OHDSI MEETINGS THIS WEEK
Patient-level prediction (Eastern hemisphere) workgroup meeting - Wednesday at 3pm Hong Kong timehttps://global.gotomeeting.com/join/9729176611
Population-Level Estimation (Western hemisphere) WG Meeting - Wednesday at 12pm EThttps://meetings.webex.com/collabs/#/meetings/detail?uuid=M6WE9AOKFETH2VEFPVCZWWBIT0-D1JL&rnd=479368.763622
Architecture WG meeting - Thursday at 1pm EThttps://jjconferencing.webex.com/mw3000/mywebex/default.do?service=1&main_url=%2Fmc3000%2Fe.do%3Fsiteurl%3Djjconferencing%26AT%3DMI%26EventID%3D283835502%26MTID%3Dmb7e839a762fbdaab0608f27500679223%26Host%3DQUhTSwAAAASTOYBxx2KWYTIL7ZSgHgHJoIHctjgxp8k5mgqAEPo2a1ESa12-8hjAVWbZznuDt96ugkT31G8sY5iSWLRwSwhI0%26FrameSet%3D2&siteurl=jjconferencing&nomenu=true
Hadoop WG meeting - Friday at 11am EThttp://cloudera.webex.com/meet/sdolley
ANNOUNCEMENTS
Happy Independance Day! In the name of freedom, there will be no OHDSI community call or CDM workgroup meeting this week. The next CDM workgroup meeting will be next Tuesday, July 11th at 1pm.
Hadoop Hack-A-Thon - Our first Hadoop Hack-A-Thon was a great success! Join in next week’s community call to hear all about it. In the meantime, check-out photos from the event:https://www.ohdsi.org/photos-from-2017-hadoop-hack-a-thon/
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
There is no great genius without some touch of madness.
COMMUNITY PUBLICATIONS
Predicting Biomedical Metadata in CEDAR: a Study of Gene Expression Omnibus (GEO)
A crucial and limiting factor in data reuse is the lack of accurate, structured, and complete descriptions of data, known as metadata. Towards improvi…
vigiRank for statistical signal detection in pharmacovigilance: First results from prospective real-world usehttp://onlinelibrary.wiley.com/doi/10.1002/pds.4247/full
Personalized glucose forecasting for type 2 diabetes using data assimilation.
DJ Albers, M Levine, B Gluckman, H Ginsberg, G Hripcsak and L Mamykina,
PLoS computational biology , 2017 04
Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual's blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges.
Clinical Research Informatics for Big Data and Precision Medicine.
C Weng and MG Kahn,
Yearbook of medical informatics , Nov 2016 10
To reflect on the notable events and significant developments in Clinical Research Informatics (CRI) in the year of 2015 and discuss near-term trends impacting CRI.We selected key publications that highlight not only important recent advances in CRI but also notable events likely to have significant impact on CRI activities over the next few years or longer, and consulted the discussions in relevant scientific communities and an online living textbook for modern clinical trials. We also related the new concepts with old problems to improve the continuity of CRI research.The highlights in CRI in 2015 include the growing adoption of electronic health records (EHR), the rapid development of regional, national, and global clinical data research networks for using EHR data to integrate scalable clinical research with clinical care and generate robust medical evidence. Data quality, integration, and fusion, data access by researchers, study transparency, results reproducibility, and infrastructure sustainability are persistent challenges.The advances in Big Data Analytics and Internet technologies together with the engagement of citizens in sciences are shaping the global clinical research enterprise, which is getting more open and increasingly stakeholder-centered, where stakeholders include patients, clinicians, researchers, and sponsors.
