Indiana University-Purdue University Indianapolis (IUPUI) / Autoimmune Hepatitis (AIH), an incurable chronic condition of unknown cause where the body attacks its own liver, is a rare disease, with a current diagnosed worldwide prevalence of < 150,000. Inadequately treated, AIH can cause progressive liver damage and ultimately liver failure. A wide variety of symptoms are associated with AIH including severe fatigue, joint pain, depression, anxiety, and insomnia. While precision medicine’s genomics has attempted to shed light on the disease, other non-molecular “-omics” approaches can be taken in studying AIH patients, who often utilize social media to gather information from other patients or care providers to apply to their own AIH disease course. It is proposed that these patient-generated social mediomes can create self-report health records for patients – and facets of their lives - otherwise unreachable by conventional research. In this feasibility study, I examined in an exploratory fashion the social mediome of a large (N > 1000) gathering of AIH patients and caregivers as present on a Facebook Group to determine the potential of mining various types health-related user information. The following types of information were mined, with feasible indicating a reliability of F >= 0.670: 1) Types of health information shared and structures of information sharing (Feasible) 2) Types and directionality of support provided by and to users (Portions feasible) 3) Clinical factors (AIH-related and otherwise) disclosed by users a. Medication intake (Feasible) b. Signs and symptoms (including pain and injury) and diagnosed comorbidities (Portions feasible) c. Results of disease monitoring blood tests (Portions feasible) 4) Contextual (non-clinical; environmental; social) factors disclosed by users (Detection of which type of factor discussed occasionally feasible). The resulting knowledge is required to adequately describe the disease not only clinically, but also environmentally and socially, and will form part of the basis for future disease studies.

Indiana University-Purdue University Indianapolis (IUPUI) / Periodontal disease (PD) affects 42% of US population resulting in compromised quality of life, the potential for tooth loss and influence on overall health. Despite significant understanding of PD etiology, limited longitudinal studies have investigated PD change in response to various treatments. A major barrier is the difficulty of conducting randomized controlled trials with adequate numbers of patients over a longer time. Electronic dental record (EDR) data offer the opportunity to study outcomes following various periodontal treatments. However, using EDR data for research has challenges including quality and missing data. In this dissertation, I studied a cohort of patients with PD from EDR to monitor their disease status over time. I studied retrospectively 28,908 patients who received comprehensive oral evaluation at the Indiana University School of Dentistry between January 1st-2009 and December 31st-2014. Using natural language processing and automated approaches, we 1) determined PD diagnoses from periodontal charting based on case definitions for surveillance studies, 2) extracted clinician-recorded diagnoses from clinical notes, 3) determined the number of patients with disease improvement or progression over time from EDR data. We found 100% completeness for age, sex; 72% for race; 80% for periodontal charting findings; and 47% for clinician-recorded diagnoses. The number of visits ranged from 1-14 with an average of two visits. From diagnoses obtained from findings, 37% of patients had gingivitis, 55% had moderate periodontitis, and 28% had severe periodontitis. In clinician-recorded diagnoses, 50% patients had gingivitis, 18% had mild, 14% had moderate, and 4% had severe periodontitis. The concordance between periodontal charting-generated and clinician-recorded diagnoses was 47%. The results indicate that case definitions for PD are underestimating gingivitis and overestimating the prevalence of periodontitis. Expert review of findings identified clinicians relying on visual assessment and radiographic findings in addition to the case definition criteria to document PD diagnosis. / 2021-08-10

