Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This iteration of the living review includes publications up to a cut-off date of 22 April Results: In total, 53 publications are included in this version of our review.
Over 30 entities were extracted, with PICOs population, intervention, comparator, outcome being the most frequently extracted. Conclusions: This living systematic review presents an overview of semi automated data-extraction literature of interest to different types of systematic review.
We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data. The lack of publicly available gold-standard data for evaluation, and lack of application thereof, makes it difficult to draw conclusions on which is the best-performing system for each data extraction target. With this living review we aim to review the literature continually.
In a systematic review, data extraction is the process of capturing key characteristics of studies in structured and standardised form based on information in journal articles and reports. It is a necessary precursor to assessing the risk of bias in individual studies and synthesising their findings. Interventional, diagnostic, or prognostic systematic reviews routinely extract information from a specific set of fields that can be predefined.
The data extraction task can be time-consuming and repetitive when done by hand. This creates opportunities for support through intelligent software, which identify and extract information automatically.
When applied to the field of health research, this semi-automation sits at the interface between evidence-based medicine EBM and data science, and as described in the following section, interest in its development has grown in parallel with interest in AI in other areas of computer science.
This review is, to the best of our knowledge, the only living systematic review of data extraction methods. We have identified four previous reviews of tools and methods, 2 — 5 two documents providing overviews and guidelines relevant to our topic, 6 , 7 and an ongoing effort to list published tools for different parts of the systematic reviewing process.
A recent systematic review of machine-learning for systematic review automation, published in Portuguese, included 35 publications. The authors examined journals in which publications about systematic review automation are published, and conducted a term-frequency and citation analysis. They categorised papers by systematic review task, and provided a brief overview of data extraction methods. In , Tsafnat et al. The reviewers focused on tasks related to PICO classification and supporting the screening process.
Beller et al. They conclude that tools facilitating screening are widely accessible and usable, while data extraction tools are still at piloting stages or require a higher amount of human input. The systematic reviews from to present an overview of classical machine learning and natural language processing NLP methods applied to tasks such as data mining in the field of evidence-based medicine. At the time of publication of these documents, methods such as topic modelling Latent Dirichlet Allocation and support vector machines SVM were considered state-of-the art for language models.
The age of these publications means that the latest static or contextual embedding-based and neural methods are not included. These newer methods, 9 however, are used in contemporary systematic review automation software which will be reviewed in the scope of this living review. We aim to review published methods and tools aimed at automating or semi-automating the process of data extraction in the context of a systematic review of medical research studies.
We will do this in the form of a living systematic review, keeping information up to date and relevant to the challenges faced by systematic reviewers at any time. Our objectives in reviewing this literature are two-fold. First, we want to examine the methods and tools from the data science perspective, seeking to reduce duplicate efforts, summarise current knowledge, and encourage comparability of published methods.
Second, we seek to highlight the added value of the methods and tools from the perspective of systematic reviewers who wish to use semi automation for data extraction, i.
Is it reliable? We address these issues by summarising important caveats discussed in the literature, as well as factors that facilitate the adoption of tools in practice. This review was conducted following a preregistered and published protocol.
Any deviations from the protocol have been described below. We are conducting a living review because the field of systematic review semi automation is evolving rapidly along with advances in language processing, machine-learning and deep-learning.
The process of updating started as described in the protocol 11 and is shown in Figure 1. Articles from the dblp and IEEE are added every two months. This image is reproduced under the terms of a Creative Commons Attribution 4. The decision for full review updates is made every six months based on the number of new publications added to the review. For more details about this, please refer to the protocol or to the Cochrane living systematic review guidance.
In between updates, the screening process and current state of the data extraction is visible via the living review website. We searched five electronic databases, using the search methods previously described in our protocol. Searches on the arXiv computer science and dblp were conducted on full database dumps using the search functionality described by McGuinness and Schmidt. Originally, we planned to include a full literature search from the Web of Science Core Collection.
This reduced the Web of Science Core Collection publications to abstracts, which were added to the studies in the initial screening step.
The dataset, code and weights of trained models are available in Underlying data: Appendix C. This decision was made to facilitate continuous reference retrieval. Screening and data extraction were conducted as stated in the protocol. In short, we initially screened all retrieved publications using the Abstrackr tool. All abstracts were screened by two independent reviewers. Conflicting judgements were resolved by the authors who made the initial screening decisions.
Full texts screening was conducted in a similar manner to abstract screening but used our web application for living systematic reviews described in the following section. We previously developed a web application to automate reference retrieval for living review updates see Software availability 13 , to support both abstract and full text screening for review updates, and to manage the data extraction process throughout.
