Knowledge Extraction and Prediction from Behavior Science Randomized Controlled Trials: A Case Study in Smoking Cessation

Due to the fast pace at which randomized controlled trials are published in the health domain, researchers, consultants and policymakers would benefit from more automatic ways to process them by both extracting relevant information and automating the meta-analysis processes. In this paper, we present a novel methodology based on natural language processing and reasoning models to 1) extract relevant information from RCTs and 2) predict potential outcome values on novel scenarios, given the extracted knowledge, in the domain of behavior change for smoking cessation

ExaCT: automatic extraction of clinical trial characteristics from journal publications

<p>Abstract</p> <p>Background</p> <p>Clinical trials are one of the most important sources of evidence for guiding evidence-based practice and the design of new trials. However, most of this information is available only in free text - e.g., in journal publications - which is labour intensive to process for systematic reviews, meta-analyses, and other evidence synthesis studies. This paper presents an automatic information extraction system, called ExaCT, that assists users with locating and extracting key trial characteristics (e.g., eligibility criteria, sample size, drug dosage, primary outcomes) from full-text journal articles reporting on randomized controlled trials (RCTs).</p> <p>Methods</p> <p>ExaCT consists of two parts: an information extraction (IE) engine that searches the article for text fragments that best describe the trial characteristics, and a web browser-based user interface that allows human reviewers to assess and modify the suggested selections. The IE engine uses a statistical text classifier to locate those sentences that have the highest probability of describing a trial characteristic. Then, the IE engine's second stage applies simple rules to these sentences to extract text fragments containing the target answer. The same approach is used for all 21 trial characteristics selected for this study.</p> <p>Results</p> <p>We evaluated ExaCT using 50 previously unseen articles describing RCTs. The text classifier (<it>first stage</it>) was able to recover 88% of relevant sentences among its top five candidates (top5 recall) with the topmost candidate being relevant in 80% of cases (top1 precision). Precision and recall of the extraction rules (<it>second stage</it>) were 93% and 91%, respectively. Together, the two stages of the extraction engine were able to provide (partially) correct solutions in 992 out of 1050 test tasks (94%), with a majority of these (696) representing fully correct and complete answers.</p> <p>Conclusions</p> <p>Our experiments confirmed the applicability and efficacy of ExaCT. Furthermore, they demonstrated that combining a statistical method with 'weak' extraction rules can identify a variety of study characteristics. The system is flexible and can be extended to handle other characteristics and document types (e.g., study protocols).</p

Understanding in vivo modelling of depression

Major Depressive Disorder (MDD) is the leading source of disability globally. Treatment-resistance among patients is common and even effective pharmacological therapies have a delayed effect on symptom relief. Better understanding of the mechanisms underlying depression and the search for potential effective and novel therapeutic targets are high research and healthcare priorities. Animal models are commonly used to mimic aspects of the phenotype of the human disorder to characterise candidate antidepressant agents. Despite these tools, no new pharmacological interventions have been discovered in the last decade and no reliable biomarkers have been identified for clinical use. Systematically reviewing the literature on animal models of depression may provide an overview of our current understanding of the underlying biological mechanisms and why no new therapies have been effectively translated to clinic. This field of research is large, and over 70,000 potentially relevant articles were identified in 2016. Therefore systematically reviewing this literature presents challenges for human resources. To combat these challenges, the following contributions to the field have been made: (1) the novel application of machine learning techniques to identify errors in human systematic review citation screening; and (2), the novel application of regular expression dictionaries to large corpuses of preclinical animal literature to help cluster publications into the disease model investigated and drug intervention tested. These tools have been applied for systematic review and meta-analysis methodology to the field of animal models of depression. All literature on animal models of depression has been systematically identified using searches carried out in PubMed and EMBASE in May 2016. This literature has been screened with the help of machine learning classification algorithms, based on a random set of dual human screened records (5749 records). This achieved a sensitivity of 98.7% and a specificity of 86% as assessed on in an independent validation dataset. Machine learning has been used to identify human screening errors in the set of documents used to train the algorithm. Correction of these errors with further human intervention, sees an improvement in specificity to 88.3%. These algorithms allow irrelevant documents to be automatically removed, reducing the corpus to 18,407 articles that highly likely to be relevant to the research area of animal models of depression. Custom-made regular expression dictionaries of (1) techniques to induce depressive-like phenotypes in animals, and (2) known antidepressants have been curated. The text-mining dictionaries for anti-depressant drugs and commonly used methods of model induction have been applied to categorise and visualise this large corpus of records to allow prioritisation of sub-topics of depression for further in depth systematic review and meta-analyses. These machine-assisted tools for systematic review methodology are available free to use, online. Systematic review and meta-analysis has been conducted on two sub-topics of the literature on animal models of depression. Firstly, the literature on the effects of ketamine as an anti-depressant in animal models of depression has been summarised with systematic review techniques and the effects of ketamine on depressive-like behaviour in the forced swim test, has been pooled using meta-analysis. The timing of administration of ketamine relative to the outcome assessment was significantly associated with decreases in effect size. This meta-analysis revealed no statistically significant heterogeneity between the studies. Secondly, the literature on use of gut microbial altering interventions to induce and treat depressive-like phenotypes in animal models of depression has been summarised and their effects have been pooled across studies using meta-analysis. The systematic review and meta-analysis of microbiota interventions identified a broad range of outcomes investigated in the primary literature and several probiotic treatments to reduce depressive-like behaviour were investigate gaps in the literature. Finally, a primary hypothesis-confirming animal experiment, where measures to reduce the risk of bias have been implemented was carried out to investigate the effects of prebiotics on depressive- and anxiety-like behaviour in a genetic animal model of depression, the Flinders Sensitive Line (FSL) rats. Online tools have been developed to provide an overview of animal models of depression and anti-depressant drugs investigated in the literature, using systematic review methodology and automation tools. This thesis reports meta-analyses on two sub-topics within animal models of depression; the effect of microbiota interventions, and the effects of ketamine; along with a primary animal experiment to test the effects of prebiotics on depressive-like behaviour in a genetic rodent model of depression