Three Essays on Enhancing Clinical Trial Subject Recruitment Using Natural Language Processing and Text Mining

Patient recruitment and enrollment are critical factors for a successful clinical trial; however, recruitment tends to be the most common problem in most clinical trials. The success of a clinical trial depends on efficiently recruiting suitable patients to conduct the trial. Every clinical trial research has a protocol, which describes what will be done in the study and how it will be conducted. Also, the protocol ensures the safety of the trial subjects and the integrity of the data collected. The eligibility criteria section of clinical trial protocols is important because it specifies the necessary conditions that participants have to satisfy. Since clinical trial eligibility criteria are usually written in free text form, they are not computer interpretable. To automate the analysis of the eligibility criteria, it is therefore necessary to transform those criteria into a computer-interpretable format. Unstructured format of eligibility criteria additionally create search efficiency issues. Thus, searching and selecting appropriate clinical trials for a patient from relatively large number of available trials is a complex task. A few attempts have been made to automate the matching process between patients and clinical trials. However, those attempts have not fully integrated the entire matching process and have not exploited the state-of-the-art Natural Language Processing (NLP) techniques that may improve the matching performance. Given the importance of patient recruitment in clinical trial research, the objective of this research is to automate the matching process using NLP and text mining techniques and, thereby, improve the efficiency and effectiveness of the recruitment process. This dissertation research, which comprises three essays, investigates the issues of clinical trial subject recruitment using state-of-the-art NLP and text mining techniques. Essay 1: Building a Domain-Specific Lexicon for Clinical Trial Subject Eligibility Analysis Essay 2: Clustering Clinical Trials Using Semantic-Based Feature Expansion Essay 3: An Automatic Matching Process of Clinical Trial Subject Recruitment In essay1, I develop a domain-specific lexicon for n-gram Named Entity Recognition (NER) in the breast cancer domain. The domain-specific dictionary is used for selection and reduction of n-gram features in clustering in eassy2. The domain-specific dictionary was evaluated by comparing it with Systematized Nomenclature of Medicine--Clinical Terms (SNOMED CT). The results showed that it add significant number of new terms which is very useful in effective natural language processing In essay 2, I explore the clustering of similar clinical trials using the domain-specific lexicon and term expansion using synonym from the Unified Medical Language System (UMLS). I generate word n-gram features and modify the features with the domain-specific dictionary matching process. In order to resolve semantic ambiguity, a semantic-based feature expansion technique using UMLS is applied. A hierarchical agglomerative clustering algorithm is used to generate clinical trial clusters. The focus is on summarization of clinical trial information in order to enhance trial search efficiency. Finally, in essay 3, I investigate an automatic matching process of clinical trial clusters and patient medical records. The patient records collected from a prior study were used to test our approach. The patient records were pre-processed by tokenization and lemmatization. The pre-processed patient information were then further enhanced by matching with breast cancer custom dictionary described in essay 1 and semantic feature expansion using UMLS Metathesaurus. Finally, I matched the patient record with clinical trial clusters to select the best matched cluster(s) and then with trials within the clusters. The matching results were evaluated by internal expert as well as external medical expert

Three Processes that Form Online Social Networking Post-Adoptive Use Intention

Not all individuals log into an online social networking (OSN) website because they have deliberately reflected on how useful and fun it will be. For some users, this post-adoptive use decision requires a less deliberate process based on past experience. For still others, the decision is automatic and requires little, if any, reflection on beliefs or prior experiences. While past research has examined these different post-adoptive thought processes, no research to date has done so in an OSN context. This study develops a research model that combines reflective, transitional, and non-reflective thought processes into a comprehensive model of post-adoptive OSN intention. We test the hypotheses with cross sectional data collected from Facebook users. We find that all three thought processes predict intention, although the effects of experience on intention during the transitional and non-reflective thought processes are strongest. Results also show that habit, enjoyment, trust, usefulness, and privacy concern predict OSN continuance intention

The Surprising Lack of Effect of Privacy Concerns on Intention to Use Online Social Networks

The number of users of Online Social Networks (OSNs) has increased dramatically. To join OSNs, users need to disclosetheir information to others. If people have higher levels of privacy concerns, they may hesitate to expose their information.Therefore, privacy concerns should be an important factor affecting the use of OSNs. Based on prior studies, we investigatehow individuals’ perceived benefits (usefulness, playfulness) and perceived costs (privacy concern) directly influence theirintention to continue using OSNs, and how the benefits are mediated by cost factors in cognitive cost-benefit calculations.We suggest five hypotheses and examine them empirically with 391 survey responses. The results only support the directeffect of perceived benefits on OSNs. Results do not show any direct effect or mediation effect of privacy concerns on theintention to use OSNs. This paper contributes to future social network studies by providing a conceptual framework as wellas empirical results

Adoption in Korea: A longitudinal (1920-2006) analysis of ideological changes in the public discourse

A longitudinal analysis (1920-2006) of “adoption” as an issue represented in the public discourse in Korea, is presented in this master thesis. Newspaper articles referring to adoption were identified in searchable electronic databases. As Korea is a major source of transnational adoption and also has the highest ratio of adoptions per 100.000 births, one would expect adoption to constitute a prominent social issue in the public discourse. Across the seven decades (1920-2006) available to analysis, the frequency of newspaper articles referring to adoption varied strongly: With exception to a few occasions during the twenties, adoption was virtually absent as an issue in the public discourse until after WWII, and in particular after the Korean War. After the huge interest created by the Korean War faded, the interest for adoption stayed at a rather low level until the late eighties/early nineties when the interest increased rapidly until present (2006) and by far surpassed even the frequency of articles referring to adoption immediately after the Korean War. Qualitative analysis of newspaper articles containing the word adoption showed that adoption was conceived of within ideologically frameworks changing over time

Attitude Change Process toward ERP Systems Using the Elaboration Likelihood Model

This study examines how different types of information processing routes influence a user’s attitude toward an Enterprise Resource Planning (ERP) system based on the Elaboration Likelihood Model. We tested the main effects of content quality (central route) and system credibility (peripheral route) on attitude change and the moderating effects of motivation and IT ability. Using student survey data, we tested the research model empirically. Consistent with previous research, the results reveal that content quality and system credibility are the primary factors affecting individual’s attitude change. Along with this, individuals’ extrinsic motivation positively moderates the relationship between central route and attitude change towards the system, and negatively moderates the one between peripheral route and attitude change. However, we do not observe significant interaction effects of IT ability. This study will explain individuals’ dual processing mechanism in attitude change toward information systems, which has not been extensively studied in an IS education context