Automatic data extraction to support meta-analysis statistical analysis: a case study on breast cancer

Abstract

Background Meta-analyses aggregate results of different clinical studies to assess the effectiveness of a treatment. Despite their importance, meta-analyses are time-consuming and labor-intensive as they involve reading hundreds of research articles and extracting data. The number of research articles is increasing rapidly and most meta-analyses are outdated shortly after publication as new evidence has not been included. Automatic extraction of data from research articles can expedite the meta-analysis process and allow for automatic updates when new results become available. In this study, we propose a system for automatically extracting data from research abstracts and performing statistical analysis. Materials and methods Our corpus consists of 1011 PubMed abstracts of breast cancer randomized controlled trials annotated with the core elements of clinical trials: Participants, Intervention, Control, and Outcomes (PICO). We proposed a BERT-based named entity recognition (NER) model to identify PICO information from research abstracts. After extracting the PICO information, we parse numeric outcomes to identify the number of patients having certain outcomes for statistical analysis. Results The NER model extracted PICO elements with relatively high accuracy, achieving F1-scores greater than 0.80 in most entities. We assessed the performance of the proposed system by reproducing the results of an existing meta-analysis. The data extraction step achieved high accuracy, however the statistical analysis step achieved low performance because abstracts sometimes lack all the required information. Conclusion We proposed a system for automatically extracting data from research abstracts and performing statistical analysis. We evaluated the performance of the system by reproducing an existing meta-analysis and the system achieved a relatively good performance, though more substantiation is required.

Comparative Topic Modeling for Determinants of Divergent Report Results Applied to Macular Degeneration Studies

“…Various examples exist in the literature of capturing information from papers via text mining for performing MA. Mutinda et al [6] propose a named entity recognition (NER) model to identify participants, intervention, control, and outcomes (PICO) from clinical trials. In another domain, the Neurosynth Corpus [7] is created via text mining, namely by tying keyword mentions to fMRI images from a large sample of studies to assemble a library of neuroimaging representations associated with cognitive, physical, and emotional states.…”

Surgical procedure long terms recognition from Chinese literature incorporating structural feature

“…NER is widely used in Chinese medical texts, involving electronic medical records [ 18 , 19 ], traditional Chinese medicine texts [ 20 , 21 ], clinical guidelines [ 22 ], disease subtypes [ 23 ], admission notes [ 24 ], PICO [ 25 ] disease and plant name [ 26 ], e. tc. Most approaches focus on feature extraction either by rule and dictionary-based means or by machine learning and deep learning means [ 27 , 28 ].…”

Methods for using Bing's AI‐powered search engine for data extraction for a systematic review

“…1 NLP algorithms can identify and extract key information such as study characteristics, outcomes, and statistical data from research papers. 1,9 By harnessing the power of NLP, systematic reviewers may save substantial time and resources, accelerate the review process, and enhance the overall quality and reliability of the extracted data. 9,10 One of these models which has been given a large amount of public attention and may provide opportunities to enhance the data extraction process is ChatGPT.…”

“…1,9 By harnessing the power of NLP, systematic reviewers may save substantial time and resources, accelerate the review process, and enhance the overall quality and reliability of the extracted data. 9,10 One of these models which has been given a large amount of public attention and may provide opportunities to enhance the data extraction process is ChatGPT. [11][12][13] However, despite the potential opportunities there have been many concerns regarding how and when ChatGPT should be used to facilitate systematic reviews.…”

“…By leveraging NLP algorithms, researchers can automate the extraction process, significantly reducing the manual effort required 1 . NLP algorithms can identify and extract key information such as study characteristics, outcomes, and statistical data from research papers 1,9 . By harnessing the power of NLP, systematic reviewers may save substantial time and resources, accelerate the review process, and enhance the overall quality and reliability of the extracted data 9,10 .…”