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Lokker C, McKibbon KA, Afzal M, Navarro T, Linkins LA, Haynes RB, Iorio A. The McMaster Health Information Research Unit: Over a Quarter-Century of Health Informatics Supporting Evidence-Based Medicine. J Med Internet Res 2024; 26:e58764. [PMID: 39083765 PMCID: PMC11325105 DOI: 10.2196/58764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 06/15/2024] [Accepted: 07/16/2024] [Indexed: 08/02/2024] Open
Abstract
Evidence-based medicine (EBM) emerged from McMaster University in the 1980-1990s, which emphasizes the integration of the best research evidence with clinical expertise and patient values. The Health Information Research Unit (HiRU) was created at McMaster University in 1985 to support EBM. Early on, digital health informatics took the form of teaching clinicians how to search MEDLINE with modems and phone lines. Searching and retrieval of published articles were transformed as electronic platforms provided greater access to clinically relevant studies, systematic reviews, and clinical practice guidelines, with PubMed playing a pivotal role. In the early 2000s, the HiRU introduced Clinical Queries-validated search filters derived from the curated, gold-standard, human-appraised Hedges dataset-to enhance the precision of searches, allowing clinicians to hone their queries based on study design, population, and outcomes. Currently, almost 1 million articles are added to PubMed annually. To filter through this volume of heterogenous publications for clinically important articles, the HiRU team and other researchers have been applying classical machine learning, deep learning, and, increasingly, large language models (LLMs). These approaches are built upon the foundation of gold-standard annotated datasets and humans in the loop for active machine learning. In this viewpoint, we explore the evolution of health informatics in supporting evidence search and retrieval processes over the past 25+ years within the HiRU, including the evolving roles of LLMs and responsible artificial intelligence, as we continue to facilitate the dissemination of knowledge, enabling clinicians to integrate the best available evidence into their clinical practice.
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Affiliation(s)
- Cynthia Lokker
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - K Ann McKibbon
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Muhammad Afzal
- Department of Computing and Data Science, Birmingham City University, Birmingham, United Kingdom
| | - Tamara Navarro
- Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Lori-Ann Linkins
- 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
| | - 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
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Tóth B, Berek L, Gulácsi L, Péntek M, Zrubka Z. Automation of systematic reviews of biomedical literature: a scoping review of studies indexed in PubMed. Syst Rev 2024; 13:174. [PMID: 38978132 PMCID: PMC11229257 DOI: 10.1186/s13643-024-02592-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 06/20/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND The demand for high-quality systematic literature reviews (SRs) for evidence-based medical decision-making is growing. SRs are costly and require the scarce resource of highly skilled reviewers. Automation technology has been proposed to save workload and expedite the SR workflow. We aimed to provide a comprehensive overview of SR automation studies indexed in PubMed, focusing on the applicability of these technologies in real world practice. METHODS In November 2022, we extracted, combined, and ran an integrated PubMed search for SRs on SR automation. Full-text English peer-reviewed articles were included if they reported studies on SR automation methods (SSAM), or automated SRs (ASR). Bibliographic analyses and knowledge-discovery studies were excluded. Record screening was performed by single reviewers, and the selection of full text papers was performed in duplicate. We summarized the publication details, automated review stages, automation goals, applied tools, data sources, methods, results, and Google Scholar citations of SR automation studies. RESULTS From 5321 records screened by title and abstract, we included 123 full text articles, of which 108 were SSAM and 15 ASR. Automation was applied for search (19/123, 15.4%), record screening (89/123, 72.4%), full-text selection (6/123, 4.9%), data extraction (13/123, 10.6%), risk of bias assessment (9/123, 7.3%), evidence synthesis (2/123, 1.6%), assessment of evidence quality (2/123, 1.6%), and reporting (2/123, 1.6%). Multiple SR stages were automated by 11 (8.9%) studies. The performance of automated record screening varied largely across SR topics. In published ASR, we found examples of automated search, record screening, full-text selection, and data extraction. In some ASRs, automation fully complemented manual reviews to increase sensitivity rather than to save workload. Reporting of automation details was often incomplete in ASRs. CONCLUSIONS Automation techniques are being developed for all SR stages, but with limited real-world adoption. Most SR automation tools target single SR stages, with modest time savings for the entire SR process and varying sensitivity and specificity across studies. Therefore, the real-world benefits of SR automation remain uncertain. Standardizing the terminology, reporting, and metrics of study reports could enhance the adoption of SR automation techniques in real-world practice.
