses-based components. Using components as building blocks, logical combinations with AND, OR, AND NOT were tested and local experts recommended their preferred data source-tailored combination. The population identified per data sources by resulting algorithms varied from 3.5% to 15.7%, however, age-specific results were fairly comparable. The impact of individual components was assessed: diagnoses-based components identified the majority of cases in PCDs (93-100%), while drug-based components were the main contributors in RLDs (81-100%). The proposed data derivation procedure allowed the generation of data source-tailored case-finding algorithms in a standardized fashion, facilitated transparent documentation of the process and benchmarking of data sources, and provided bases for interpretation of possible inter-data source inconsistency of findings in future studies.
