Doctor of Philosophy

dissertationElectronic Health Records (EHRs) provide a wealth of information for secondary uses. Methods are developed to improve usefulness of free text query and text processing and demonstrate advantages to using these methods for clinical research, specifically cohort identification and enhancement. Cohort identification is a critical early step in clinical research. Problems may arise when too few patients are identified, or the cohort consists of a nonrepresentative sample. Methods of improving query formation through query expansion are described. Inclusion of free text search in addition to structured data search is investigated to determine the incremental improvement of adding unstructured text search over structured data search alone. Query expansion using topic- and synonym-based expansion improved information retrieval performance. An ensemble method was not successful. The addition of free text search compared to structured data search alone demonstrated increased cohort size in all cases, with dramatic increases in some. Representation of patients in subpopulations that may have been underrepresented otherwise is also shown. We demonstrate clinical impact by showing that a serious clinical condition, scleroderma renal crisis, can be predicted by adding free text search. A novel information extraction algorithm is developed and evaluated (Regular Expression Discovery for Extraction, or REDEx) for cohort enrichment. The REDEx algorithm is demonstrated to accurately extract information from free text clinical iv narratives. Temporal expressions as well as bodyweight-related measures are extracted. Additional patients and additional measurement occurrences are identified using these extracted values that were not identifiable through structured data alone. The REDEx algorithm transfers the burden of machine learning training from annotators to domain experts. We developed automated query expansion methods that greatly improve performance of keyword-based information retrieval. We also developed NLP methods for unstructured data and demonstrate that cohort size can be greatly increased, a more complete population can be identified, and important clinical conditions can be detected that are often missed otherwise. We found a much more complete representation of patients can be obtained. We also developed a novel machine learning algorithm for information extraction, REDEx, that efficiently extracts clinical values from unstructured clinical text, adding additional information and observations over what is available in structured text alone

Using Explainable Deep Learning and Logistic Regression to Evaluate Complementary and Integrative Health Treatments in Patients with Musculoskeletal Disorders

There is an increasing interest in the use of Complementary and Integrative Health (CIH) for treatment of pain as an alternative to opioid medications. We use a novel explainable deep learning approach compared and contrasted to a traditional logistic regression model to explore the impact of musculoskeletal disorder related factors on the use of CIH. The impact scores from the neural network show high correlation with the log-odds ratios of the logistic regression, showing the promise that neural networks can be used to identify high impact factors without depending on a priori assumptions and limitations of traditional statistical models

The effect of simulated narratives that leverage EMR data on shared decision-making: a pilot study

BACKGROUND: Shared decision-making can improve patient satisfaction and outcomes. To participate in shared decision-making, patients need information about the potential risks and benefits of treatment options. Our team has developed a novel prototype tool for shared decision-making called hearts like mine (HLM) that leverages EHR data to provide personalized information to patients regarding potential outcomes of different treatments. These potential outcomes are presented through an Icon array and/or simulated narratives for each “person” in the display. In this pilot project we sought to determine whether the inclusion of simulated narratives in the display affects individuals’ decision-making. Thirty subjects participated in this block-randomized study in which they used a version of HLM with simulated narratives and a version without (or in the opposite order) to make a hypothetical therapeutic decision. After each decision, participants completed a questionnaire that measured decisional confidence. We used Chi square tests to compare decisions across conditions and Mann–Whitney U tests to examine the effects of narratives on decisional confidence. Finally, we calculated the mean of subjects’ post-experiment rating of whether narratives were helpful in their decision-making. RESULTS: In this study, there was no effect of simulated narratives on treatment decisions (decision 1: Chi squared = 0, p = 1.0; decision 2: Chi squared = 0.574, p = 0.44) or Decisional confidence (decision 1, w = 105.5, p = 0.78; decision 2, w = 86.5, p = 0.28). Post-experiment, participants reported that narratives helped them to make decisions (mean = 3.3/4). CONCLUSIONS: We found that simulated narratives had no measurable effect on decisional confidence or decisions and most participants felt that the narratives were helpful to them in making therapeutic decisions. The use of simulated stories holds promise for promoting shared decision-making while minimizing their potential biasing effect

Identification and Use of Frailty Indicators from Text to Examine Associations with Clinical Outcomes Among Patients with Heart Failure.

