In general, many studies have a high risk of selection bias because the gold standards used in the respective studies were not randomly selected. The risk of performance bias is also likely to be high because the investigators were not blinded.

For the systems that used rule-based approaches, it was unclear whether the gold standard was used to train the rules or if there were a separate training set. The risk of attrition bias is unclear based on the study design of these non-randomized studies evaluating the performance of NLP methods.

Lastly, the risk of reporting bias is unclear because of the lack of protocols in the development, implementation, and evaluation of NLP methods. Participants — Sixteen studies explored the extraction of the number of participants [ 12 , 13 , 16 — 20 , 23 , 24 , 28 — 30 , 32 , 39 ], their age [ 24 , 29 , 39 , 41 ], sex [ 24 , 39 ], ethnicity [ 41 ], country [ 24 , 39 ], comorbidities [ 21 ], spectrum of presenting symptoms, current treatments, and recruiting centers [ 21 , 24 , 28 , 29 , 32 , 41 ], and date of study [ 39 ].

Among them, only six studies [ 28 — 30 , 32 , 39 , 41 ] extracted data elements as opposed to highlighting the sentence containing the data element. Unfortunately, each of these studies used a different corpus of reports, which makes direct comparisons impossible.

Intervention — Thirteen studies explored the extraction of interventions [ 12 , 13 , 16 — 20 , 22 , 24 , 28 , 34 , 39 , 40 ], intervention groups [ 34 , 35 ], and intervention details for replication if feasible [ 36 ].

Of these, only six studies [ 28 , 34 — 36 , 39 , 40 ] extracted intervention elements. Unfortunately again, each of these studies used a different corpus.

For example, Kiritchenko et al. Outcomes and comparisons — Fourteen studies also explored the extraction of outcomes and time points of collection and reporting [ 12 , 13 , 16 — 20 , 24 , 25 , 28 , 34 — 36 , 40 ] and extraction of comparisons [ 12 , 16 , 22 , 23 ].

Of these, only six studies [ 28 , 34 — 36 , 40 ] extracted the actual data elements. For example, De Bruijn et al. Results — Two studies [ 36 , 40 ] extracted sample size data element from full text on two different data sets.

Interpretation — Three studies explored extraction of overall evidence [ 26 , 42 ] and external validity of trial findings [ 25 ]. However, all these studies only highlighted sentences containing the data elements relevant to interpretation.

Objectives — Two studies [ 24 , 25 ] explored the extraction of research questions and hypotheses. However, both these studies only highlighted sentences containing the data elements relevant to interpretation.

Methods — Twelve studies explored the extraction of the study design [ 13 , 18 , 20 , 24 ], study duration [ 12 , 29 , 40 ], randomization method [ 25 ], participant flow [ 36 , 37 , 40 ], and risk of bias assessment [ 27 ]. Of these, only four studies [ 29 , 36 , 37 , 40 ] extracted the corresponding data elements from text using different sets of corpora.

For example, Restificar et al. Previous reviews on the automation of systematic review processes describe technologies for automating the overall process or other steps.

Tsafnat et al. Here, we focus on data extraction. None of the existing reviews [ 43 — 47 ] focus on the data extraction step. For example, Tsafnat et al. In comparison, we identified 26 studies and critically examined their contribution in relation to all the data elements that need to be extracted to fully support the data extraction step.

Thomas et al. The authors also pointed out the potential of these technologies to assist at various stages of the systematic review. Slaughter et al. The authors mentioned the need for development of new tools for reporting on and searching for structured data from clinical trials.

They mentioned text extraction algorithms for evaluating risk of bias and evidence synthesis but remain limited to one particular method for extraction of PICO elements.

Most natural language processing research has focused on reducing the workload for the screening step of systematic reviews Step 3.

Wallace et al. Jonnalagadda et al. Cohen et al. Choong et al. Adeva et al. Shemilt et al. Among the 26 studies included in this systematic review, only three of them use a common corpus, namely medical abstracts from the PIBOSO corpus.

Unfortunately, even that corpus facilitates only classification of sentences into whether they contain one of the data elements corresponding to the PIBOSO categories.

No two other studies shared the same gold standard or dataset for evaluation. This limitation made it impossible for us to compare and assess the relative significance of the reported accuracy measures.

Few data elements, which are also relatively straightforward to extract automatically, such as the total number of participants 14 overall and 5 for extracting the actual data elements , have a relatively higher number of studies aiming towards extracting the same data element. This is not the case with other data elements.

There are 27 out of 52 potential data elements that have not been explored for automated extraction, even if for highlighting the sentences containing them; seven more data elements were explored just by one study.

The current state of informatics research for data extraction is exploratory, and multiple studies need to be conducted using the same gold standard and on the extraction of the same data elements for effective comparison.

Our study has limitations. First, there is a possibility that data extraction algorithms were not published in journals or that our search might have missed them. We sought to minimize this limitation by searching in multiple bibliographic databases, including PubMed, IEEExplore, and ACM Digital Library.

However, investigators may have also failed to publish algorithms that had lower F-scores than were previously reported, which we would not have captured. Second, we did not publish a protocol a priori, and our initial findings may have influenced our methods.

