OHDSI MEETINGS THIS WEEK
OHDSI Community Call - Tuesday at 12pm EThttps://meetings.webex.com/collabs/#/meetings/detail?uuid=M59X2V1U61WC9ASID2Z5N3UT95-D1JL&rnd=811649.9868221112121
Architecture Working Group - Thursday at 10am EThttps://jjconferencing.webex.com/mw3100/mywebex/default.do?service=1&siteurl=jjconferencing&nomenu=true&main_url=%2Fmc3100%2Fe.do%3Fsiteurl%3Djjconferencing%26AT%3DMI%26EventID%3D610982452%26UID%3D501476547%26Host%3DQUhTSwAAAAQu4P1o9qm71JJ1Zj4-uvZbjQttsCinu71JCRxBAHAXnzjjRAiTspTzU9ojLmjMF4CcTBWw4zn1dqYPTWu5vJ9_0%26FrameSet%3D2%26MTID%3Dm3e1ceeca56f1e94c9fcf1ae98c10e02e1
GIS working group meeting - Next Monday (July 23rd) at 10am ET
Simple, modern video meetings for the global workforce. Join from anywhere, including your desktop, browser, mobile device, or video room device.
ANNOUNCEMENTS
2018 OHDSI Symposium - REGISTER NOW https://www.ohdsi.org/symposium-registration-2/
2018 OHDSI Symposium - TUTORIAL REGISTRATION OPEN https://www.ohdsi.org/tutorial-workshops/ https://www.ohdsi.org/tutorial-registration-2/
Intro tutorials are being offered free of cost, however registration does not guarantee a seat in the tutorial. When you register, you will be placed on the tutorial wait-list. The final participant list will be determined by the tutorial faculty.
Advanced tutorials also offer the free wait-list registration. In addition, we are also offering a limited number of paid tickets ($318.17) which will guarantee your seat in the tutorial.
2018 OHDSI Symposium - CALL FOR PARTICIPATION https://www.ohdsi.org/collaborator-showcase/ Abstract Submissions Due: 5pm on Thursday, August 7, 2018
Insanity is hereditary; you get it from your children.
COMMUNITY PUBLICATIONS
Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks.
X Pan, P Rijnbeek, J Yan and HB Shen,
BMC genomics , Jul 2018 03
RNA regulation is significantly dependent on its binding protein partner, known as the RNA-binding proteins (RBPs). Unfortunately, the binding preferences for most RBPs are still not well characterized. Interdependencies between sequence and secondary structure specificities is challenging for both predicting RBP binding sites and accurate sequence and structure motifs detection.In this study, we propose a deep learning-based method, iDeepS, to simultaneously identify the binding sequence and structure motifs from RNA sequences using convolutional neural networks (CNNs) and a bidirectional long short term memory network (BLSTM). We first perform one-hot encoding for both the sequence and predicted secondary structure, to enable subsequent convolution operations. To reveal the hidden binding knowledge from the observed sequences, the CNNs are applied to learn the abstract features. Considering the close relationship between sequence and predicted structures, we use the BLSTM to capture possible long range dependencies between binding sequence and structure motifs identified by the CNNs. Finally, the learned weighted representations are fed into a classification layer to predict the RBP binding sites. We evaluated iDeepS on verified RBP binding sites derived from large-scale representative CLIP-seq datasets. The results demonstrate that iDeepS can reliably predict the RBP binding sites on RNAs, and outperforms the state-of-the-art methods. An important advantage compared to other methods is that iDeepS can automatically extract both binding sequence and structure motifs, which will improve our understanding of the mechanisms of binding specificities of RBPs.Our study shows that the iDeepS method identifies the sequence and structure motifs to accurately predict RBP binding sites. iDeepS is available at https://github.com/xypan1232/iDeepS .
OpenPVSignal: Advancing Information Search, Sharing and Reuse on Pharmacovigilance Signals via FAIR Principles and Semantic Web Technologies.
P Natsiavas, RD Boyce, MC Jaulent and V Koutkias,
Frontiers in pharmacology , 2018
Signal detection and management is a key activity in pharmacovigilance (PV). When a new PV signal is identified, the respective information is publicly communicated in the form of periodic newsletters or reports by organizations that monitor and investigate PV-related information (such as the World Health Organization and national PV centers). However, this type of communication does not allow for systematic access, discovery and explicit data interlinking and, therefore, does not facilitate automated data sharing and reuse. In this paper, we present OpenPVSignal, a novel ontology aiming to support the semantic enrichment and rigorous communication of PV signal information in a systematic way, focusing on two key aspects: (a) publishing signal information according to the FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles, and (b) exploiting automatic reasoning capabilities upon the interlinked PV signal report data. OpenPVSignal is developed as a reusable, extendable and machine-understandable model based on Semantic Web standards/recommendations. In particular, it can be used to model PV signal report data focusing on: (a) heterogeneous data interlinking, (b) semantic and syntactic interoperability, (c) provenance tracking and (d) knowledge expressiveness. OpenPVSignal is built upon widely-accepted semantic models, namely, the provenance ontology (PROV-O), the Micropublications semantic model, the Web Annotation Data Model (WADM), the Ontology of Adverse Events (OAE) and the Time ontology. To this end, we describe the design of OpenPVSignal and demonstrate its applicability as well as the reasoning capabilities enabled by its use. We also provide an evaluation of the model against the FAIR data principles. The applicability of OpenPVSignal is demonstrated by using PV signal information published in: (a) the World Health Organization's Pharmaceuticals Newsletter, (b) the Netherlands Pharmacovigilance Centre Lareb Web site and (c) the U.S. Food and Drug Administration (FDA) Drug Safety Communications, also available on the FDA Web site.
A positive legacy of trauma? A study on the impact of natural disasters on medical utilization.
U Iqbal, YJ Li, KP Tang, HC Chien, YT Yang and YE Hsu,
International journal for quality in health care : journal of the International Society for Quality in Health Care , Jul 2018 04
The impact of natural disasters on medical utilization is largely unknown and often overlooked how it affects bereaving and non-bereaving survivors. The aim of this study is to determine the medical utilization between both survivor groups and long-term effects.A 10-year 1999-2009 population-based retrospective study by using the National Health Insurance claim database and the Household Registration database from the Department of Health, Executive Yuan, Taiwan.Taiwan 1999 Chi-Chi earthquake-affected areas.A total of 49 834 individuals which included 1183 bereaving survivors and 48 651 non-bereaving earthquake survivors.None.Medical utilization of bereaving and non-bereaving survivors.The results showed that bereaving survivors had significantly more outpatient visits before the earthquake, within 3-month period and 1 year after earthquake (odds ratio (OR) = 1.11, 1.16 and 1.08). However, after 1 year after earthquake their outpatient visits were not significantly different from non-bereaving, and even significantly less in some years. Inpatient visits of bereaving survivors had similar trend to outpatient visits, i.e. visits were more both before earthquake and within 3-month period after earthquake (OR = 1.59 and 1.89), however, they were not significantly higher than non-bereaving survivors for the following years of the study.Our study reveals that compared to non-bereaving survivors, bereaving survivors slightly had higher medical utilization in the beginning stage of earthquake, i.e. for the first 3-month period or 1 year after earthquake. However, there were no differences between these two groups in medical utilization including outpatient and inpatient visits in long run.
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.