Indiana University-Purdue University Indianapolis (IUPUI) / Unmet social needs pose significant risk to both patients and healthcare organizations by increasing morbidity, mortality, utilization, and costs. Health care delivery organizations are increasingly employing social workers to address social needs, given the growing number of policies mandating them to identify and address their patients’ social needs. However, social workers largely document their activities using unstructured or semi-structured textual descriptions, which may not provide information that is useful for modeling, decision-making, and evaluation. Therefore, without the ability to convert these social work documentations into usable information, the utility of these textual descriptions may be limited. While manual reviews are costly, time-consuming, and require technical skills, text mining algorithms such as natural language processing (NLP) and machine learning (ML) offer cheap and scalable solutions to extracting meaningful information from large text data. Moreover, the ability to extract information on social needs and social work interventions from free-text data within electronic health records (EHR) offers the opportunity to comprehensively evaluate the outcomes specific social work interventions. However, the use of text mining tools to convert these text data into usable information has not been well explored. Furthermore, only few studies sought to comprehensively investigate the outcomes of specific social work interventions in a safety-net population. To investigate the role of social workers in addressing patients’ social needs, this dissertation: 1) utilizes NLP, to extract and categorize the social needs that lead to referral to social workers, and market basket analysis (MBA), to investigate the co-occurrence of these social needs; 2) applies NLP, ML, and deep learning techniques to extract and categorize the interventions instituted by social workers to address patients’ social needs; and 3) measures the effects of receiving a specific social work intervention type on healthcare utilization outcomes.

An event is something that happens at a time and location. Events include major life events such as graduating college or getting married, and also simple day-to-day activities such as commuting to work or eating lunch. Most work on event extraction detects events and the entities involved in events. For example, cooking events will usually involve a cook, some utensils and appliances, and a final product. In this work, we target the task of determining whether events result in their expected outcomes. Specifically, we target cooking and baking events, and characterize event outcomes into two categories. First, we distinguish whether something edible resulted from the event. Second, if something edible resulted, we distinguish between perfect, partial and alternative outcomes. The main contributions of this thesis are a corpus of 4,000 tweets annotated with event outcome information and experimental results showing that the task can be automated. The corpus includes tweets that have only text as well as tweets that have text and an image.

Recently, there has been much effort in making biomedical knowledge, typically stored in scientific articles, more accessible and interoperable. As a matter of fact, the unstructured nature of such texts makes it difficult to apply  knowledge discovery and inference techniques. Annotating information units with semantic information in these texts is the first step to make the knowledge machine-analyzable.  In this work, we first study methods for automatic information extraction from natural language text. Then we discuss the main benefits and disadvantages of the state-of-art information extraction systems and, as a result of this, we adopt a machine learning approach to automatically learn extraction patterns in our experiments. Unfortunately, machine learning techniques often require a huge amount of training data, which can be sometimes laborious to gather. In order to face up to this tedious problem, we investigate the concept of weakly supervised or bootstrapping techniques. Finally, we show in our experiments that our machine learning methods performed reasonably well and significantly better than the baseline. Moreover, in the weakly supervised learning task we were able to substantially bring down the amount of labeled data needed for training of the extraction system.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第18613号 / 情博第537号 / 新制||情||95(附属図書館) / 31513 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 山本 章博, 教授 黒橋 禎夫, 教授 阿久津 達也 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Kyoto University's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink. / 京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19848号 / 情博第599号 / 新制||情||104(附属図書館) / 32884 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)准教授 河原 大輔, 教授 黒橋 禎夫, 教授 鹿島 久嗣 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

If the author of the published paper digitizes such paper and releases it to third parties using digital media such as computer networks or CD-ROMs, the volume, number, and pages of the Journal of Natural Language Processing of the publication must be indicated in a clear manner for all viewers. / 京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19850号 / 情博第601号 / 新制||情||105(附属図書館) / 32886 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)准教授 河原 大輔, 教授 黒橋 禎夫, 教授 河原 達也 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第21911号 / 情博第694号 / 新制||情||119(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 黒橋 禎夫, 教授 鹿島 久嗣, 准教授 河原 大輔 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

付記する学位プログラム名: デザイン学大学院連携プログラム / 京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第21912号 / 情博第695号 / 新制||情||119(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 黒橋 禎夫, 教授 西田 豊明, 教授 楠見 孝 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM