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Oliveira Dos Santos Á, Sergio da Silva E, Machado Couto L, Valadares Labanca Reis G, Silva Belo V. The use of artificial intelligence for automating or semi-automating biomedical literature analyses: a scoping review. J Biomed Inform 2023; 142:104389. [PMID: 37187321 DOI: 10.1016/j.jbi.2023.104389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/11/2023] [Accepted: 05/08/2023] [Indexed: 05/17/2023]
Abstract
OBJECTIVE Evidence-based medicine (EBM) is a decision-making process based on the conscious and judicious use of the best available scientific evidence. However, the exponential increase in the amount of information currently available likely exceeds the capacity of human-only analysis. In this context, artificial intelligence (AI) and its branches such as machine learning (ML) can be used to facilitate human efforts in analyzing the literature to foster EBM. The present scoping review aimed to examine the use of AI in the automation of biomedical literature survey and analysis with a view to establishing the state-of-the-art and identifying knowledge gaps. MATERIALS AND METHODS Comprehensive searches of the main databases were performed for articles published up to June 2022 and studies were selected according to inclusion and exclusion criteria. Data were extracted from the included articles and the findings categorized. RESULTS The total number of records retrieved from the databases was 12,145, of which 273 were included in the review. Classification of the studies according to the use of AI in evaluating the biomedical literature revealed three main application groups, namely assembly of scientific evidence (n=127; 47%), mining the biomedical literature (n=112; 41%) and quality analysis (n=34; 12%). Most studies addressed the preparation of systematic reviews, while articles focusing on the development of guidelines and evidence synthesis were the least frequent. The biggest knowledge gap was identified within the quality analysis group, particularly regarding methods and tools that assess the strength of recommendation and consistency of evidence. CONCLUSION Our review shows that, despite significant progress in the automation of biomedical literature surveys and analyses in recent years, intense research is needed to fill knowledge gaps on more difficult aspects of ML, deep learning and natural language processing, and to consolidate the use of automation by end-users (biomedical researchers and healthcare professionals).
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Affiliation(s)
| | - Eduardo Sergio da Silva
- Federal University of São João del-Rei, Campus Centro-Oeste Dona Lindu, Divinópolis, Minas Gerais, Brazil.
| | - Letícia Machado Couto
- Federal University of São João del-Rei, Campus Centro-Oeste Dona Lindu, Divinópolis, Minas Gerais, Brazil.
| | | | - Vinícius Silva Belo
- Federal University of São João del-Rei, Campus Centro-Oeste Dona Lindu, Divinópolis, Minas Gerais, Brazil.
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Bi Q, Kuang Z, Haihong E, Song M, Tan L, Tang X, Liu X. Research on early warning of renal damage in hypertensive patients based on the stacking strategy. BMC Med Inform Decis Mak 2022; 22:212. [PMID: 35945608 PMCID: PMC9361646 DOI: 10.1186/s12911-022-01889-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 03/31/2022] [Indexed: 11/26/2022] Open
Abstract
Background Among the problems caused by hypertension, early renal damage is often ignored. It can not be diagnosed until the condition is severe and irreversible damage occurs. So we decided to screen and explore related risk factors for hypertensive patients with early renal damage and establish the early-warning model of renal damage based on the data-mining method to achieve an early diagnosis for hypertensive patients with renal damage. Methods With the aid of an electronic information management system for hypertensive out-patients, we collected 513 cases of original, untreated hypertensive patients. We recorded their demographic data, ambulatory blood pressure parameters, blood routine index, and blood biochemical index to establish the clinical database. Then we screen risk factors for early renal damage through feature engineering and use Random Forest, Extra-Trees, and XGBoost to build an early-warning model, respectively. Finally, we build a new model by model fusion based on the Stacking strategy. We use cross-validation to evaluate the stability and reliability of each model to determine the best risk assessment model. Results According to the degree of importance, the descending order of features selected by feature engineering is the drop rate of systolic blood pressure at night, the red blood cell distribution width, blood pressure circadian rhythm, the average diastolic blood pressure at daytime, body surface area, smoking, age, and HDL. The average precision of the two-dimensional fusion model with full features based on the Stacking strategy is 0.89685, and selected features are 0.93824, which is greatly improved. Conclusions Through feature engineering and risk factor analysis, we select the drop rate of systolic blood pressure at night, the red blood cell distribution width, blood pressure circadian rhythm, and the average diastolic blood pressure at daytime as early-warning factors of early renal damage in patients with hypertension. On this basis, the two-dimensional fusion model based on the Stacking strategy has a better effect than the single model, which can be used for risk assessment of early renal damage in hypertensive patients.