This web application is already in use by another living review. All extracted data are stored in a database. Figures and tables can be exported on a daily basis and the progress in between review updates is shared on our living review website. The full spreadsheet of items extracted from each included reference is available in the Underlying data. We automated the export of PDF reports for each included publication. Calculation of percentages, export of extracted text, and creation of figures was also automated.
All data and code are free to access. In the protocol we stated that data would be available via an OSF repository. Instead, the full review data are available via the Harvard Dataverse, as this repository allows us to keep an assigned DOI after updating the repository with new content for each iteration of this living review. We also stated that we would screen all publications from the Web of Science search. We added a data extraction item for the type of information which a publication mines e.
P, IC, O into the section of primary items of interest, and we moved the type of input and output format from primary to secondary items of interest. We decided not to speculate if a dataset is likely to be available in the future and chose instead to record if the dataset was available at the time when we tried to access it.
In this current version of the review we did not yet contact the authors of the included publications. This decision was made due to time constraints, however reaching out to authors is planned as part of the first update to this living review.
Our database searches identified 10, publications after duplicates were removed see Figure 2. We identified an additional 23 publications by screening the bibliographies of included publications, in addition to reviewing the tools contained in the SRToolbox.
For future review updates we will adapt the search strategies and conduct searches in sources such as the ACL. This iteration of the living review includes 53 publications, summarised in Table 1 in Underlying data Twelve of these were among the additional 23 publications.
In total, 79 publications were excluded at the full text screening stage, with the most common reason for exclusion being that a study did not fit target entities or target data.
In most cases, this was due to the text-types mined in the publications. Electronic health records and non-trial data were common, and we created a list of datasets that would be excluded in this category see more information in Underlying data: Appendix B Some publications addressed the right kind of text but were excluded for not mining entities of interest to this review. Millard, Flach and Higgins 17 and Marshall, Kuiper and Wallace 18 looked at risk of bias classification, which is beyond the scope of this review.
Luo et al. Rathbone et al. We classified this article as not having any original data extraction approach because it does not create any structured outputs specific to P, IC, or O. Malheiros et al. Similarly, Fabbri et al. Other systematic reviewing tasks that can benefit from automation but were excluded from this review are listed in Underlying data: Appendix B. Figure 3 shows aspects of the system architectures implemented in the included publications.
A short summary of these for each publication is provided in Table 1 in Underlying data. Although SVM is also binary classifier, it was assigned as separate category due to its popularity. This figure shows that there is no obvious choice of system architecture for this task. Results are divided into different categories of machine-learning and natural language processing approaches and coloured by the year of publication.
More than one architecture component per publication is possible. They are frequently used in studies published between and now. Rule-bases, including approaches using heuristics, wordlists, and regular expressions, were one of the earliest techniques used for data extraction in EBM literature.
It remains one of the most frequently used approaches to automation. Although used more frequently in the past, the five publications published between and now that use this approach combine it with conditional random fields CRF , 24 use it alone, 25 , 26 use it with SVM 27 or use it with other binary classifiers. Recurrent neural networks RNN , CNN, and LSTM networks require larger amounts of training data, but by using embeddings or pre-training algorithms based on unlabelled data they have become increasingly more interesting in fields such as data extraction for EBM, where high-quality training data are difficult and expensive to obtain.
Precision i. This is reflected in Figure 4 , which shows that at least one of these metrics was used in almost all of the 53 included publications. Real-life evaluations, such as the percentage of outputs needing human correction, or time saved per article, were reported by one publication, 30 and an evaluation as part of a wider screening system was done in another.
There were several approaches and justifications of using macro- or micro-averaged precision, recall, or F1 scores in the included publications. Micro or macro scores are computed in multi-class cases, and the final scores can have a difference if the classes in a dataset are imbalanced as is the case in most datasets used in the included studies for this review.
Micro and macro scores were reported by, 30 , 41 whereas 26 , 42 reported micro across documents, and macro across the classes. Micro scores were used by 41 for class-level results. Micro scores were also used by, 44 — 46 and were used in the evaluation script of. Most data extraction is carried out on abstracts See Table 1 in Underlying data , 86 and Table 5.
Abstracts are the most practical choice, due to the possibility of exporting them along with literature search results from databases such as MEDLINE. Descriptions of the benefits of using full texts for data extraction include having access to a more complete dataset, while the benefits of using titles include lower complexity for the data extraction task.
