Browsing by Author "Abedtash, Hamed"

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    An Interactive User Interface for Drug Labeling to Improve Readability and Decision-Making
    (AMIA, 2015) Abedtash, Hamed; Duke, Jon D.; Department of BioHealth Informatics, School of Informatics and Computing
    FDA-approved prescribing information (also known as product labeling or labels) contain critical safety information for health care professionals. Drug labels have often been criticized, however, for being overly complex, difficult to read, and rife with overwarning, leading to high cognitive load. In this project, we aimed to improve the usability of drug labels by increasing the ‘signal-to-noise ratio’ and providing meaningful information to care providers based on patient-specific comorbidities and concomitant medications. In the current paper, we describe the design process and resulting web application, known as myDrugLabel. Using the Structured Product Label documents as a base, we describe the process of label personalization, readability improvements, and integration of diverse evidence sources, including the medical literature from PubMed, pharmacovigilance reports from FDA adverse event reporting system (FAERS), and social media signals directly into the label.
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    An interoperable electronic medical record-based platform for personalized predictive analytics
    (2017-05-31) Abedtash, Hamed; Jones, Josette F.; Duke, Jon D.; Wessel, Jennifer; Li, Xiaochun; Holden, Richard J.
    Precision medicine refers to the delivering of customized treatment to patients based on their individual characteristics, and aims to reduce adverse events, improve diagnostic methods, and enhance the efficacy of therapies. Among efforts to achieve the goals of precision medicine, researchers have used observational data for developing predictive modeling to best predict health outcomes according to patients’ variables. Although numerous predictive models have been reported in the literature, not all models present high prediction power, and as the result, not all models may reach clinical settings to help healthcare professionals make clinical decisions at the point-of-care. The lack of generalizability stems from the fact that no comprehensive medical data repository exists that has the information of all patients in the target population. Even if the patients’ records were available from other sources, the datasets may need further processing prior to data analysis due to differences in the structure of databases and the coding systems used to record concepts. This project intends to fill the gap by introducing an interoperable solution that receives patient electronic health records via Health Level Seven (HL7) messaging standard from other data sources, transforms the records to observational medical outcomes partnership (OMOP) common data model (CDM) for population health research, and applies predictive models on patient data to make predictions about health outcomes. This project comprises of three studies. The first study introduces CCD-TOOMOP parser, and evaluates OMOP CDM to accommodate patient data transferred by HL7 consolidated continuity of care documents (CCDs). The second study explores how to adopt predictive model markup language (PMML) for standardizing dissemination of OMOP-based predictive models. Finally, the third study introduces Personalized Health Risk Scoring Tool (PHRST), a pilot, interoperable OMOP-based model scoring tool that processes the embedded models and generates risk scores in a real-time manner. The final product addresses objectives of precision medicine, and has the potentials to not only be employed at the point-of-care to deliver individualized treatment to patients, but also can contribute to health outcome research by easing collecting clinical outcomes across diverse medical centers independent of system specifications.
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    A Novel Pipeline for Targeting Breast Cancer Patients on Twitter for Clinical Trial Recruitment
    (Office of the Vice Chancellor for Research, IUPUI, 2016-04-08) Sligh, Jon; Abedtash, Hamed; Yang, Mengye; Zhang, Enming; Jones, Josette
    Background and Preliminary Exploration: Breast cancer is the leading form of cancer in women, estimated to reach the incidence rate of 246,660 in 2016 in the US population. Scientist have developed new therapies for mitigating the disease and side effects in recent years through conducting randomized clinical trials as the gold standard clinical research method. However, recruiting individuals into clinical trials including breast cancer patients has remained a significant challenge. Our preliminary analysis on ClinicalTrial.gov registry showed that the majority of terminated clinical trials were due to recruitment challenges. Out of 525 terminated trials on breast cancer patients registered in the database, 230 (43.8%) of the terminations happened due to low or slow accrual, 34 (6.5%) due to lack of funding, and 31 (5.9%) due to toxicity concerns. Objectives: In this study, we developed and assess a scalable framework to identify Twitter users who have breast cancer based on personal health mentions on Twitter. In fact, we are looking for “fingerprints” of patients’ health status on Twitter, a microblogging social networking service. This method could provide a new avenue for contacting potential study candidates for recruitment. Methods: We analyzed the tweets of users who were following at least one of the top 40 twitter accounts where breast cancer patients gather. The rationale behind this approach is that cancer patients are following certain Twitter accounts to access support from other patients, doctors, or healthcare institutions. Consequently, these top twitter accounts provide a central point in which to find actual patients with breast cancer. We retrieved users’ tweets from Twitter API, and processed through the framework to match cancer relevant words and phrases individually and in combinations (caner, benign, malignant, etc.), possessive terms (I, my, has, have, etc.), and supporting attributes (mass, tumor, hair loss, etc.) to determine if the user has been diagnosed with cancer. The performance of the pipeline was measured in terms of sensitivity and specificity of detecting actual breast cancer patients. Results: We retrieved 25,870,106 tweets of 40 cancer community followers on Twitter. After excluding “retweets” and non-related breast cancer messages, we selected 81,429 tweets for further processing. The developed text processing pipeline could find total of 462 tweets based on the predefined sets of rules, representing 218 unique users. Our new method of Twitter data retrieval and text processing could identify breast cancer patients with remarkable sensitivity of 88.7% and specificity of 91.0%.
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    Systematic review of the effectiveness of health-related behavioral interventions using portable activity sensing devices (PASDs)
    (Oxford University Press, 2017-09-01) Abedtash, Hamed; Holden, Richard J.; BioHealth Informatics, School of Informatics and Computing
    Background: Portable activity sensing devices (PASDs) have received significant interest as tools for objectively measuring activity-related parameters and promoting health-related outcomes. Studies of PASDs suggest the potential value of integrating them with behavioral interventions to improve intermediate and downstream clinical outcomes. Objectives: This systematic review describes and evaluates evidence from controlled studies of interventions using PASDs on their effectiveness in health-related outcomes. Study quality was also assessed. Methods: A systematic literature search was performed of MEDLINE, Cochrane Central Register of Controlled Trials, PsycINFO, EMBASE, and CINAHL databases. We included English-language papers of controlled trials through 2015 reporting the effectiveness of PASDs in improving health-related outcomes in any population. We extracted and analyzed data on study characteristics including design, target population, interventions, and findings. Results: Seventeen trials met the inclusion criteria from a total of 9553 unique records. Study objectives varied greatly, but most sought to increase physical activity. Studies with a "passive" intervention arm using a PASD with minimal behavioral support generally did not demonstrate effectiveness in improving health-related outcomes. Interventions integrating PASDs with multiple behavioral change techniques were more likely to be effective, particularly for intermediate outcomes such as physical activity and weight loss. Trials had small sample sizes but were generally free of bias, except for blinding and selection bias. Conclusion: There is insufficient evidence to draw a conclusion about the general health-related benefits of PASD interventions. PASD interventions may improve intermediate outcomes when coupled with multiple behavioral change techniques. Devices alone or with minimal behavioral change support are insufficient to change health-related outcomes.