OHDSI MEETINGS THIS WEEK
OHDSI Community Call - Tuesday at 12pm EThttps://meetings.webex.com/collabs/#/meetings/detail?uuid=M59X2V1U61WC9ASID2Z5N3UT95-D1JL&rnd=811649.98682
NLP working group - Wednesday at 2pm EThttps://global.gotomeeting.com/join/707196421
Patient-level prediction (Eastern hemisphere) workgroup meeting - Wednesday at 3pm Hong Kong/Taiwan timehttps://global.gotomeeting.com/join/9729176611
Population-Level Estimation (Western hemisphere) workgroup meeting - Thursday at 12pm EThttps://meetings.webex.com/collabs/#/meetings/detail?uuid=M3T9BZV9RSB6YNDM8WDDZMI19D-D1JL&occurrenceID=1501776000000
Hadoop WG meeting - Friday at 11am EThttp://cloudera.webex.com/meet/sdolley
GIS working group meeting - Monday (September 18th) at 10am EThttps://tufts.webex.com/mw3200/mywebex/default.do?service=1&siteurl=tufts&nomenu=true&main_url=%2Fmc3200%2Fe.do%3Fsiteurl%3Dtufts%26AT%3DMI%26EventID%3D562301137%26UID%3D528546812%26Host%3DQUhTSwAAAAT_EHuT3Ok-zHVhY1-kVGh78TH62dPsFk0x99qz1E9039sh_Eiepw8CoZeIF2SfnopQ8oAZaLN9PkzIZovRf2kV0%26FrameSet%3D2%26MTID%3Dm243d8e9a9c6c2d42d5182aeb5d30efdb1
ANNOUNCEMENTS
2017 OHDSI Symposium Registration - Registration for this year’s symposium is filling up quickly. If you haven’t registered yet, we recommend you do so soon! Register here: https://www.ohdsi.org/symposium-registration/
Tutorial Registration - Tutorial registration is now closed. Selected participants will be notified this week that they have been accepted into their respective tutorial.
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 - Leading up to the symposium, 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.
I am so clever that sometimes I don’t understand a single word of what I am saying.
COMMUNITY PUBLICATIONS
Two new computational methods for data analysis: A social network analysis-based classifier and the GEEORD SAS module.
Benzodiazepines use and breast cancer risk: A population-based study and gene expression profiling evidence.
U Iqbal, TH Chang, PA Nguyen, S Syed-Abdul, HC Yang, CW Huang, S Atique, WC Yang, M Moldovan, WS Jian, MH Hsu, Y Yen and YJ Li,
Journal of biomedical informatics , 2017 10
The aim of this study was to investigate whether long-term use of Benzodiazepines (BZDs) is associated with breast cancer risk through the combination of population-based observational and gene expression profiling evidence. We conducted a population-based case-control study by using 1998 to 2009year Taiwan National Health Insurance Research Database and investigated the association between BZDs use and breast cancer risk. We selected subjects age of >20years old and six eligible controls matched for age, sex and the index date (i.e., free of any cancer at the case diagnosis date) by using propensity scores. A bioinformatics analysis approach was also performed for the identification of oncogenesis effects of BZDs on breast cancer. We used breast cancer gene expression data from the Cancer Genome Atlas and perturbagen signatures of BZDs from the Library of Integrated Cellular Signatures database in order to identify the oncogenesis effects of BZDs on breast cancer. We found evidence of increased breast cancer risk for diazepam (OR, 1.16; 95%CI, 0.95-1.42; connectivity score [CS], 0.3016), zolpidem (OR, 1.11; 95%CI, 0.95-1.30; CS, 0.2738), but not for lorazepam (OR, 1.04; 95%CI, 0.89-1.23; CS, -0.2952) consistently in both methods. The finding for alparazolam was contradictory from the two methods. Diazepam and zolpidem trends showed association, although not statistically significant, with breast cancer risk in both epidemiological and bioinformatics analyses outcomes. The methodological value of our study is in introducing the way of combining epidemiological and bioinformatics approaches in order to answer a common scientific question. Combining the two approaches would be a substantial step towards uncovering, validation and further application of previously unknown scientific knowledge to the emerging field of precision medicine informatics.
From Big Data to Smart Data for Pharmacovigilance: The Role of Healthcare Databases and Other Emerging Sources.
