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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).
We evaluated ExaCT using 50 previously unseen articles describing RCTs. The text classifier (first stage) 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 (second stage) 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.
There exists the need, therefore, to extract key trial characteristics from full-text journal articles, whether into standardized databases such as HSDB or into local databases or spreadsheets for evidence synthesis projects such as systematic reviews and meta-analyses. Automated methods for this extraction would reduce the time and labor cost compared to current manual methods , and would benefit a wide range of users who need to summarize RCT information from full-text journal articles.
In this paper, we present a fully operational system, called ExaCT, that assists human reviewers - who we will henceforth call \"curators\" - in excerpting sentences and fragments of text describing 21 key trial characteristics (Table 1) - which we will henceforth call \"information elements\" - from journal publications on RCTs. ExaCT consists of a web browser-based user interface integrated with an automatic information extraction (IE) engine. The IE engine extracts sentences and fragments of text from the journal articles as descriptors of the information elements of interest. The user interface allows the curator to review and modify these excerpts prior to saving the data for coding using OCRe, or for other purposes.
There were two main reasons for us to go beyond previous approaches. First, in the context of the HSDB Project, we needed to look at the full text of publications rather than only the abstract or summary. Abstracts tend not to address various trial characteristics, such as complete eligibility criteria, funding sources, secondary outcomes, and whether the trial was stopped early. These details must be identified and extracted in order to support methodologically rigorous data analysis. Methods that work well on succinct types of text such as abstracts, with their neatly delimited context, do not do as well on a publication's full text. Second, each of the previous approaches focused on a small number (1-4) of information elements, so that coverage of all of our 21 information elements would have required us to implement a range of different techniques.
To overcome these limitations, we proposed a unified approach to extract 21 diverse information elements from full-text RCT publications . We make use of a two-step approach to IE. First, a text classifier selects the sentences in the text that are most likely to contain a particular piece of target information. Then, simple regular expression rules are applied to extract the exact text excerpts from these selected sentences. This strategy was based on the intuition that if the context is sufficiently restricted (e.g. 'this sentence is the most likely one to mention the start date of a trial'), then a simple rule (e.g., 'the first occurrence of a date') is enough to extract the sought-after information. The proposed approach does not require extensive individual modelling for each information element as do methods with a strong semantic and/or linguistic reliance . Our preliminary results showed good performance demonstrating that our statistical classifier for sentence selection, combined with simple ('weak') extraction rules, can address the diversity in the task. Independent of our earlier study, Patwardhan and Riloff  exploited a strikingly similar two-stage IE strategy that they found to be beneficial in their application domains (public safety), though their scope and design details differ from ours.
The key part of the ExaCT system is its IE engine, which extracts pieces of information (sentences and/or text fragments) from a trial publication to fill slots in the pre-defined template. The template includes 21 information elements (Table 1) based on the CONSORT statement [32, 33] and on a task analysis of the information needs of systematic reviewing . Fig. 1 presents an example of the template filled in with the information contained in a trial publication abstract. Noticeably, the elements vary greatly in their structure. Some are short, precise pieces of information, e.g. the number of subjects enrolled (sample size). Others, such as eligibility criteria, are lengthy, free-text descriptions spanning several sentences. Even though all this information is essential for a comprehensive description of a trial, often some parts are skipped (e.g. start date and end date of enrolment) or poorly defined in a publication (e.g. \"main outcomes\" instead of a distinction between primary and secondary outcomes).
ExaCT's IE engine looks for text excerpts that most closely describe the trial information elements of interest. For each element (with the exception of publication details, i.e. author name, date of publication, and DOI, which are retrieved directly from PubMed), the system outputs the five best candidate sentences in decreasing order of confidence (Fig. 2). The text fragments identified as containing the target information on the element are highlighted in the retrieved sentences. If the confidence level of a particular sentence is too low, no text fragments are highlighted, even if the sentence is among the five best. For eligibility criteria, the whole sentence is considered the target, so no fragments are highlighted in those sentences. Note that in a publication, for each information element there can be
Example of the system's output. The publication details of an article are retrieved directly from PubMed. For other information elements, the system outputs five best candidate sentences in decreasing order of confidence. The text fragments identified by the system as containing the target information are highlighted in the retrieved sentences whose confidence score is above a certain threshold. For eligibility criteria, the whole sentence is considered the target, so no fragments are highlighted in those sentences. Sentences in black were confirmed as correct answers by the field expert.
Our unified approach is based on a machine learning paradigm. Manually labeled training material is collected so that the system can automatically learn the correct context for each information element. Then, a set of hand-crafted 'weak' rules is applied to the identified contexts to extract the exact values for each element. For example, in a sentence that contains enough language clues (i.e. words and phrases) for the system to recognize the context for start date of enrolment, the first appearance of a date is returned as the target for this element. This approach relies on two main assumptions. First, a 'weak' extraction rule, too unspecific to extract a precise piece of information from the whole article, will likely be accurate in a narrow enough context (e.g., a sentence). Second, segmentation at the sentence level provides a context that is narrow enough to directly get to the target information and broad enough to correctly judge its relevance.
Further pre-processing of a textual document is fully automatic. First, the main sections and subsections of an article are identified. XML documents often have sections and their headings clearly marked with the corresponding tags (e.g. section heading section content ). In HTML documents, tags marking section headings are used erratically from journal to journal, but quite consistently within the same article. Assuming this consistency, we employ the following algorithm for section detection. For each article, sequences of HTML tags surrounding the phrases commonly found to be section headings in scientific publications (such as 'Abstract', 'Methods', 'Results') are collected; subsequently, all phrases surrounded by identical or similar tag sequences within the article are assumed to be section headings or subsection headings. As previously noted in , the detection of section boundaries is not a trivial task. The above algorithm gives only approximate boundaries for major sections. However, incidental observations indicated that this pre-processing step was often helpful and never harmful. Since this step is a non-critical component of the overall system, we find that an independent evaluation of this algorithm is beyond the scope of the current work.
Sentence classification is built around a statistical machine learning component, based on the Support Vector Machine (SVM) algorithm, which learns a statistical model from articles that a field expert manually annotated. A separate statistical model is created for each information element. Then, at the classification step, each element's model is applied to all sentences to discover which sentences are most similar to the training examples for this element. Within the classification step, a sentence is represented with a bag-of-terms, where terms are words and annotation tags, as well as multi-word phrases (word n-grams). For each information element, the output of the classification stage is a ranked list of the top five sentences scored by a classifier as the most promising to contain the target information. If the confidence score of the top candidate sentence is very low, a \"not found\" message is shown to the user. 153554b96e