Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review

Abdelkader, Wael; Navarro, Tamara; Parrish, Rick; Cotoi, Chris; Germini, Federico; Iorio, Alfonso; Lokker, Cynthia

doi:10.2196/30401

Cited by 22 publications

(19 citation statements)

References 63 publications

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“…A selection of applications of NLP for information extraction in biomedical/clinical text include: tagging [ 43 ], normalization and linking of biomedical entities [ 14 ], relation extraction [ 44 ], and event extraction [ 45 ]. To our knowledge, NLP has not been used for preparing narrative or systematic reviews although several reports compared ML approaches to guide initial selection of the literature corpus rather than using ML for downstream information extraction from the retrieved documents [ 46 , 47 , 48 ]. A PubMed knowledge graph connected (1) authors, their educational background, funding data, and affiliation history with (2) the diseases, drugs, genes, species, and mutations identified in their corpus of abstracts [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

Identifying and Analyzing Topic Clusters in a Nutri-, Food-, and Diet-Proteomic Corpus Using Machine Reading

Monteiro

Morine²,

Ued

et al. 2023

Nutrients

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Nutrition affects the early stages of disease development, but the mechanisms remain poorly understood. High-throughput proteomic methods are being used to generate data and information on the effects of nutrients, foods, and diets on health and disease processes. In this report, a novel machine reading pipeline was used to identify all articles and abstracts on proteomics, diet, food, and nutrition in humans. The resulting proteomic corpus was further analyzed to produce seven clusters of “thematic” content defined as documents that have similar word content. Examples of publications from several of these clusters were then described in a similar way to a typical descriptive review.

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Section: Discussionmentioning

confidence: 99%

Identifying and Analyzing Topic Clusters in a Nutri-, Food-, and Diet-Proteomic Corpus Using Machine Reading

Monteiro

Morine²,

Ued

et al. 2023

Nutrients

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“…BERT is considered the state-of-the-art model for NLP. To our knowledge, this is the first experiment to investigate the use of PTLMs in the identification of high-quality articles from the biomedical literature [ 51 ]. Our study leverages a large data set of over 150,000 citations that have been manually tagged by experienced research associates, making it one of the few reliable sources for training machine learning models to identify high-quality clinical literature [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Approach to Refine the Identification of High-Quality Clinical Research Articles From the Biomedical Literature: Protocol for Algorithm Development and Validation

Abdelkader¹,

Navarro²,

Parrish³

et al. 2021

JMIR Res Protoc

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Background A barrier to practicing evidence-based medicine is the rapidly increasing body of biomedical literature. Use of method terms to limit the search can help reduce the burden of screening articles for clinical relevance; however, such terms are limited by their partial dependence on indexing terms and usually produce low precision, especially when high sensitivity is required. Machine learning has been applied to the identification of high-quality literature with the potential to achieve high precision without sacrificing sensitivity. The use of artificial intelligence has shown promise to improve the efficiency of identifying sound evidence. Objective The primary objective of this research is to derive and validate deep learning machine models using iterations of Bidirectional Encoder Representations from Transformers (BERT) to retrieve high-quality, high-relevance evidence for clinical consideration from the biomedical literature. Methods Using the HuggingFace Transformers library, we will experiment with variations of BERT models, including BERT, BioBERT, BlueBERT, and PubMedBERT, to determine which have the best performance in article identification based on quality criteria. Our experiments will utilize a large data set of over 150,000 PubMed citations from 2012 to 2020 that have been manually labeled based on their methodological rigor for clinical use. We will evaluate and report on the performance of the classifiers in categorizing articles based on their likelihood of meeting quality criteria. We will report fine-tuning hyperparameters for each model, as well as their performance metrics, including recall (sensitivity), specificity, precision, accuracy, F-score, the number of articles that need to be read before finding one that is positive (meets criteria), and classification probability scores. Results Initial model development is underway, with further development planned for early 2022. Performance testing is expected to star in February 2022. Results will be published in 2022. Conclusions The experiments will aim to improve the precision of retrieving high-quality articles by applying a machine learning classifier to PubMed searching. International Registered Report Identifier (IRRID) DERR1-10.2196/29398

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“…The summary measures used in this analysis were the number and frequency of occurrence of the themes identified by the reviewers. Owing to the heterogeneity in the population, index method [ 8 ], and outcomes, we did not perform a quantitative synthesis of the results.…”

Section: Methodsmentioning

confidence: 99%

Research and Application of Artificial Intelligence Based on Electronic Health Records of Patients With Cancer: Systematic Review

Yang¹,

Mu²,

Peng³

et al. 2022

JMIR Med Inform

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Background With the accumulation of electronic health records and the development of artificial intelligence, patients with cancer urgently need new evidence of more personalized clinical and demographic characteristics and more sophisticated treatment and prevention strategies. However, no research has systematically analyzed the application and significance of artificial intelligence based on electronic health records in cancer care. Objective The aim of this study was to conduct a review to introduce the current state and limitations of artificial intelligence based on electronic health records of patients with cancer and to summarize the performance of artificial intelligence in mining electronic health records and its impact on cancer care. Methods Three databases were systematically searched to retrieve potentially relevant papers published from January 2009 to October 2020. Four principal reviewers assessed the quality of the papers and reviewed them for eligibility based on the inclusion criteria in the extracted data. The summary measures used in this analysis were the number and frequency of occurrence of the themes. Results Of the 1034 papers considered, 148 papers met the inclusion criteria. Cancer care, especially cancers of female organs and digestive organs, could benefit from artificial intelligence based on electronic health records through cancer emergencies and prognostic estimates, cancer diagnosis and prediction, tumor stage detection, cancer case detection, and treatment pattern recognition. The models can always achieve an area under the curve of 0.7. Ensemble methods and deep learning are on the rise. In addition, electronic medical records in the existing studies are mainly in English and from private institutional databases. Conclusions Artificial intelligence based on electronic health records performed well and could be useful for cancer care. Improving the performance of artificial intelligence can help patients receive more scientific-based and accurate treatments. There is a need for the development of new methods and electronic health record data sharing and for increased passion and support from cancer specialists.

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Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review

Cited by 22 publications

References 63 publications

Identifying and Analyzing Topic Clusters in a Nutri-, Food-, and Diet-Proteomic Corpus Using Machine Reading

Identifying and Analyzing Topic Clusters in a Nutri-, Food-, and Diet-Proteomic Corpus Using Machine Reading

A Deep Learning Approach to Refine the Identification of High-Quality Clinical Research Articles From the Biomedical Literature: Protocol for Algorithm Development and Validation

Research and Application of Artificial Intelligence Based on Electronic Health Records of Patients With Cancer: Systematic Review

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