However, we performed key steps, including screening, full-text review, and data extraction in duplicate to minimize potential bias in our systematic review. A systematic review of 26 studies concluded that information-retrieval technology produces positive impact on physicians in terms of decision enhancement, learning, recall, reassurance, and confirmation [ 62 ].

The authors mention the need for development of new tools for reporting on and searching for structured data from published literature. Automated information extraction framework that extract data elements have the potential to assist the systematic reviewers and to eventually automate the screening and data extraction steps.

Medical science is currently witnessing a rapid pace at which medical knowledge is being created—75 clinical trials a day [ 66 ]. Evidence-based medicine [ 67 ] requires clinicians to keep up with published scientific studies and use them at the point of care.

However, it has been shown that it is practically impossible to do that even within a narrow specialty [ 68 ]. A critical barrier is that finding relevant information, which may be located in several documents, takes an amount of time and cognitive effort that is incompatible with the busy clinical workflow [ 69 , 70 ].

Rapid systematic reviews using automation technologies will enable clinicians with up-to-date and systematic summaries of the latest evidence. Our systematic review describes previously reported methods to identify sentences containing some of the data elements for systematic reviews and only a few studies that have reported methods to extract these data elements.

However, most of the data elements that would need to be considered for systematic reviews have been insufficiently explored to date, which identifies a major scope for future work.

We hope that these automated extraction approaches might first act as checks for manual data extraction currently performed in duplicate; then serve to validate manual data extraction done by a single reviewer; then become the primary source for data element extraction that would be validated by a human; and eventually completely automate data extraction to enable living systematic reviews.

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Summerscales RL, Argamon S, Hupert J, Schwartz A. Identifying treatments, groups, and outcomes in medical abstracts. The Sixth Midwest Computational Linguistics Colloquium MCLC Most organizations still rely on manual data extraction to retrieve pertinent data from unstructured sources for business use.

However, the process is not only resource intensive but also has several downsides that can impact the business growth. Manual data extraction has a high error rate compared to automated tools. The error rate can be attributed to:. These issues can cause serious delays in various business processes, especially the ones dealing with high volumes of data on a daily basis.

Another key issue with manual data scraping is the cost and effort that goes into verifying the extracted data. Regular data quality checks are an essential step in the data extraction process. They prevent issues arising due to inaccurate data being sent forward for analysis. Quality verification with manual data process requires hiring additional personnel that spend hours ensuring the accuracy of the entered data by going over each file and checking for errors or inconsistencies.

This ends up costing the organization both time and money. With data scraping tools that include automation, this can be done easily at a lower cost and in less time. And finally, manual data extraction can divert employees from completing more value-adding tasks.

This can be detrimental to an organization and keep it from achieving its strategic objectives that require timely delivery of specific data. Usually, organizations outsource the data extraction process when the source s are unstructured, believing that by delegating the task to third party organizations they can accelerate their data-to-insights journey.

While that might be feasible, outsourcing data extraction can result in delays, slowing productivity. Fortunately, rapid technological advancements have made it possible to automate the process through data extraction tools. Users can easily extract relevant information from various documents, clean and validate the data, and deliver it to its desired destination.

Employees can easily analyze and use the extracted data timely decision making. The automation capability of these tools ensure faster and error-free data extraction. You can build extraction logic for incoming documents and apply the logic to other unstructured documents with a similar layout.

This allows the organization to dedicate their resources to meaningful tasks rather than manual, repetitive work that can cause delays and generate error-prone results. Inaccurate data can result in a poor customer experience. Intelligent document processing allows you to extract information quickly and accurately.

Manual document processing is expensive and time consuming. You need to allocate resources to process large volume documents, reducing agility. Moreover, your employees are doing manual stare and compare tasks, reducing worker morale.

Intelligent document processing helps you overcome these challenges by automating the classification, extraction, and analysis of data. This allows you to allocate resources to high-value tasks, and enable faster decision cycles. Legacy OCR solution is often difficult to scale especially with variability in document types and volumes.

Intelligent document processing helps you scale quickly with large volumes and different document types. Incomplete loan packages, tax forms, paystubs, and other missing data found during the underwriting process, often creates more work and increases potential for bad loans which is costly and risky.

Using AWS intelligent document processing, you can extract the most important information from mortgage applications and accelerate response times to customers. Data extraction can be particularly challenging in the insurance sector given the varying document layouts and formats for quotes, insurance forms, claims, and receipts.

Using AWS Intelligent document processing, you can quickly extract relevant information such case ID, property address quickly and accurately.

To help your public sector organizations make faster and more accurate decisions Intelligent document processing on AWS is here to help.

You can process invoices, taxes, benefit claims, licenses, and financial records to extract necessary data points for a decision to be made.

Learn how Black Knight drives efficiency and delivers cost savings ». Inawisdom automates document processing using AWS AI ». Processing documents, such as agreements, court filings, or legal dockets, is a difficult task for legal teams. Contractual documents are often in non-standardized formats.

The typical workflow for reviewing legal filings involves loading, reading, and extracting case number, parties involved or legal entities from the documents, requiring hours of manual effort.