AUTOMETA: Automatic Meta-Analysis System Employing Natural Language Processing

Mutinda, Faith W.; Yada, Shuntaro; Wakamiya, Shoko; Aramaki, Eiji

doi:10.3233/shti220150

Cited by 3 publications

(1 citation statement)

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“…Recent embedding-based methods such as BERT have made it easier to study negation detection [181] and text similarity [173,174]. Text similarity has also been studied to identify semantically similar concepts [175], similar patients [177], or to detect redundancy in clinical texts [172,176].…”

Section: Context Analysismentioning

confidence: 99%

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

Bazoge,

Morin,

Daille

et al. 2023

JMIR Med Inform

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Background In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. Objective The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. Methods This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. Results We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%). Conclusions CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.

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Section: Context Analysismentioning

confidence: 99%

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

Bazoge,

Morin,

Daille

et al. 2023

JMIR Med Inform

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Evaluation of RMES, an Automated Software Tool Utilizing AI, for Literature Screening with Reference to Published Systematic Reviews as Case-Studies: Development and Usability Study

Sugiura,

Saegusa,

Jin

et al. 2024

JMIR Form Res

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Background Systematic reviews and meta-analyses are important to evidence-based medicine, but the information retrieval and literature screening procedures are burdensome tasks. Rapid Medical Evidence Synthesis (RMES; Deloitte Tohmatsu Risk Advisory LLC) is a software designed to support information retrieval, literature screening, and data extraction for evidence-based medicine. Objective This study aimed to evaluate the accuracy of RMES for literature screening with reference to published systematic reviews. Methods We used RMES to automatically screen the titles and abstracts of PubMed-indexed articles included in 12 systematic reviews across 6 medical fields, by applying 4 filters: (1) study type; (2) study type + disease; (3) study type + intervention; and (4) study type + disease + intervention. We determined the numbers of articles correctly included by each filter relative to those included by the authors of each systematic review. Only PubMed-indexed articles were assessed. Results Across the 12 reviews, the number of articles analyzed by RMES ranged from 46 to 5612. The number of PubMed-cited articles included in the reviews ranged from 4 to 47. The median (range) percentage of articles correctly labeled by RMES using filters 1-4 were: 80.9% (57.1%-100%), 65.2% (34.1%-81.8%), 70.5% (0%-100%), and 58.6% (0%-81.8%), respectively. Conclusions This study demonstrated good performance and accuracy of RMES for the initial screening of the titles and abstracts of articles for use in systematic reviews. RMES has the potential to reduce the workload involved in the initial screening of published studies.

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Evaluation of RMES, an Automated Software Tool Utilizing AI, for Literature Screening with Reference to Published Systematic Reviews as Case-Studies: Development and Usability Study (Preprint)

Sugiura,

Saegusa,

Jin

et al. 2023

Preprint

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BACKGROUND Systematic reviews and meta-analyses are important to evidence-based medicine, but the information retrieval and literature screening procedures are burdensome tasks. Rapid Medical Evidence Synthesis (RMES; Deloitte Tohmatsu Risk Advisory LLC) is a software designed to support information retrieval, literature screening, and data extraction for evidence-based medicine. OBJECTIVE This study aimed to evaluate the accuracy of RMES for literature screening with reference to published systematic reviews. METHODS We used RMES to automatically screen the titles and abstracts of PubMed-indexed articles included in 12 systematic reviews across 6 medical fields, by applying 4 filters: (1) study type; (2) study type + disease; (3) study type + intervention; and (4) study type + disease + intervention. We determined the numbers of articles correctly included by each filter relative to those included by the authors of each systematic review. Only PubMed-indexed articles were assessed. RESULTS Across the 12 reviews, the number of articles analyzed by RMES ranged from 46 to 5612. The number of PubMed-cited articles included in the reviews ranged from 4 to 47. The median (range) percentage of articles correctly labeled by RMES using filters 1-4 were: 80.9% (57.1%-100%), 65.2% (34.1%-81.8%), 70.5% (0%-100%), and 58.6% (0%-81.8%), respectively. CONCLUSIONS This study demonstrated good performance and accuracy of RMES for the initial screening of the titles and abstracts of articles for use in systematic reviews. RMES has the potential to reduce the workload involved in the initial screening of published studies.

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AUTOMETA: Automatic Meta-Analysis System Employing Natural Language Processing

Cited by 3 publications

References 0 publications

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

Evaluation of RMES, an Automated Software Tool Utilizing AI, for Literature Screening with Reference to Published Systematic Reviews as Case-Studies: Development and Usability Study

Evaluation of RMES, an Automated Software Tool Utilizing AI, for Literature Screening with Reference to Published Systematic Reviews as Case-Studies: Development and Usability Study (Preprint)

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