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You searched for +publisher:"Vanderbilt University" +contributor:("Jeremy Warner"). Showing records 1 – 3 of 3 total matches.

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Vanderbilt University

1. Carroll, Robert James. Defining Phenotypes, Predicting Drug Response, and Discovering Genetic Associations in the Electronic Health Record with Applications in Rheumatoid Arthritis.

Degree: PhD, Biomedical Informatics, 2014, Vanderbilt University

Electronic Health Records (EHRs) allow for the digital capture of patient information and have proven to be a valuable tool for patient treatment. In this dissertation, I explore reuse of EHR data for clinical and genomic research with a focus on rheumatoid arthritis (RA). RA is a chronic autoimmune disorder that primarily affects joints with swelling, stiffness, and pain, and if left untreated can lead to permanent joint damage. Phenome wide association studies (PheWAS) leverage the breadth of codified diagnostic information about patients in the EHR to find disease associations. A package for the R statistical language is presented here that includes the tools needed to perform EHR-based or observational trial PheWAS, from ICD-9 code translation to association testing and meta-analysis. It includes a versatile plotting system for phenotype related information following the Manhattan plot paradigm. This methodology is applied in conjunction with genetic risk scores (GRS) to assess pleiotropy and shared genetic risk among phenotypes. Investigations of 99 known risk variants for RA and three formulations of GRS show that the GRS is more specific to RA than the individual single nucleotide polymorphisms, but the GRSs had clinically interesting associations with hypothyroidism. Presented next is the development of an algorithm to retrospectively identify drug response to etanercept in the EHR. Using chart reviews and a variety of input data including billing codes, processed free text, and medication entries, a support vector machine and random forest classifier were created that can discriminate between drug responders and non-responders with an area under the receiver operating characteristic curve of 0.939 and 0.923, respectively. The drug response algorithm was applied to create a case control cohort. Using these records, the final study identifies phenotypes associated with etanercept response, including fibromyalgia and several axial skeleton disease phenotypes: intervertebral disc disorders, degeneration of intervertebral disc, and spinal stenosis. Taken together, these studies demonstrate that EHR data can be an important tool for clinical and genomic research, and offer particular promise for the study of RA. Advisors/Committee Members: Josh Denny (chair), Tom Lasko (committee member), Hua Xu (committee member), Digna Velez-Edwards (committee member), Jeremy Warner (committee member).

Subjects/Keywords: Secondary use; data analysis; informatics

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APA (6th Edition):

Carroll, R. J. (2014). Defining Phenotypes, Predicting Drug Response, and Discovering Genetic Associations in the Electronic Health Record with Applications in Rheumatoid Arthritis. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu/available/etd-11222014-155557/ ;

Chicago Manual of Style (16th Edition):

Carroll, Robert James. “Defining Phenotypes, Predicting Drug Response, and Discovering Genetic Associations in the Electronic Health Record with Applications in Rheumatoid Arthritis.” 2014. Doctoral Dissertation, Vanderbilt University. Accessed November 22, 2019. http://etd.library.vanderbilt.edu/available/etd-11222014-155557/ ;.

MLA Handbook (7th Edition):

Carroll, Robert James. “Defining Phenotypes, Predicting Drug Response, and Discovering Genetic Associations in the Electronic Health Record with Applications in Rheumatoid Arthritis.” 2014. Web. 22 Nov 2019.

Vancouver:

Carroll RJ. Defining Phenotypes, Predicting Drug Response, and Discovering Genetic Associations in the Electronic Health Record with Applications in Rheumatoid Arthritis. [Internet] [Doctoral dissertation]. Vanderbilt University; 2014. [cited 2019 Nov 22]. Available from: http://etd.library.vanderbilt.edu/available/etd-11222014-155557/ ;.

