OHDSI MEETINGS THIS WEEK CDM/Vocabulary Work Group Meeting - Tuesday from 1-2pm ET
Patient Visualization Work Group Meeting - Tuesday at 2pm ET
ANNOUNCEMENTS FIRST OHDSI NETWORK STUDY RELEASED: http://www.pnas.org/content/early/2016/06/01/1510502113.full.pdf
OHDSI Symposium 2016 - REGISTER NOW! http://www.ohdsi.org/events/ohdsi-symposium-2016/
OHDSI Symposium 2016 - Call for participation June 22nd 2016 http://www.ohdsi.org/ohdsi-symposium-2016-call-for-participation/
COMMUNITY PUBLICATIONS Characterizing treatment pathways at scale using the OHDSI network http://www.pnas.org/content/early/2016/06/01/1510502113.full.pdf
Learning statistical models of phenotypes using noisy labeled training data
V Agarwal, T Podchiyska, JM Banda, V Goel, TI Leung, EP Minty, TE Sweeney, E Gyang and NH Shah,
Journal of the American Medical Informatics Association : JAMIA , 2016 11
Traditionally, patient groups with a phenotype are selected through rule-based definitions whose creation and validation are time-consuming. Machine learning approaches to electronic phenotyping are limited by the paucity of labeled training datasets. We demonstrate the feasibility of utilizing semi-automatically labeled training sets to create phenotype models via machine learning, using a comprehensive representation of the patient medical record.We use a list of keywords specific to the phenotype of interest to generate noisy labeled training data. We train L1 penalized logistic regression models for a chronic and an acute disease and evaluate the performance of the models against a gold standard.Our models for Type 2 diabetes mellitus and myocardial infarction achieve precision and accuracy of 0.90, 0.89, and 0.86, 0.89, respectively. Local implementations of the previously validated rule-based definitions for Type 2 diabetes mellitus and myocardial infarction achieve precision and accuracy of 0.96, 0.92 and 0.84, 0.87, respectively.We have demonstrated feasibility of learning phenotype models using imperfectly labeled data for a chronic and acute phenotype. Further research in feature engineering and in specification of the keyword list can improve the performance of the models and the scalability of the approach.Our method provides an alternative to manual labeling for creating training sets for statistical models of phenotypes. Such an approach can accelerate research with large observational healthcare datasets and may also be used to create local phenotype models.
Hypothyroidism risk compared among nine common bipolar disorder therapies in a large US cohort
CG Lambert, AJ Mazurie, NR Lauve, NG Hurwitz, SS Young, RL Obenchain, NW Hengartner, DJ Perkins, M Tohen and B Kerner,
Bipolar disorders , 2016 05
Thyroid abnormalities in patients with bipolar disorder (BD) have been linked to lithium treatment for decades, yet other drugs have been less well studied. Our objective was to compare hypothyroidism risk for lithium versus the anticonvulsants and second-generation antipsychotics commonly prescribed for BD.Administrative claims data on 24,574 patients with BD were analyzed with competing risk survival analysis. Inclusion criteria were (i) one year of no prior hypothyroid diagnosis nor BD drug treatment, (ii) followed by at least one thyroid test during BD monotherapy on lithium carbonate, mood-stabilizing anticonvulsants (lamotrigine, valproate, oxcarbazepine, or carbamazepine) or antipsychotics (aripiprazole, olanzapine, risperidone, or quetiapine). The outcome was cumulative incidence of hypothyroidism per drug, in the presence of the competing risk of ending monotherapy, adjusted for age, sex, physician visits, and thyroid tests.Adjusting for covariates, the four-year cumulative risk of hypothyroidism for lithium (8.8%) was 1.39-fold that of the lowest risk therapy, oxcarbazepine (6.3%). Lithium was non-statistically significantly different from quetiapine. While lithium conferred a higher risk when compared to all other treatments combined as a group, hypothyroidism risk error bars overlapped for all drugs. Treatment (p = 3.86e-3), age (p = 6.91e-10), sex (p = 3.93e-7), and thyroid testing (p = 2.79e-87) affected risk. Patients taking lithium were tested for hypothyroidism 2.26-3.05 times more frequently than those on other treatments.Thyroid abnormalities occur frequently in patients with BD regardless of treatment. Therefore, patients should be regularly tested for clinical or subclinical thyroid abnormalities on all therapies and treated as indicated to prevent adverse effects of hormone imbalances on mood.
