Copyright © 2021 pubrica. All rights reserved 1

How to Extract Data from your Paper for

Systemic Review

Dr. Nancy Agnes, Head, Technical Operations, Pubrica, sales@pubrica.com

In-brief

Data should be extracted based on previously

identified interventions and outcomes developed

during the formulation of the study topic,

inclusion/exclusion requirements, and search

procedure. It should not be challenging to classify

the data elements that need to be retrieved from

each included sample if those phases have been

completed properly. To analyze and assess findings,

extract data from related studies. It is important to

use sound data collection techniques when the data

is being collected (1)

. Data processing can begin as

soon as you begin collecting data, and it can even

determine which data types you retain.

I. 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. In this domain, tools and methods are often

based on automating data processing relevant to the

PICO framework (Population, Intervention,

Comparator, and Outcome).

Table 1 A summary of included extraction methods and their evaluation

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II. 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 (3)

.

III. 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.

Papers must detail the whole process of

implementing and evaluating a system.

The data used for mining in the included articles

must be abstracts, conference proceedings, full

texts, or portions of full texts from randomized

clinical experiments, comparative cohort studies,

Copyright © 2021 pubrica. All rights reserved 3

or case management articles in the form of

abstracts, conference proceedings, full texts, or

parts of full texts.

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; or

• All articles relating to electronic health records

or genomic data mining may be disqualified.

IV. 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.

Output format: The format in which the data is

exported after extraction is a text file.

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)(4)

.

V. LIMITATIONS

First, there's a chance that data extraction algorithms

haven't been published in journals or that our search

has missed them. We searched several bibliographic

databases, including PubMed, IEEExplore, and the

ACM Digital Library, to overcome this limit. Second,

we did not publish a protocol ahead of time, and our

preliminary results may have affected our procedures.

To eliminate potential bias in our systematic analysis,

we duplicated main steps such as sampling, full-text

review, and data extraction.

VI. 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 (5)

.

VII. CONCLUSIONS

The studies have described methods to extract these

data elements, so data extraction for systematic

reviews outlines previously reported methods to

categorize sentences containing some of the data

extraction elements. 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(6)

.

REFERENCES

1. Splieth, Christian H., et al. "How to intervene in

the caries process: proximal caries in adolescents

and adults—a systematic review and meta-

analysis." Clinical oral investigations 24.5

(2020): 1623-1636.

2. Van Rensburg, Dina C. Christa Janse, et al.

"How to manage travel fatigue and jet lag in

athletes? A systematic review of

interventions." British journal of sports

medicine 54.16 (2020): 960-968.

3. Miake-Lye, Isomi M., et al. "Unpacking

organizational readiness for change: an updated

systematic review and content analysis of

assessments." BMC health services research 20.1

(2020): 106.

4. Karunananthan, Sathya, et al. "PROTOCOL:

When and how to replicate systematic

reviews." Campbell Systematic Reviews 16.2

(2020): e1087.

5. Muka, Taulant, et al. "A 24-step guide on how to

design, conduct, and successfully publish a

systematic review and meta-analysis in medical

research." European journal of

epidemiology 35.1 (2020): 49-60.

6. Haddaway, N. R., Bethel, A., Dicks, L. V.,

Koricheva, J., Macura, B., Petrokofsky, G., ... &

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Stewart, G. B. (2020). Eight problems with

literature reviews and how to fix them. Nature

Ecology & Evolution, 1-8.