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Affiliation(s)
- Barbara Tóth
- Doctoral School of Innovation Management, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary
| | - László Berek
- Doctoral School for Safety and Security, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary
- University Library, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary
| | - László Gulácsi
- HECON Health Economics Research Center, University Research, and Innovation Center, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary
| | - Márta Péntek
- HECON Health Economics Research Center, University Research, and Innovation Center, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary
| | - Zsombor Zrubka
- HECON Health Economics Research Center, University Research, and Innovation Center, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary.
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Sivarajkumar S, Kelley M, Samolyk-Mazzanti A, Visweswaran S, Wang Y. An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study. JMIR Med Inform 2024; 12:e55318. [PMID: 38587879 PMCID: PMC11036183 DOI: 10.2196/55318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/20/2024] [Accepted: 02/24/2024] [Indexed: 04/09/2024] Open
Abstract
BACKGROUND Large language models (LLMs) have shown remarkable capabilities in natural language processing (NLP), especially in domains where labeled data are scarce or expensive, such as the clinical domain. However, to unlock the clinical knowledge hidden in these LLMs, we need to design effective prompts that can guide them to perform specific clinical NLP tasks without any task-specific training data. This is known as in-context learning, which is an art and science that requires understanding the strengths and weaknesses of different LLMs and prompt engineering approaches. OBJECTIVE The objective of this study is to assess the effectiveness of various prompt engineering techniques, including 2 newly introduced types-heuristic and ensemble prompts, for zero-shot and few-shot clinical information extraction using pretrained language models. METHODS This comprehensive experimental study evaluated different prompt types (simple prefix, simple cloze, chain of thought, anticipatory, heuristic, and ensemble) across 5 clinical NLP tasks: clinical sense disambiguation, biomedical evidence extraction, coreference resolution, medication status extraction, and medication attribute extraction. The performance of these prompts was assessed using 3 state-of-the-art language models: GPT-3.5 (OpenAI), Gemini (Google), and LLaMA-2 (Meta). The study contrasted zero-shot with few-shot prompting and explored the effectiveness of ensemble approaches. RESULTS The study revealed that task-specific prompt tailoring is vital for the high performance of LLMs for zero-shot clinical NLP. In clinical sense disambiguation, GPT-3.5 achieved an accuracy of 0.96 with heuristic prompts and 0.94 in biomedical evidence extraction. Heuristic prompts, alongside chain of thought prompts, were highly effective across tasks. Few-shot prompting improved performance in complex scenarios, and ensemble approaches capitalized on multiple prompt strengths. GPT-3.5 consistently outperformed Gemini and LLaMA-2 across tasks and prompt types. CONCLUSIONS This study provides a rigorous evaluation of prompt engineering methodologies and introduces innovative techniques for clinical information extraction, demonstrating the potential of in-context learning in the clinical domain. These findings offer clear guidelines for future prompt-based clinical NLP research, facilitating engagement by non-NLP experts in clinical NLP advancements. To the best of our knowledge, this is one of the first works on the empirical evaluation of different prompt engineering approaches for clinical NLP in this era of generative artificial intelligence, and we hope that it will inspire and inform future research in this area.