Council of Science Editors:

Carroll RJ. Defining Phenotypes, Predicting Drug Response, and Discovering Genetic Associations in the Electronic Health Record with Applications in Rheumatoid Arthritis. [Doctoral Dissertation]. Vanderbilt University; 2014. Available from: http://etd.library.vanderbilt.edu/available/etd-11222014-155557/ ;


Vanderbilt University

2. Yin, Zhijun. Automated Learning Of Health Behaviors Through Consumer Authored Natural Language Text.

Degree: PhD, Computer Science, 2018, Vanderbilt University

Traditional methods for collecting data in support of clinical research include prospectively collected surveys, retrospective analyses of existing medical records, and a combination of the two. Yet these approaches tend to focus on a medical-centric worldview and, as a result, provide only a partial view of a patient's life. As distributed systems, cloud services and mobile devices grow in sophistication and market penetration, large amounts of personal data are generated every day, particularly in online environments, where a range of aspects of their life are disclosed, including information related to one's health. This situation provides an opportunity for healthcare providers and biomedical researchers to learn about patients from their own voice and beyond traditional data sources. However, collecting, processing, and acting upon self-authored natural language text imposes challenges on automatically extracting health-related information, including, but not limited to, ambiguity in communication, noisy data, long exposition that contains many different types of health information, and high-dimensionality in predictive model interoperability. This dissertation applies a data-driven approach to investigate how self-authored information in three different online environments can be relied upon to learn about health-related behaviors. Specifically, this dissertation investigates three foundational questions. First, how do individuals disclose health status through a general social media platform (e.g., Twitter)? Second, can patients' long-term treatment adherence be inferred through online health communities (e.g., forums in breastcancer.org)? Third, how can we learn patients' needs based on the messages they send to healthcare providers over a patient portal that is connected to an electronic medical record (EMR) system that is ingrained in the everyday functions of a large academic medical center? To process consumer-authored natural language text, this dissertation illustrates how to combine text mining, machine learning, and statistical inference to 1) extract health related events (e.g., adherence status), 2) create interpretable factors (e.g., semantic groups), 3) build efficient predicting models (e.g., predicting medication interruption events), and 4) learn meaningful health-related associations (e.g., semantics and health status disclosure, emotions and portray of adherence status, topics and medication adherence). It is shown that many factors communicated through self-authored text (e.g., emotions, personalities, and other factors that are not captured in structured EMRs) can be applied to explain an individual's health-related behavior. This research provides evidence that self-generated information can be applied to supplement traditional data sources to facilitate healthcare research. Advisors/Committee Members: Ching-Hua Chen (committee member), Daniel Fabbri (committee member), Jeremy Warner (committee member), Yevgeniy Vorobeychik (committee member), Yuan Xue (committee member), Bradley Malin (chair).

Subjects/Keywords: Hormonal Therapy; Data Mining; Machine Learning; Statistical Inference; Natural Language Processing; Treatment Adherence; User Generated Content; Health Behavior; Patient Portal; Privacy; Social Media; Online Health Community

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yin, Z. (2018). Automated Learning Of Health Behaviors Through Consumer Authored Natural Language Text. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu/available/etd-02112018-221351/ ;

Chicago Manual of Style (16th Edition):

Yin, Zhijun. “Automated Learning Of Health Behaviors Through Consumer Authored Natural Language Text.” 2018. Doctoral Dissertation, Vanderbilt University. Accessed November 22, 2019. http://etd.library.vanderbilt.edu/available/etd-02112018-221351/ ;.

MLA Handbook (7th Edition):

Yin, Zhijun. “Automated Learning Of Health Behaviors Through Consumer Authored Natural Language Text.” 2018. Web. 22 Nov 2019.

Vancouver:

Yin Z. Automated Learning Of Health Behaviors Through Consumer Authored Natural Language Text. [Internet] [Doctoral dissertation]. Vanderbilt University; 2018. [cited 2019 Nov 22]. Available from: http://etd.library.vanderbilt.edu/available/etd-02112018-221351/ ;.