Frailty is an important health outcomes indicator and valuable for guiding healthcare decisions in older adults, but is rarely collected in a quantitative, systematic fashion in routine healthcare. Using a cohort of 12,000 Veterans with heart failure, we investigated the feasibility of topic modeling to identify frailty topics in clinical notes. Topics were generated through unsupervised learning and then manually reviewed by an expert. A total of 53 frailty topics were identified from 100,000 notes. We further examined associations of frailty with age-, sex-, and Charlson Comorbidity Index-adjusted 1-year hospitalizations and mortality (composite outcome) using logistic regression. Frailty (≤ 4 topics versu

Genetic Patterns of Domestication in Pigeonpea (Cajanus cajan (L.) Millsp.) and Wild Cajanus Relatives

Pigeonpea (Cajanus cajan) is an annual or short-lived perennial food legume of acute regional importance, providing significant protein to the human diet in less developed regions of Asia and Africa. Due to its narrow genetic base, pigeonpea improvement is increasingly reliant on introgression of valuable traits from wild forms, a practice that would benefit from knowledge of its domestication history and relationships to wild species. Here we use 752 single nucleotide polymorphisms (SNPs) derived from 670 low copy orthologous genes to clarify the evolutionary history of pigeonpea (79 accessions) and its wild relatives (31 accessions). We identified three well-supported lineages that are geographically clustered and congruent with previous nuclear and plastid sequence-based phylogenies. Among all species analyzed Cajanus cajanifolius is the most probable progenitor of cultivated pigeonpea. Multiple lines of evidence suggest recent gene flow between cultivated and non-cultivated forms, as well as historical gene flow between diverged but sympatric species. Evidence supports that primary domestication occurred in India, with a second and more recent nested population bottleneck focused in tropical regions that is the likely consequence of pigeonpea breeding. We find abundant allelic variation and genetic diversity among the wild relatives, with the exception of wild species from Australia for which we report a third bottleneck unrelated to domestication within India. Domesticated C. cajan possess 75% less allelic diversity than the progenitor clade of wild Indian species, indicating a severe “domestication bottleneck” during pigeonpea domestication

Detecting Secular Trends in Clinical Treatment through Temporal Analysis

© 2019, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. Medical treatments change over time for multiple reasons, including introduction of new treatments, availability of new scientific evidence, change in institutional guidelines, and market efforts by pharmaceutical and medical device companies. Monitoring and analyzing these secular trends will also inform the evaluation of evidence based practice as well as outcome research. Using a large national clinical dataset from the United States Veterans Health Administration (VHA), we measured the change in prevalence of all diseases, medications, and procedures by year from 2001 to 2014. To assess statistical significance, we used a generalized linear model. Among the large number of changes that were observed, multiple significant changes were related to diabetes mellitus type II (DM2). Prevalence of DM2 in the VHA increased after 2001 but plateaued by 2008; blood sugar testing by glycosylated hemoglobin increased consistently while glucose testing decreased; and the trend of insulin and metformin use was consistent with the trend in DM2 prevalence, while glyburide and rosiglitazone use dropped sharply

Explaining AI models for clinical research: Validation through model comparison and data simulation

© Copyright 2019 IADIS Press All rights reserved. For clinical research to take advantage of artificial intelligence techniques such as the various types of deep neural networks, we need to be able to explain the deep neural network models to clinicians and researchers. While some explanation approaches have been developed, their validation and utilization are very limited. In this study, we evaluated a novel explainable artificial intelligence method called impact assessment by applying it to deep neural networks trained on real world and simulated data. Using real clinical data, the impact scores from deep neural networks were compared with odds ratios from logistic regression models. Using simulated data, the impact scores from deep neural networks were compared with the impact scores calculated based on the ground truth (i.e. formulas used to generate the simulated data). The correlations between impact scores and odds ratios ranged from 0.63 to 0.97. The correlations between impact scores from DNN and ground truth ranged were all above 0.99. These suggest that the impact score provide a valid explanation of the contribution of a variable in a DNN model

Taught by Arrington

Taught by Arrington Moderated by Douglas Anderson, Dean, Huntsman School George Daines, Attorney, Daines & Jenkins Michael Dryden, President, Earth Toxics Dwight Israelsen, Huntsman School Professor Hardy Redd, Retired, La Sal Livestock Janet Daines Stowell, Educator, Chicago, Illinoi