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Affiliation(s)
- Qiubo Bi
- School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Zemin Kuang
- Department of Hypertension, Beijing Anzhen Hospital of Capital Medical University, Beijing, 100029, China
| | - E Haihong
- School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
| | - Meina Song
- School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Ling Tan
- School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Xinying Tang
- Department of Cardiology, The First People's Hospital of Chenzhou, The University of South China, Chenzhou, 423000, China
| | - Xing Liu
- Department of Anesthesiology, Third Xiangya Hospital, Central South University, Changsha, 410013, China
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Wei W, Liu H, Sun Z. Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework. Scientometrics 2022; 127:4315-4333. [PMID: 35875341 PMCID: PMC9294791 DOI: 10.1007/s11192-022-04462-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 06/30/2022] [Indexed: 11/30/2022]
Abstract
The detection of emerging trends is of great interest to many stakeholders such as government and industry. Previous research focused on the machine learning, network analysis and time series analysis based on the bibliometrics data and made a promising progress. However, these approaches inevitably have time delay problems. For the reason that leader papers of “emerging topics” share the similar characters with the “cover papers”, this study present a novel approach to translate the “emerging topics” detection to “cover paper” prediction. By using “AdaBoost model” and topic model, we construct a machine learning framework to imitate the top journal (chief) editor’s judgement to select cover paper from material science. The results of our prediction were validated by consulting with field experts. This approach was also suitable for the Nature, Science, and Cell journals.
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Abdelkader W, Navarro T, Parrish R, Cotoi C, Germini F, Iorio A, Haynes RB, Lokker C. Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review. JMIR Med Inform 2021; 9:e30401. [PMID: 34499041 PMCID: PMC8461527 DOI: 10.2196/30401] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/15/2021] [Accepted: 07/25/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND The rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy. OBJECTIVE The goal of the research was to summarize the nature and comparative performance of machine learning approaches that have been applied to retrieve high-quality evidence for clinical consideration from the biomedical literature. METHODS We conducted a systematic review of studies that applied machine learning techniques to identify high-quality clinical articles in the biomedical literature. Multiple databases were searched to July 2020. Extracted data focused on the applied machine learning model, steps in the development of the models, and model performance. RESULTS From 3918 retrieved studies, 10 met our inclusion criteria. All followed a supervised machine learning approach and applied, from a limited range of options, a high-quality standard for the training of their model. The results show that machine learning can achieve a sensitivity of 95% while maintaining a high precision of 86%. CONCLUSIONS Machine learning approaches perform well in retrieving high-quality clinical studies. Performance may improve by applying more sophisticated approaches such as active learning and unsupervised machine learning approaches.
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Affiliation(s)
- Wael Abdelkader
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Tamara Navarro
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Rick Parrish
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Chris Cotoi
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Federico Germini
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Alfonso Iorio
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - R Brian Haynes
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Cynthia Lokker
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
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Automatic Identification of High Impact Relevant Articles to Support Clinical Decision Making Using Attention-Based Deep Learning. ELECTRONICS 2020. [DOI: 10.3390/electronics9091364] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
To support evidence-based precision medicine and clinical decision-making, we need to identify accurate, appropriate, and clinically relevant studies from voluminous biomedical literature. To address the issue of accurate identification of high impact relevant articles, we propose a novel approach of attention-based deep learning for finding and ranking relevant studies against a topic of interest. For learning the proposed model, we collect data consisting of 240,324 clinical articles from the 2018 Precision Medicine track in Text REtrieval Conference (TREC) to identify and rank relevant documents matched with the user query. We built a BERT (Bidirectional Encoder Representations from Transformers) based classification model to classify high and low impact articles. We contextualized word embedding to create vectors of the documents, and user queries combined with genetic information to find contextual similarity for determining the relevancy score to rank the articles. We compare our proposed model results with existing approaches and obtain a higher accuracy of 95.44% as compared to 94.57% (the next best performer) and get a higher precision by about 14% at P@5 (precision at 5) and about 12% at P@10 (precision at 10). The contextually viable and competitive outcomes of the proposed model confirm the suitability of our proposed model for use in domains like evidence-based precision medicine.
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Rundo L, Pirrone R, Vitabile S, Sala E, Gambino O. Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine. J Biomed Inform 2020; 108:103479. [DOI: 10.1016/j.jbi.2020.103479] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 04/27/2020] [Accepted: 06/06/2020] [Indexed: 12/28/2022]
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Rae AR, Savery ME, Mork JG, Demner-Fushman D. A High Recall Classifier for Selecting Articles for MEDLINE Indexing. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2020; 2019:727-734. [PMID: 32308868 PMCID: PMC7153058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
MEDLINE is the National Library of Medicine's premier bibliographic database for biomedical literature. A highly valuable feature of the database is that each record is manually indexed with a controlled vocabulary called MeSH. Most MEDLINE journals are indexed cover-to-cover, but there are about 200 selectively indexed journals for which only articles related to biomedicine and life sciences are indexed. In recent years, the selection process has become an increasing burden for indexing staff, and this paper presents a machine learning based system that offers very significant time savings by semi-automating the task. At the core of the system is a high recall classifier for the identification of journal articles that are in-scope for MEDLINE. The system is shown to reduce the number of articles requiring manual review by 54%, equivalent to approximately 40,000 articles per year.
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Affiliation(s)
- Alastair R Rae
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD
| | - Max E Savery
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD
| | - James G Mork
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD
| | - Dina Demner-Fushman
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD
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Milliken LK, Motomarry SK, Kulkarni A. ARtPM: Article Retrieval for Precision Medicine. J Biomed Inform 2019; 95:103224. [DOI: 10.1016/j.jbi.2019.103224] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 05/13/2019] [Accepted: 06/08/2019] [Indexed: 10/26/2022]
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