Council of Science Editors:

Yin Z. Automated Learning Of Health Behaviors Through Consumer Authored Natural Language Text. [Doctoral Dissertation]. Vanderbilt University; 2018. Available from: http://etd.library.vanderbilt.edu/available/etd-02112018-221351/ ;

3. Sulieman, Lina Mahmoud. A process modeling strategy to learn ischemic stroke treatment patterns from electronic medical records.

Degree: MS, Biomedical Informatics, 2014, Vanderbilt University

Process mining corresponds to a collection of methodologies designed to extract knowledge from event logs (e.g., time-stamped events) and provide a description about the underlying processes of a system. Various approaches have been developed and successfully applied to characterize, as well assess the efficiency of, the processes in traditional information management systems. In many instances, the clinical setting can be represented as a sequence of events that are aligned to deliver the best outcome. As such, to date, there have been several attempts to apply process mining techniques to learn and describe clinical workflows by learning frequent patterns from the event logs of electronic medical record (EMR) systems. However, the existing sets of techniques are designed to work with highly-structured data and systematic processes, such as those that occur immediately before and after a surgery. As such, the existing set of clinical processes that can be learned via such methods are limited in that they are 1) cumbersome and very detailed which will be difficult to read and analyze, 2) and fail to describe the actions invoked to treat subpopulations within a cohort of patients admitted for the same disease. This thesis introduces a multi-step process mining strategy, called Treatment Mining using Frequent Sequential Patterns (TM-FSP), to learn clinical workflows from high-dimensional patient episodes. TM-FSP filters the time-ordered sets of medication classes and laboratory test types into frequent events to represent the data in a lower-dimensional form. Next, patient event sequences are subject to a multiple sequence alignment strategy and clustered based on the similarity of their aligned event patterns. Finally, the common actions for each cluster are extracted and reported as workflows. We evaluated TM-FSP with a cohort of 133 patients diagnosed with ischemic stroke at the Vanderbilt University Medical Center. The results illustrate that 7 medications and 12 laboratory test forms 2,020 patterns that are associated with the treatment of this cohort. Moreover, it was discovered that subgroups of patients, who are influenced by lipid metabolism disorders lead to variation in their treatment by excluding Beta blockers and Insulin from their treatment course. Advisors/Committee Members: Bradley A. Malin (chair), Nancy Lorenzi (committee member), Jeremy Warner (committee member), Daniel Fabbri (committee member).

Subjects/Keywords: SPADE; MSA; multiple sequence alignment; treatment; sequence; frequent pattern mining; process mining; workflow mining; clinical workflow; clinical pathway

Vanderbilt University Medical Center and the series of experiments applied to evaluate TMFSP. In… …and data derived from the electronic medical record system of Vanderbilt University medical… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sulieman, L. M. (2014). A process modeling strategy to learn ischemic stroke treatment patterns from electronic medical records. (Masters Thesis). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu/available/etd-07152014-074713/ ;

Chicago Manual of Style (16th Edition):

Sulieman, Lina Mahmoud. “A process modeling strategy to learn ischemic stroke treatment patterns from electronic medical records.” 2014. Masters Thesis, Vanderbilt University. Accessed November 22, 2019. http://etd.library.vanderbilt.edu/available/etd-07152014-074713/ ;.

MLA Handbook (7th Edition):

Sulieman, Lina Mahmoud. “A process modeling strategy to learn ischemic stroke treatment patterns from electronic medical records.” 2014. Web. 22 Nov 2019.

Vancouver:

Sulieman LM. A process modeling strategy to learn ischemic stroke treatment patterns from electronic medical records. [Internet] [Masters thesis]. Vanderbilt University; 2014. [cited 2019 Nov 22]. Available from: http://etd.library.vanderbilt.edu/available/etd-07152014-074713/ ;.

Council of Science Editors:

Sulieman LM. A process modeling strategy to learn ischemic stroke treatment patterns from electronic medical records. [Masters Thesis]. Vanderbilt University; 2014. Available from: http://etd.library.vanderbilt.edu/available/etd-07152014-074713/ ;

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