Doctor of Philosophy

dissertationThe primary objective of cancer registries is to capture clinical care data of cancer populations and aid in prevention, allow early detection, determine prognosis, and assess quality of various treatments and interventions. Furthermore, the role of cancer registries is paramount in supporting cancer epidemiological studies and medical research. Existing cancer registries depend mostly on humans, known as Cancer Tumor Registrars (CTRs), to conduct manual abstraction of the electronic health records to find reportable cancer cases and extract other data elements required for regulatory reporting. This is often a time-consuming and laborious task prone to human error affecting quality, completeness and timeliness of cancer registries. Central state cancer registries take responsibility for consolidating data received from multiple sources for each cancer case and to assign the most accurate information. The Utah Cancer Registry (UCR) at the University of Utah, for instance, leads and oversees more than 70 cancer treatment facilities in the state of Utah to collect data for each diagnosed cancer case and consolidate multiple sources of information.Although software tools helping with the manual abstraction process exist, they mainly focus on cancer case findings based on pathology reports and do not support automatic extraction of other data elements such as TNM cancer stage information, an important prognostic factor required before initiating clinical treatment. In this study, I present novel applications of natural language processing (NLP) and machine learning (ML) to automatically extract clinical and pathological TNM stage information from unconsolidated clinical records of cancer patients available at the central Utah Cancer Registry. To further support CTRs in their manual efforts, I demonstrate a new approach based on machine learning to consolidate TNM stages from multiple records at the patient level

Optimising medication data collection in a large-scale clinical trial

© 2019 Lockery et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Objective: Pharmaceuticals play an important role in clinical care. However, in community-based research, medication data are commonly collected as unstructured free-text, which is prohibitively expensive to code for large-scale studies. The ASPirin in Reducing Events in the Elderly (ASPREE) study developed a two-pronged framework to collect structured medication data for 19,114 individuals. ASPREE provides an opportunity to determine whether medication data can be cost-effectively collected and coded, en masse from the community using this framework. Methods: The ASPREE framework of type-to-search box with automated coding and linked free text entry was compared to traditional method of free-text only collection and post hoc coding. Reported medications were classified according to their method of collection and analysed by Anatomical Therapeutic Chemical (ATC) group. Relative cost of collecting medications was determined by calculating the time required for database set up and medication coding. Results Overall, 122,910 participant structured medication reports were entered using the type-tosearch box and 5,983 were entered as free-text. Free-text data contributed 211 unique medications not present in the type-to-search box. Spelling errors and unnecessary provision of additional information were among the top reasons why medications were reported as freetext. The cost per medication using the ASPREE method was approximately USD $0.03 compared with USD$0.20 per medication for the traditional method. Conclusion Implementation of this two-pronged framework is a cost-effective alternative to free-text only data collection in community-based research. Higher initial set-up costs of this combined method are justified by long term cost effectiveness and the scientific potential for analysis and discovery gained through collection of detailed, structured medication data

Adverse Drug Event Detection, Causality Inference, Patient Communication and Translational Research

Adverse drug events (ADEs) are injuries resulting from a medical intervention related to a drug. ADEs are responsible for nearly 20% of all the adverse events that occur in hospitalized patients. ADEs have been shown to increase the cost of health care and the length of stays in hospital. Therefore, detecting and preventing ADEs for pharmacovigilance is an important task that can improve the quality of health care and reduce the cost in a hospital setting. In this dissertation, we focus on the development of ADEtector, a system that identifies ADEs and medication information from electronic medical records and the FDA Adverse Event Reporting System reports. The ADEtector system employs novel natural language processing approaches for ADE detection and provides a user interface to display ADE information. The ADEtector employs machine learning techniques to automatically processes the narrative text and identify the adverse event (AE) and medication entities that appear in that narrative text. The system will analyze the entities recognized to infer the causal relation that exists between AEs and medications by automating the elements of Naranjo score using knowledge and rule based approaches. The Naranjo Adverse Drug Reaction Probability Scale is a validated tool for finding the causality of a drug induced adverse event or ADE. The scale calculates the likelihood of an adverse event related to drugs based on a list of weighted questions. The ADEtector also presents the user with evidence for ADEs by extracting figures that contain ADE related information from biomedical literature. A brief summary is generated for each of the figures that are extracted to help users better comprehend the figure. This will further enhance the user experience in understanding the ADE information better. The ADEtector also helps patients better understand the narrative text by recognizing complex medical jargon and abbreviations that appear in the text and providing definitions and explanations for them from external knowledge resources. This system could help clinicians and researchers in discovering novel ADEs and drug relations and also hypothesize new research questions within the ADE domain

Extracting information from the text of electronic medical records to improve case detection: a systematic review

Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall)

Medical Big Data and Big Data Quality Problems

Medical big data has generated much excitement in recent years and for good reason. It can be an invaluable resource for researchers in general and insurers in particular. This Article, however, argues that users of medical big data must proceed with caution and recognize the data’s considerable limitations and shortcomings. These can consist of data errors, missing information, lack of standardization, record fragmentation, software problems, and other flaws. The Article analyzes a variety of data quality problems. It also formulates recommendations to address these deficiencies, including data audits, workforce and technical solutions, and regulatory approache

Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration\u27s Adverse Event Reporting System Narratives

BACKGROUND: The Food and Drug Administration\u27s (FDA) Adverse Event Reporting System (FAERS) is a repository of spontaneously-reported adverse drug events (ADEs) for FDA-approved prescription drugs. FAERS reports include both structured reports and unstructured narratives. The narratives often include essential information for evaluation of the severity, causality, and description of ADEs that are not present in the structured data. The timely identification of unknown toxicities of prescription drugs is an important, unsolved problem. OBJECTIVE: The objective of this study was to develop an annotated corpus of FAERS narratives and biomedical named entity tagger to automatically identify ADE related information in the FAERS narratives. METHODS: We developed an annotation guideline and annotate medication information and adverse event related entities on 122 FAERS narratives comprising approximately 23,000 word tokens. A named entity tagger using supervised machine learning approaches was built for detecting medication information and adverse event entities using various categories of features. RESULTS: The annotated corpus had an agreement of over .9 Cohen\u27s kappa for medication and adverse event entities. The best performing tagger achieves an overall performance of 0.73 F1 score for detection of medication, adverse event and other named entities. C ONCLUSIONS: In this study, we developed an annotated corpus of FAERS narratives and machine learning based models for automatically extracting medication and adverse event information from the FAERS narratives. Our study is an important step towards enriching the FAERS data for postmarketing pharmacovigilance