OHDSI MEETINGS THIS WEEK
Gold Standard Phenotype Library Work Group - Tuesday at 10am ET
The Book of OHDSI Work Group Meeting - Tuesday at 11am ET
Zoom URL: https://columbiauniversity.zoom.us/j/258043190
OHDSI Community Call - NO CALL THIS WEEK
Pharmacovigilance Evidence Investigation - Wednesday at 9am ET
ATLAS Work Group - Wednesday at 10am ET
Population-Level Estimation (Western hemisphere) Work Group Call - Thursday at 12pm ET
You can find a full list of upcoming OHDSI meetings here:
2019 European Symposium - Registration is open for second annual European Symposium, set to take place March 29th, 2019 in Rotterdam, Netherlands. For more details, including where to register, check out Peter’s post: European OHDSI Symposium Registration now open!
2019 OHDSI F2F - SAVE THE DATE! The 2019 OHDSI F2F will take place on June 3-4th 2019 at Case Western Reserve University in Cleveland, OH. For more details check out the event page: https://www.ohdsi.org/events/2019-ohdsi-face-to-face/
2019 OHDSI Symposium - SAVE THE DATE! - It’s official! The 2019 OHDSI Symposium will take place on September 15-17th 2019 at the Bethesda North Marriott & Conference Center. The main symposium will be Monday, September 16th with tutorials on September 15th and 17th. For more information, check out the 2019 OHDSI Symposium event page: https://www.ohdsi.org/events/2019-ohdsi-symposium/
2018 OHDSI Symposium Recording - Video records from the main symposium are available here: https://www.ohdsi.org/2018-ohdsi-symposium-videos/
2018 Tutorial Recordings
CDM Tutorial: https://www.ohdsi.org/past-events/2018-tutorials-omop-common-data-model-and-standardized-vocabularies/
OHDSI Ecosystem: https://www.ohdsi.org/past-events/2018-tutorials-overview-of-the-ohdsi-analysis-ecosystem/
Cohort Definition and Phenotyping Tutorial: https://www.ohdsi.org/past-events/cohort-definitionphenotyping-tutorial/
Patient-Level Prediction Tutorial: https://www.ohdsi.org/past-events/patient-level-prediction/
Population-Level Estimation Tutorial: https://www.ohdsi.org/past-events/population-level-estimation/
Data Quality: COMING SOON
There is no greater agony than bearing an untold story inside you.
Maya Angelou COMMUNITY PUBLICATIONS
Comparative Effectiveness of Adjunctive Psychotropic Medications in Patients With Schizophrenia.
TS Stroup, T Gerhard, S Crystal, C Huang, Z Tan, MM Wall, C Mathai and M Olfson,
JAMA psychiatry, Feb 2019 20
People with schizophrenia are commonly treated with psychotropic medications in addition to antipsychotics, but there is little evidence about the comparative effectiveness of these adjunctive treatment strategies.To study the comparative real-world effectiveness of adjunctive psychotropic treatments for patients with schizophrenia.This comparative effectiveness study used US national Medicaid data from January 1, 2001, to December 31, 2010, to examine the outcomes of initiating treatment with an antidepressant, a benzodiazepine, a mood stabilizer, or another antipsychotic among adult outpatients (aged 18-64 years) diagnosed with schizophrenia who were stably treated with a single antipsychotic. Data analysis was performed from January 1, 2017, to June 30, 2018. Multinomial logistic regression models were used to estimate propensity scores to balance covariates across the 4 medication groups. Weighted Cox proportional hazards regression models were used to compare treatment outcomes during 365 days on an intention-to-treat basis.Risk of hospitalization for a mental disorder (primary), emergency department (ED) visits for a mental disorder, and all-cause mortality.The study cohort included 81 921 adult outpatients diagnosed with schizophrenia (mean [SD] age, 40.7 [12.4] years; 37 515 women [45.8%]) who were stably treated with a single antipsychotic and then initiated use of an antidepressant (n = 31 117), a benzodiazepine (n = 11 941), a mood stabilizer (n = 12 849), or another antipsychotic (n = 26 014) (reference treatment). Compared with initiating use of another antipsychotic, initiating use of an antidepressant was associated with a lower risk (hazard ratio [HR], 0.84; 95% CI, 0.80-0.88) of psychiatric hospitalization, whereas initiating use of a benzodiazepine was associated with a higher risk (HR, 1.08; 95% CI, 1.02-1.15); the risk associated with initiating use of a mood stabilizer (HR, 0.98; 95% CI, 0.94-1.03) was not significantly different from initiating use of another antipsychotic. A similar pattern of associations was observed in psychiatric ED visits for initiating use of an antidepressant (HR, 0.92; 95% CI, 0.88-0.96), a benzodiazepine (HR, 1.12; 95% CI, 1.07-1.19), and a mood stabilizer (HR, 0.99; 95% CI, 0.94-1.04). Initiating use of a mood stabilizer was associated with an increased risk of mortality (HR, 1.31; 95% CI, 1.04-1.66).In the treatment of schizophrenia, initiating adjunctive treatment with an antidepressant was associated with reduced risk of psychiatric hospitalization and ED visits compared with initiating use of alternative psychotropic medications. Associations of benzodiazepines and mood stabilizers with poorer outcomes warrant clinical caution and further investigation.
Advancing Clinical Research Through Natural Lanuguage Processing on Electronic Health Records: Traditional Machine Learning Meets Deep Learning
Data Quality in Clinical Research
Preparing next-generation scientists for biomedical big data: artificial intelligence approaches
Criteria2Query: a natural language interface to clinical databases for cohort definition.
C Yuan, PB Ryan, C Ta, Y Guo, Z Li, J Hardin, R Makadia, P Jin, N Shang, T Kang and C Weng,
Journal of the American Medical Informatics Association : JAMIA, Feb 2019 07
Cohort definition is a bottleneck for conducting clinical research and depends on subjective decisions by domain experts. Data-driven cohort definition is appealing but requires substantial knowledge of terminologies and clinical data models. Criteria2Query is a natural language interface that facilitates human-computer collaboration for cohort definition and execution using clinical databases.Criteria2Query uses a hybrid information extraction pipeline combining machine learning and rule-based methods to systematically parse eligibility criteria text, transforms it first into a structured criteria representation and next into sharable and executable clinical data queries represented as SQL queries conforming to the OMOP Common Data Model. Users can interactively review, refine, and execute queries in the ATLAS web application. To test effectiveness, we evaluated 125 criteria across different disease domains from ClinicalTrials.gov and 52 user-entered criteria. We evaluated F1 score and accuracy against 2 domain experts and calculated the average computation time for fully automated query formulation. We conducted an anonymous survey evaluating usability.Criteria2Query achieved 0.795 and 0.805 F1 score for entity recognition and relation extraction, respectively. Accuracies for negation detection, logic detection, entity normalization, and attribute normalization were 0.984, 0.864, 0.514 and 0.793, respectively. Fully automatic query formulation took 1.22 seconds/criterion. More than 80% (11+ of 13) of users would use Criteria2Query in their future cohort definition tasks.We contribute a novel natural language interface to clinical databases. It is open source and supports fully automated and interactive modes for autonomous data-driven cohort definition by researchers with minimal human effort. We demonstrate its promising user friendliness and usability.
Healthcare quality-improvement and measurement strategies and its challenges ahead.
U Iqbal, A Humayun and YJ Li,
International journal for quality in health care : journal of the International Society for Quality in Health Care, Feb 2019 01