Figure 6 shows that RCTs are the most common study design texts used for data extraction in the included publications see also extended Table 1 in Underlying data This is not surprising, because systematic reviews of interventions are the most common type of systematic review, and they are usually focusing on evidence from RCTs. Systematic reviews of diagnostic test accuracy are less frequent, and only one included publication specifically focused on text and entities related to these studies, 48 while another mentioned diagnostic procedures among other fields of interest.
Commonly, randomized controlled trials RCT text was at least one of the target text types used in the included publications. Mining P, IC, and O elements is the most common task performed in the literature of systematic review semi- automation see Table 1 in Underlying data , 86 and Figure 7. However, some of the less-frequent data extraction targets in the literature can be categorised as sub-classes of a PICO. P, population; I, intervention; C, comparison; O, outcome.
Notably, seven publications annotated or worked with datasets that differentiated between intervention and control arms. Most data extraction approaches focused on recognising instances of entity or sentence classes, and a small number of publications went one step further to normalise to actual concepts. A total of 36 publications extracted at least one type of information at the entity level, while 32 publications used sentence level see Table 1 extended version in Underlying data We defined the entity level as any number of words that is shorter than a whole sentence, e.
Therefore, most systems described using, or were assumed to use, text files as input data. Eight included publications described using PDF files as input. Most publications mentioned only classification scores without specifying an output type.
We used a list of 17 items to investigate reproducibility, transparency, description of testing, data availability, and internal and external validity of the approaches in each publication.
The maximum and minimum number of items that were positively rated were 16 and 1, respectively, with a median of 10 see Table 1 in Underlying data Scores were added up and calculated based on the data provided in Appendix A see Underlying data 86 , using the sum and median functions integrated in Excel.
Publications from recent years showed a trend towards more complete and clear reporting. Publications published in recent years are increasingly reporting that they are using these benchmark datasets rather than creating and annotating their own corpora see 4 for more details.
Different types of pre-processing, with representative examples for usage and implementation, are listed in Table 1 below.
In the case of machine learning and neural networks, we looked for a description of hyperparameters and feature generation, and for the details of implementation e. Hyperparameters were rarely described in full, but if the framework e. For rule-based methods we looked for a description of how rules were derived, and for a list of full or representative rules given as examples. Where multiple data extraction approaches were described, we gave a positive rating if the best-performing approach was described.
Most publications provided descriptions of the dataset s used for training and evaluation. The size of each dataset, as well as the frequencies of classes within the data, were transparent and described for most included publications.
All datasets, along with a short description and availability of the data, are shown in Table 4. One, for example, applied their system to new, unlabelled data and reported that classifying the whole of PubMed takes around 20 hours using a graphics processing unit GPU.
Figure 9 shows that most of the included publications did not provide any source code. GitHub is the most popular platform for making code accessible. Some publications also provided links to notebooks on Google Colab, which is a cloud-based platform to develop and execute code online.
Two publications provided access to parts of the code, or access was restricted. A full list of code repositories from the included publications is available in Table 2. We rated this item as negative if only the performance scores were given, i.
In most publications a brief error analysis was common, for example discussions on representative examples for false negatives and false positives, 41 major error sources 63 or highlighting errors with respect to every entity class. A small number of publications did a real-life assessment, where the data extraction algorithm was applied to different, unlabelled, and often much larger datasets or tested while conducting actual systematic reviews.
Figure 10 shows the extent to which all basic metrics were reported in the included publications. When dealing with entity-level data extraction it can be challenging to define the quantity of true negative entities.
This is true especially if entities are labelled and extracted based on text chunks, because there can be many combinations of phrases and tokens that constitute an entity. For each included paper. Recall i.
Some machine-learning architectures need to convert text into features before performing classification. A feature can be, for example, the number of times that a certain word occurs, or the length of an abstract. The number of features used, e. Compiling and testing code from every publication is outside the scope of this review. Instead, in Figure 11 and Table 3 we recorded the publications where a web interface or finished application was available. Of those 17, datasets used in six publications were not publicly available, but in these cases, there were often overlaps of at least one author in the author teams, explaining facilitated access to data.
In total, we counted 36 unique corpora with labelled data. Table 4 shows a summary of the corpora, their size, classes, and cross-reference to known publications re-using each data set. Where available, we collected the corpora, provide a central link to all datasets, and will add datasets as they become available during the life span of this living review see Underlying data 86 , 87 below. When a dataset is made freely available without barriers i. Copyright issues surrounding data sharing were noted by, 48 therefore they shared the gold-standard annotations used as training or evaluation data and information on how to obtain the texts.