G Trifirò, J Sultana and A Bate,
Drug safety , Feb 2018
In the last decade 'big data' has become a buzzword used in several industrial sectors, including but not limited to telephony, finance and healthcare. Despite its popularity, it is not always clear what big data refers to exactly. Big data has become a very popular topic in healthcare, where the term primarily refers to the vast and growing volumes of computerized medical information available in the form of electronic health records, administrative or health claims data, disease and drug monitoring registries and so on. This kind of data is generally collected routinely during administrative processes and clinical practice by different healthcare professionals: from doctors recording their patients' medical history, drug prescriptions or medical claims to pharmacists registering dispensed prescriptions. For a long time, this data accumulated without its value being fully recognized and leveraged. Today big data has an important place in healthcare, including in pharmacovigilance. The expanding role of big data in pharmacovigilance includes signal detection, substantiation and validation of drug or vaccine safety signals, and increasingly new sources of information such as social media are also being considered. The aim of the present paper is to discuss the uses of big data for drug safety post-marketing assessment.
Discovering associations between adverse drug events using pattern structures and ontologies.
G Personeni, E Bresso, MD Devignes, M Dumontier, M Smaïl-Tabbone and A Coulet,
Journal of biomedical semantics , Aug 22 2017
Patient data, such as electronic health records or adverse event reporting systems, constitute an essential resource for studying Adverse Drug Events (ADEs). We explore an original approach to identify frequently associated ADEs in subgroups of patients.Because ADEs have complex manifestations, we use formal concept analysis and its pattern structures, a mathematical framework that allows generalization using domain knowledge formalized in medical ontologies. Results obtained with three different settings and two different datasets show that this approach is flexible and allows extraction of association rules at various levels of generalization.The chosen approach permits an expressive representation of a patient ADEs. Extracted association rules point to distinct ADEs that occur in a same group of patients, and could serve as a basis for a recommandation system. The proposed representation is flexible and can be extended to make use of additional ontologies and various patient records.
Procedure Prediction from Symbolic Electronic Health Records via Time Intervals Analytics.
R Moskovitch, F Polubriaginof, A Weiss, P Ryan and N Tatonetti,
Journal of biomedical informatics , Nov 2017
Prediction of medical events, such as clinical procedures, is essential for preventing disease, understanding disease mechanism, and increasing patient quality of care. Although longitudinal clinical data from Electronic Health Records provides opportunities to develop predictive models, the use of these data faces significant challenges. Primarily, while the data are longitudinal and represent thousands of conceptual events having duration, they are also sparse, complicating the application of traditional analysis approaches. Furthermore, the framework presented here takes advantage of the events duration and gaps. International standards for electronic healthcare data represent data elements, such as procedures, conditions, and drug exposures, using eras, or time intervals. Such eras contain both an event and a duration and enable the application of time intervals mining - a relatively new subfield of data mining. In this study, we present Maitreya, a framework for time intervals analytics in longitudinal clinical data. Maitreya discovers frequent time intervals related patterns (TIRPs), which we use as prognostic markers for modelling clinical events. We introduce three novel TIRP metrics that are normalized versions of the horizontal-support, that represents the number of TIRP instances per patient. We evaluate Maitreya on 28 frequent and clinically important procedures, using the three novel TIRP representation metrics in comparison to no temporal representation and previous TIRPs metrics. We also evaluate the epsilon value that makes Allen's relations more flexible with several settings of 30, 60, 90 and 180days in comparison to the default zero. For twenty-two of these procedures, the use of temporal patterns as predictors was superior to non-temporal features, and the use of the vertically normalized horizontal support metric to represent TIRPs as features was most effective. The use of the epsilon value with thirty days was slightly better than the zero.
Impact of the black triangle label on prescribing of new drugs in the United Kingdom: lessons for the United States at a time of deregulationhttp://onlinelibrary.wiley.com/doi/10.1002/pds.4304/full
Offline and online data assimilation for real-time blood glucose forecasting in type 2 diabetes
ECG-ViEW II, a freely accessible electrocardiogram database.
YG Kim, D Shin, MY Park, S Lee, MS Jeon, D Yoon and RW Park,
PloS one , 2017
The Electrocardiogram Vigilance with Electronic data Warehouse II (ECG-ViEW II) is a large, single-center database comprising numeric parameter data of the surface electrocardiograms of all patients who underwent testing from 1 June 1994 to 31 July 2013. The electrocardiographic data include the test date, clinical department, RR interval, PR interval, QRS duration, QT interval, QTc interval, P axis, QRS axis, and T axis. These data are connected with patient age, sex, ethnicity, comorbidities, age-adjusted Charlson comorbidity index, prescribed drugs, and electrolyte levels. This longitudinal observational database contains 979,273 electrocardiograms from 461,178 patients over a 19-year study period. This database can provide an opportunity to study electrocardiographic changes caused by medications, disease, or other demographic variables. ECG-ViEW II is freely available at http://www.ecgview.org.