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Affiliation(s)
- Sonish Sivarajkumar
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States
| | - Mark Kelley
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, United States
| | - Alyssa Samolyk-Mazzanti
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, United States
| | - Shyam Visweswaran
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Yanshan Wang
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
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Hocking L, Parkinson S, Adams A, Molding Nielsen E, Ang C, de Carvalho Gomes H. Overcoming the challenges of using automated technologies for public health evidence synthesis. Euro Surveill 2023; 28:2300183. [PMID: 37943502 PMCID: PMC10636742 DOI: 10.2807/1560-7917.es.2023.28.45.2300183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 08/10/2023] [Indexed: 11/10/2023] Open
Abstract
Many organisations struggle to keep pace with public health evidence due to the volume of published literature and length of time it takes to conduct literature reviews. New technologies that help automate parts of the evidence synthesis process can help conduct reviews more quickly and efficiently to better provide up-to-date evidence for public health decision making. To date, automated approaches have seldom been used in public health due to significant barriers to their adoption. In this Perspective, we reflect on the findings of a study exploring experiences of adopting automated technologies to conduct evidence reviews within the public health sector. The study, funded by the European Centre for Disease Prevention and Control, consisted of a literature review and qualitative data collection from public health organisations and researchers in the field. We specifically focus on outlining the challenges associated with the adoption of automated approaches and potential solutions and actions that can be taken to mitigate these. We explore these in relation to actions that can be taken by tool developers (e.g. improving tool performance and transparency), public health organisations (e.g. developing staff skills, encouraging collaboration) and funding bodies/the wider research system (e.g. researchers, funding bodies, academic publishers and scholarly journals).
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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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Sousa DF, Couto FM. K-RET: knowledgeable biomedical relation extraction system. BIOINFORMATICS (OXFORD, ENGLAND) 2023; 39:7108769. [PMID: 37018156 PMCID: PMC10112952 DOI: 10.1093/bioinformatics/btad174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/25/2023] [Accepted: 03/29/2023] [Indexed: 04/20/2023]
Abstract
MOTIVATION Relation extraction (RE) is a crucial process to deal with the amount of text published daily, e.g. to find missing associations in a database. RE is a text mining task for which the state-of-the-art approaches use bidirectional encoders, namely, BERT. However, state-of-the-art performance may be limited by the lack of efficient external knowledge injection approaches, with a larger impact in the biomedical area given the widespread usage and high quality of biomedical ontologies. This knowledge can propel these systems forward by aiding them in predicting more explainable biomedical associations. With this in mind, we developed K-RET, a novel, knowledgeable biomedical RE system that, for the first time, injects knowledge by handling different types of associations, multiple sources and where to apply it, and multi-token entities. RESULTS We tested K-RET on three independent and open-access corpora (DDI, BC5CDR, and PGR) using four biomedical ontologies handling different entities. K-RET improved state-of-the-art results by 2.68% on average, with the DDI Corpus yielding the most significant boost in performance, from 79.30% to 87.19% in F-measure, representing a P-value of 2.91×10-12. AVAILABILITY AND IMPLEMENTATION https://github.com/lasigeBioTM/K-RET.
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Affiliation(s)
- Diana F Sousa
- Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisboa 1749-016, Portugal
| | - Francisco M Couto
- Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisboa 1749-016, Portugal
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Monteiro JP, Morine MJ, Ued FV, Kaput J. Identifying and Analyzing Topic Clusters in a Nutri-, Food-, and Diet-Proteomic Corpus Using Machine Reading. Nutrients 2023; 15:nu15020270. [PMID: 36678141 PMCID: PMC9863309 DOI: 10.3390/nu15020270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/27/2022] [Accepted: 12/30/2022] [Indexed: 01/06/2023] Open
Abstract
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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Affiliation(s)
- Jacqueline Pontes Monteiro
- Department of Pediatrics, Ribeirão Preto Medical School, University of São Paulo, Bandeirantes Avenue, 3900, Ribeirão Preto 14049-900, Brazil
- Correspondence:
| | | | - Fabio V. Ued
- Department of Pediatrics, Ribeirão Preto Medical School, University of São Paulo, Bandeirantes Avenue, 3900, Ribeirão Preto 14049-900, Brazil
