5/8/2023 0 Comments Html regex data extractor![]() ![]() A clinical trial might report partial results in multiple publications during the course of a study. In each review, we identified the included primary studies and archived publication reports in PDF format. Although we focused on common clinical trial data types such as sample size, group size, and PICO values, the technique can be applied to other data types that are used in SR development.įrom the Cochrane Library, we retrieved systematic reviews on the subject “heart and circulation” that were published after October 2014. We followed the extractive approach in the present study to automatically collect relevant sentences and phrases from the published PDF manuscripts. Abstractive approaches attempt to build a common semantic model and then generate graphs or natural language summaries to describe the model ( 24, 25). Extractive approaches obtain relevant words, phrases, or sentences from the original text sources to construct the summary ( 22, 23). Previous research has generally followed two main approaches: extractive and abstractive ( 20, 21). Text summarization research aims to reduce texts while keeping the most important information. In the present research, we investigated an automatic extractive text summarization system to collect relevant data from full-text publications to support the development of systematic reviews. Extracting short phrases (or fragments) and measuring sensitivity (or recall) were not their primary focus. The authors used the supervised distant supervision algorithm to rank sentences based on the relevance to PICO elements. conducted another notable work on extracting relevant sentences from full-text PDF reports to aid SR data extraction ( 19). In practice, SRs must select studies outside the top five clinical journals and many study publications are not available in HTML format. ExaCT selects RCT studies from top five core clinical journals that have full-texts available in HTML format. Their method first uses a machine learning classifier to select the top five relevant sentences for each element, and then uses hand-crafted weak extraction rules to collect values for each element. ExaCT is considered as one of the most successful full-text extraction systems for clinical elements. ( 18) developed ExaCT to help extract clinical trial characteristics. Full-text extraction is more challenging since it has to process much larger chunks of text containing substantial redundancy and noise. In fact, extraction from full-text reports is the standard requirement in SR development ( 16). Those studies extracted information from abstracts, which, while important, are not sufficient for extracting information for SRs. ( 15) employed rule-based and machine learning approaches to extract PICO and patient related attributes. Demner-Fushman and Lin ( 13), Kelly and Yang ( 14), and Hansen et al. PICO is a popular framework used to formulate and find answers to clinical questions. ( 12) investigated machine learning approaches to classify sentences that contain PICO (Population, Intervention, Control, and Outcome) elements. Computer methods have been proposed as a potential solution to enhance productivity and to reduce errors in SR data extraction.īoundin et al. This is partially because of human factors such as limited time and resources, inconsistency, and tedium-induced errors. Yet, studies have shown that the manual data extraction task has a high prevalence of errors ( 7, 8). Data extraction is one of the steps in SR development whose goal is to collect relevant information from published reports to perform quality appraisal and data synthesis, including meta-analysis. The development of systematic reviews has been faulted as resource-intensive and slow ( 4- 6). ![]() Cochrane usage data in 2009 showed that “Every day someone, somewhere searches The Cochrane Library every second, reads an abstract every two seconds, and downloads a full-text article every three seconds.” ( 3). Cochrane reviews aim to identify and synthesize the highest standard in evidence-based practice ( 2). The Cochrane Collaboration is an internationally recognized non-profit organization that develops SRs for health-related topics. An SR attempts to comprehensively identify, appraise, and synthesize the best available evidence to find reliable answers to research questions ( 1). Systematic reviews (SR) are important information sources for healthcare providers, researchers, and policy makers. ![]()
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