Data Mining in Large-Scale Clinical Visit Data for Rett Syndrome Patients

Rett syndrome (RTT) is a rare neurological disorder that predominantly affects girls. Research on RTT has mostly centered around gene mutations and possibility of cure using gene therapy. In this thesis we perform the first large scale systematic study of RTT patient records. The thesis has two major goals. One is to identify behavioral groups and the other is to study the association of medications and behavior or conditions. To achieve the first goal we apply standard clustering techniques like non-negative matrix factorization and k-means. We identify behavioral groups which could be used by clinicians for formulating better treatments. For the second goal we start with the most popular existing technique, disproportionality analysis, and make necessary adaptations for our data set. We then generalize this method and suggest an alternate approach which efficiently answers which medication caused the most change in a behavior. We test both approaches and show that the medications shown to decrease seizures the most are indeed those prescribed for the same. Using this as a tool, clinicians can identify possible side effects of medications

Using the Literature to Identify Confounders

Prior work in causal modeling has focused primarily on learning graph structures and parameters to model data generating processes from observational or experimental data, while the focus of the literature-based discovery paradigm was to identify novel therapeutic hypotheses in publicly available knowledge. The critical contribution of this dissertation is to refashion the literature-based discovery paradigm as a means to populate causal models with relevant covariates to abet causal inference. In particular, this dissertation describes a generalizable framework for mapping from causal propositions in the literature to subgraphs populated by instantiated variables that reflect observational data. The observational data are those derived from electronic health records. The purpose of causal inference is to detect adverse drug event signals. The Principle of the Common Cause is exploited as a heuristic for a defeasible practical logic. The fundamental intuition is that improbable co-occurrences can be “explained away” with reference to a common cause, or confounder. Semantic constraints in literature-based discovery can be leveraged to identify such covariates. Further, the asymmetric semantic constraints of causal propositions map directly to the topology of causal graphs as directed edges. The hypothesis is that causal models conditioned on sets of such covariates will improve upon the performance of purely statistical techniques for detecting adverse drug event signals. By improving upon previous work in purely EHR-based pharmacovigilance, these results establish the utility of this scalable approach to automated causal inference

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

Development and preliminary validation of an instrument to enable laypersons to assess suspected side effects from medicines

Purpose Research into causality assessment tools enabling patients to assess suspected adverse drug reactions (ADRs) is limited. Supporting patients with tools could improve their confidence in discussions with health professionals and encourage reporting of suspected ADRs to regulators. This study describes development and preliminary validation of an instrument: Side Effect Patient ASsessment Tool (SE‐PAST). Methods SE‐PAST was developed from survey and interview data involving patients experiencing suspected ADRs. It included 10 statements enabling causality assessment, covering timing, additional information sources, and experiences, with four options: yes/no/don't know/not applicable. Scoring and weighting resulted in four categories of causal association: highly probable, probable, possible, unlikely. Validation involved obtaining feedback from 31 individuals experiencing an ADR. Further validation involved online distribution through patient support groups and comparison of reported symptoms to known ADRs. Results Validators found SE‐PAST easy to read (31), to understand (27), and to complete (29). A total of 294 respondents completed SE‐PAST online, with 98% completing eight or more causality assessment statements. Symptoms were categorised as highly probable (46; 16%), probable (80; 62%), possible (44; 15%), and unlikely (21; 7%). A total of 221 respondents identified one suspected medicine, with 95% of these reporting at least one symptom known to be an ADR. Of 227 providing feedback, 139 (61%) found SE‐PAST useful, 160 (71%) felt motivated to discuss their experience with a health professional, and 136 (60%) were encouraged to report to the regulator. Conclusion SE‐PAST was easily completed and understood by people experiencing suspected ADRs and could be useful in encouraging patient reporting to health professionals and agencies