How to extract data from your paper for systemic review — Pubrica

Introduction:

Researchers in evidence-based medicine are overwhelmed by the volume of primary research papers, both old and modern. Since it is currently impractical to scan for appropriate data with accuracy, support for the early stages of the systematic review phase — searching and screening studies for eligibility — is needed. Not only could better automatic data extraction help with the stage of analysis known as “data extraction,” but it could also help with other aspects of the review process.

Systematic review (semi)automation research lies at the intersection between evidence-based medicine and computer science. Besides the advancement in computing power and storage space, computers’ capacity to serve humans grows. Data extraction for systematic analysis is a time-consuming process (2). It opens up possibilities for sophisticated machines to assist.

Work Flow and study design:

Two critics will separately screen both titles and abstracts. Any discrepancies in judgement would be addressed and, if possible, overcome with the assistance of a third reviewer. The evaluation process for complete texts would be the same, a single reviewer will extract data, and a random 10% selection from each reviewer will be reviewed separately. We plan to contact the writers of reports for confirmation or additional material if necessary. We will provide a cross-sectional overview of the data from our searches in the case study and any published update. The analysis will include the features of each reviewed method or tool, as well as a summary of our outcomes. In addition, we will evaluate the quality of reporting at the publication level.

Eligibility criteria:

1. Eligible papers

• Full-text articles describing an initial natural language processing method to extract data for structured reviewing activities will be included. The Extended data contains data areas of concern adapted from the Cochrane Handbook for Systematic Reviews of Interventions. The whole spectrum of natural language processing (NLP) techniques includes regular expressions, rule-based structures, machine learning, and deep artificialnetworks.

2. Ineligible papers

We will exclude papers reporting:

· image editing and downloading biomedical data from PDF files without the use of natural language processing (NLP), including data retrieval from graphs;

· any study that focuses merely on protocol planning, synthesis of previously extracted data, write-up, text pre-processing, and dissemination will be disqualified;

· Methods or tools that do not use natural language processing and instead focus on administrative interfaces, document storage, databases, or version control.

Key items for data extraction:

Primary

Machine learning approaches used

Reported performance metrics used for evaluation

Type of data

· Scope: full text, abstract, or conference proceedings

· Study type: randomized clinical experiment, cohort, and case-control

· Input data format: For example, data imported as standardized results of literature searches (e.g. RIS), APIs, or data imported from PDF or text files.

Secondary:

· Data mining granularity: Does the machine retrieve individual entities, words, or whole sections of text?

Other indicators that have been published, such as the effect on systemic review processes (e.g. time saved during data extraction).

Future work:

According to a systematic analysis, information retrieval technology positively affects physicians in decision-making — the need for new methods to report on and searching for organized data in written literature. The use of an automated knowledge extraction process to retrieve data elements can aid comprehensive reviewers and, in the long run, simplify the searching and data extraction steps.

Conclusions:

Data extraction approaches may serve as checks for currently conducted manual data extraction, then serve to verify manual data extraction achieved by a single reviewer, then become the primary source for data element extraction that a person will check, and finally full data extraction to allow live systematic reviews.

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