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Weimer K, Buschhart C, Broelz EK, Enck P, Horing B. Bibliometric Properties of Placebo Literature From the JIPS Database: A Descriptive Study. Front Psychiatry 2022; 13:853953. [PMID: 35401284 PMCID: PMC8990029 DOI: 10.3389/fpsyt.2022.853953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 03/04/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVES First dedicated articles about placebo effects have been published in the 1940s, and more than 5,000 articles have been published in scientific organs since. However, the evolution of this research field has rarely been examined. By means of bibliometric analyses we aim to generate research metrics such as the number and types of publications as well as topics, authorship networks, impacts, and future directions. METHODS Bibliometric methods were applied to the Journal of Interdisciplinary Placebo Studies (JIPS) database. It comprises around 5,000 scientific articles dedicated to researching placebo effects and mechanisms and is expanded continually through individual curation, making it a prime candidate for investigation. Web scraping was used to obtain complete article information from PubMed and Web of Science. The same information was obtained for addiction research as reference field. Analyses include a general characterization of the database as well as focus points concerning publication types (data vs. non-data articles), high-impact publications and more. RESULTS Analyses show that the JIPS database is a comprehensive collection of placebo publications. The development of the field is comparable to that of the comparator field and scientific publication in general. The most frequently used keywords describe populations or study design topics; the most frequent symptoms were pain, depression and anxiety. Data and non-data (e.g., review) papers are related in proportion of about 6:4 in recent decades, indicating a stable degree of productivity. A network of 26 interconnected researchers was identified who published 25 or more articles. Placebo research contributes comparable numbers of publications to high-impact journals as the comparator field. Several additional analyses are performed, with a focus on visualization of various database parameters. CONCLUSIONS Bibliometric analyses of the JIPS database can be used to answer questions to the field, for example, to get an impression of blind spots and future directions. However, keywords used in indexing and publications themselves are often general and suggest that placebo research may still be considered a subspecialty of superordinate fields, particularly since there are no journals dedicated to placebo research itself. We invite interested colleagues to use this database for further analyses.
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Affiliation(s)
- Katja Weimer
- Department of Psychosomatic Medicine and Psychotherapy, Ulm University Medical Center, Ulm, Germany
| | - Cliff Buschhart
- University Library, Brandenburg University of Applied Sciences, Brandenburg an der Havel, Germany
| | - Ellen K Broelz
- Department of Psychosomatic Medicine and Psychotherapy, Tübingen University Hospital, Tübingen, Germany
| | - Paul Enck
- Department of Psychosomatic Medicine and Psychotherapy, Tübingen University Hospital, Tübingen, Germany
| | - Björn Horing
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Abdelkader W, Navarro T, Parrish R, Cotoi C, Germini F, Linkins LA, Iorio A, Haynes RB, Ananiadou S, Chu L, Lokker C. A Deep Learning Approach to Refine the Identification of High-Quality Clinical Research Articles From the Biomedical Literature: Protocol for Algorithm Development and Validation. JMIR Res Protoc 2021; 10:e29398. [PMID: 34847061 PMCID: PMC8669577 DOI: 10.2196/29398] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 08/24/2021] [Accepted: 09/17/2021] [Indexed: 11/16/2022] Open
Abstract
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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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
| | - Lori-Ann Linkins
- 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
| | - Sophia Ananiadou
- Department of Computer Science, University of Manchester, Manchester, United Kingdom.,The Alan Turing Institute, London, United Kingdom
| | - Lingyang Chu
- Department of Computing and Software, Faculty of Engineering, 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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10
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Yang X, Mu D, Peng H, Li H, Wang Y, Wang P, Wang Y, Han S. Research and Application of Artificial Intelligence (AI) based on Electronic Health Records from Patients with Cancer: a Systematic Review (Preprint). JMIR Med Inform 2021; 10:e33799. [PMID: 35442195 PMCID: PMC9069295 DOI: 10.2196/33799] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/24/2022] [Accepted: 03/14/2022] [Indexed: 01/12/2023] Open
Abstract
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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Affiliation(s)
- Xinyu Yang
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, China
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China
| | - Dongmei Mu
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, China
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China
| | - Hao Peng
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, China
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China
| | - Hua Li
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, China
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China
| | - Ying Wang
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, China
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China
| | - Ping Wang
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, China
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China
| | - Yue Wang
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, China
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China
| | - Siqi Han
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, China
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China
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