The following list is likely to be incomplete, due to non-available code and incomplete reporting in the included publications. The amount and types of data you collect, as well as the number of collaborators who will be extracting it, will dictate which extraction tools are best for your project.
Programs like Excel or Google Spreadsheets may be the best option for smaller or more straightforward projects, while systematic review software platforms can provide more robust support for larger or more complicated data. It is recommended that you pilot your data extraction tool, especially if you will code your data, to determine if fields should be added or clarified, or if the review team needs guidance in collecting and coding data.
Excel is the most basic tool for the management of the screening and data extraction stages of the systematic review process. Customized workbooks and spreadsheets can be designed for the review process. Covidence is a software platform built specifically for managing each step of a systematic review project, including data extraction. Read more about how Covidence can help you customize extraction tables and export your extracted data. RevMan is free software used to manage Cochrane reviews.
For more information on RevMan, including an explanation of how it may be used to extract and analyze data, watch Introduction to RevMan - a guided tour. It is also an open and searchable archive of systematic reviews and their data. Access the " Create an Extraction Form " section for more information. DistillerSR is a systematic review management software program, similar to Covidence. It guides reviewers in creating project-specific forms, extracting, and analyzing data.
JBI Sumari the Joanna Briggs Institute System for the United Management, Assessment and Review of Information is a systematic review software platform geared toward fields such as health, social sciences, and humanities. Among the other steps of a review project, it facilitates data extraction and data synthesis. View their short introductions to data extraction and analysis for more information. The SR Toolbox is a community-driven, searchable, web-based catalogue of tools that support the systematic review process across multiple domains.
The purpose of the meeting is to extract from the participants' detailed responses to these questions. The best tools for tackling Focus groups are:. This method of data collection encompasses the use of innovative methods to enhance participation to both individuals and groups.
Also under the primary category, it is a combination of Interviews and Focus Groups while collecting qualitative data. This method is key when addressing sensitive subjects.
The Combination Research method involves two or more data collection methods, for instance, interviews as well as questionnaires or a combination of semi-structured telephone interviews and focus groups.
The best tools for combination research are:. With Formplus, you can create your unique survey form. With options to change themes, font colour, font, font type, layout, width, and more, you can create an attractive survey form. The builder also gives you as many features as possible to choose from and you do not need to be a graphic designer to create a form.
Form Analytics, a feature in formplus helps you view the number of respondents, unique visits, total visits, abandonment rate, and average time spent before submission. Copy the link to your form and embed as an iframe which will automatically load as your website loads, or as a popup which opens once the respondent clicks on the link.
Embed the link on your Twitter page to give instant access to your followers. The geolocation feature on Formplus lets you ascertain where individual responses are coming. It utilises Google Maps to pinpoint the longitude and latitude of the respondent, to the nearest accuracy, along with the responses. This feature helps to conserve horizontal space as it allows you to put multiple options in one field.
This translates to including more information on the survey form. Formplus gives you a free plan with basic features you can use to collect online data. Create Online Questionnaire or Survey for Free. Input your survey title and use the form builder choice options to start creating your surveys. Use the choice option fields like single select, multiple select, checkbox, radio, and image choices to create your preferred multi-choice surveys online.
Do you want customers to rate any of your products or services delivery? Use the rating to allow survey respondents rate your products or services. This is an ideal quantitative research method of collecting data. Beautify your online questionnaire with Formplus Customisation features. You can;. Edit your survey questionnaire settings for your specific needs. Choose where you choose to store your files and responses.
Change the Email Notifications inventory and initiate an autoresponder message to all your survey questionnaire respondents. You can also transfer your forms to other users who can become form administrators. Share links of your survey questionnaire page with customers. You can start sharing your link to your survey questionnaire with your customers.
View your Responses to the Survey Questionnaire. Toggle with the presentation of your summary from the options. Whether as a single, table or cards. With online form builder analytics, a business can determine;. Try out Formplus today. You can start making your own surveys with the Formplus online survey builder. By applying these tips, you will definitely get the most out of your online surveys.
On the template, you can collect data to measure customer's satisfaction over key areas like the commodity purchase and the level of service they received.
It also gives insight as to which products the customer enjoyed, how often they buy such a product, and whether or not the customer is likely to recommend the product to a friend or acquaintance.
With this template, you would be able to measure, with accuracy, the ratio of male to female, age range and a number of unemployed persons in a particular country as well as obtain their personal details such as names and addresses. Respondents are also able to state their religious and political views about the country under review. Identifying this product or service and documenting how long the customer has used them. The overall satisfaction is measured as well as the delivery of the services.
The likelihood that the customer also recommends said product is also measured. The online questionnaire template houses the respondent's data as well as educational qualification to collect information to be used for academic research.
Respondents can also provide their gender, race, a field of study as well as present living conditions as prerequisite data for the research study. The template is a data sheet containing all the relevant information of a student. The student's name, home address, guardians name, a record of attendance as well as performance in school is well represented on this template.
This is a perfect data collection method to deploy for a school or an education organizations. Also included is a record for interaction with others as well as a space for a short comment on the overall performance and attitude of the student. This online interview consent form template allows interviewee sign off their consent to use the interview data for research or report for journalist.
With premium features like short text fields, upload, e-signature, etc. Ans: Combination Research. The best data collection method for a researcher for gathering qualitative data which generally is data relying on the feelings, opinions and beliefs of the respondents would be Combination Research. The reason why combination research is the best fit is that it encompasses the attributes of Interviews and Focus Groups.
It is also useful when gathering data that is sensitive in nature. It can be described as all-purpose quantitative data collection method. Above all, combination research improves the richness of data collected when compared with other data collection methods for qualitative data. The best data collection method a researcher can employ in gathering quantitative data which takes into consideration data that can be represented in numbers and figures that can be deduced mathematically is the Questionnaire.
These can be administered to a large number of respondents, while saving cost. For quantitative data that may be bulky or voluminous in nature, the use of a Questionnaire makes such data easy to visualize and analyze. Another key advantage of the Questionnaire is that it can be used to compare and contrast previous research work done to measure changes.
In mathematical and statistical analysis, data is defined as a collected group of information. Information, in this case, could be anything Google chrome extensions are necessary kits in the toolbox of academic researchers. This is because they help make the research processes To make sense of this raw information for your business, The qualitative data collection process may be assessed through two different points of view—that of the questionnaire and the respondents.
Pricing Templates Features Login Sign up. Sign up on Formplus Builder to create your preferred online surveys or questionnaire for data collection. You don't need to be tech-savvy! Start creating quality questionnaires with Formplus. Types of Data Collection Before broaching the subject of the various types of data collection. Primary Data Collection Primary data collection by definition is the gathering of raw data collected at the source.
Qualitative Research Method The qualitative research methods of data collection do not involve the collection of data that involves numbers or a need to be deduced through a mathematical calculation, rather it is based on the non-quantifiable elements like the feeling or emotion of the researcher.
Quantitative Method Quantitative methods are presented in numbers and require a mathematical calculation to deduce. Read Also: 15 Reasons to Choose Quantitative over Qualitative Research Use Formplus as a Primary Data Collection Tool Secondary Data Collection Secondary data collection, on the other hand, is referred to as the gathering of second-hand data collected by an individual who is not the original user.
Walking you through them, here are a few reasons; Integrity of the Research A key reason for collecting data, be it through quantitative or qualitative methods is to ensure that the integrity of the research question is indeed maintained. Reduce the likelihood of errors The correct use of appropriate data collection of methods reduces the likelihood of errors consistent with the results.
Decision Making To minimize the risk of errors in decision-making, it is important that accurate data is collected so that the researcher doesn't make uninformed decisions. Save Cost and Time Data collection saves the researcher time and funds that would otherwise be misspent without a deeper understanding of the topic or subject matter.
What is a Data Collection Tool? Structured Interviews - Simply put, it is a verbally administered questionnaire. In terms of depth, it is surface level and is usually completed within a short period. For speed and efficiency, it is highly recommendable, but it lacks depth. Semi-structured Interviews - In this method, there subsist several key questions which cover the scope of the areas to be explored.
It allows a little more leeway for the researcher to explore the subject matter. Unstructured Interviews - It is an in-depth interview that allows the researcher to collect a wide range of information with a purpose.
An advantage of this method is the freedom it gives a researcher to combine structure with flexibility even though it is more time-consuming.
Pros In-depth information Freedom of flexibility Accurate data. Cons Time-consuming Expensive to collect. What are the best Data Collection Tools for Interviews? Audio Recorder An audio recorder is used for recording sound on disc, tape, or film.
Digital Camera An advantage of a digital camera is that it can be used for transmitting those images to a monitor screen when the need arises. Camcorder A camcorder is used for collecting data through interviews.
Pros Can be administered in large numbers and is cost-effective. It can be used to compare and contrast previous research to measure change. Easy to visualize and analyze. Questionnaires offer actionable data. Respondent identity is protected. Questionnaires can cover all areas of a topic.
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