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Lu X, Yang C, Liang L, Hu G, Zhong Z, Jiang Z. Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review. J Am Med Inform Assoc 2024:ocae243. [PMID: 39259922 DOI: 10.1093/jamia/ocae243] [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: 03/20/2024] [Revised: 08/15/2024] [Accepted: 09/02/2024] [Indexed: 09/13/2024] Open
Abstract
OBJECTIVE The objective of our research is to conduct a comprehensive review that aims to systematically map, describe, and summarize the current utilization of artificial intelligence (AI) in the recruitment and retention of participants in clinical trials. MATERIALS AND METHODS A comprehensive electronic search was conducted using the search strategy developed by the authors. The search encompassed research published in English, without any time limitations, which utilizes AI in the recruitment process of clinical trials. Data extraction was performed using a data charting table, which included publication details, study design, and specific outcomes/results. RESULTS The search yielded 5731 articles, of which 51 were included. All the studies were designed specifically for optimizing recruitment in clinical trials and were published between 2004 and 2023. Oncology was the most covered clinical area. Applying AI to recruitment in clinical trials has demonstrated several positive outcomes, such as increasing efficiency, cost savings, improving recruitment, accuracy, patient satisfaction, and creating user-friendly interfaces. It also raises various technical and ethical issues, such as limited quantity and quality of sample size, privacy, data security, transparency, discrimination, and selection bias. DISCUSSION AND CONCLUSION While AI holds promise for optimizing recruitment in clinical trials, its effectiveness requires further validation. Future research should focus on using valid and standardized outcome measures, methodologically improving the rigor of the research carried out.
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Affiliation(s)
- Xiaoran Lu
- Department of Philosophy, School of the Art, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Chen Yang
- Department of Philosophy, School of Humanities, Central South University, Changsha, Hunan 410075, P.R. China
| | - Lu Liang
- Department of Philosophy, School of Humanities, Central South University, Changsha, Hunan 410075, P.R. China
| | - Guanyu Hu
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shanxi 710049, P.R. China
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, United Kingdom
| | - Ziyi Zhong
- Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Zihao Jiang
- School of Marxism, Shenzhen Polytechnic University, Shenzhen, Guangdong 518055, P.R. China
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Munzone E, Marra A, Comotto F, Guercio L, Sangalli CA, Lo Cascio M, Pagan E, Sangalli D, Bigoni I, Porta FM, D'Ercole M, Ritorti F, Bagnardi V, Fusco N, Curigliano G. Development and Validation of a Natural Language Processing Algorithm for Extracting Clinical and Pathological Features of Breast Cancer From Pathology Reports. JCO Clin Cancer Inform 2024; 8:e2400034. [PMID: 39137368 DOI: 10.1200/cci.24.00034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 04/26/2024] [Accepted: 06/25/2024] [Indexed: 08/15/2024] Open
Abstract
PURPOSE Electronic health records (EHRs) are valuable information repositories that offer insights for enhancing clinical research on breast cancer (BC) using real-world data. The objective of this study was to develop a natural language processing (NLP) model specifically designed to extract structured data from BC pathology reports written in natural language. METHODS During the initial phase, the algorithm's development cohort comprised 193 pathology reports from 116 patients with BC from 2012 to 2016. A rule-based NLP algorithm was applied to extract 26 variables for analysis and was compared with the manual extraction of data performed by both a data entry specialist and an oncologist. Following the first approach, the data set was expanded to include 513 reports, and a Named Entity Recognition (NER)-NLP model was trained and evaluated using K-fold cross-validation. RESULTS The first approach led to a concordance analysis, which revealed an 82.9% agreement between the algorithm and the oncologist, whereas the concordance between the data entry specialist and the oncologist was 90.8%. The second training approach introduced the definition of an NER-NLP model, in which the accuracy showed remarkable potential (97.8%). Notably, the model demonstrated remarkable performance, especially for parameters such as estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and Ki-67 (F1-score 1.0). CONCLUSION The present study aligns with the rapidly evolving field of artificial intelligence (AI) applications in oncology, seeking to expedite the development of complex cancer databases and registries. The results of the model are currently undergoing postprocessing procedures to organize the data into tabular structures, facilitating their utilization in real-world clinical and research endeavors.
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Affiliation(s)
- Elisabetta Munzone
- Division of Medical Senology, European Institute of Oncology IRCCS, Milan, Italy
| | - Antonio Marra
- Division of Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, Milan, Italy
| | | | | | | | - Martina Lo Cascio
- Central Management of Information Systems and Technologies, European Institute of Oncology IRCCS, Milan, Italy
| | - Eleonora Pagan
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
| | - Davide Sangalli
- Central Management of Information Systems and Technologies, European Institute of Oncology IRCCS, Milan, Italy
| | | | | | - Marianna D'Ercole
- Division of Pathology, European Institute of Oncology IRCCS, Milan, Italy
| | | | - Vincenzo Bagnardi
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
| | - Nicola Fusco
- Division of Pathology, European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Giuseppe Curigliano
- Division of Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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Nilsen P, Sundemo D, Heintz F, Neher M, Nygren J, Svedberg P, Petersson L. Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare. FRONTIERS IN HEALTH SERVICES 2024; 4:1368030. [PMID: 38919828 PMCID: PMC11196845 DOI: 10.3389/frhs.2024.1368030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 05/31/2024] [Indexed: 06/27/2024]
Abstract
Background Evidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, research has shown that there are many barriers to achieving the goals of the EBP model. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making. The aim of this paper was to pinpoint key challenges pertaining to the three pillars of EBP and to investigate the potential of AI in surmounting these challenges and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcare to achieve this. Challenges with the three components of EBP Clinical decision-making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generation and dissemination processes, as well as the scarcity of high-quality evidence. Direct application of evidence is not always viable because studies often involve patient groups distinct from those encountered in routine healthcare. Clinicians need to rely on their clinical experience to interpret the relevance of evidence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. Achieving patient involvement and shared decision-making between clinicians and patients remains challenging in routine healthcare practice due to factors such as low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patient knowledge and ineffective communication strategies, busy healthcare environments and limited resources. AI assistance for the three components of EBP AI presents a promising solution to address several challenges inherent in the research process, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinicians in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential to increase patient autonomy although there is a lack of research on this issue. Conclusion This review underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0. However, there are also uncertainties regarding how AI will contribute to a more evidence-based healthcare. Hence, empirical research is essential to validate and substantiate various aspects of AI use in healthcare.
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Affiliation(s)
- Per Nilsen
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - David Sundemo
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Lerum Närhälsan Primary Healthcare Center, Lerum, Sweden
| | - Fredrik Heintz
- Department of Computer and Information Science, Linköping University, Linköping, Sweden
| | - Margit Neher
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Jens Nygren
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Petra Svedberg
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Lena Petersson
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
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Zhang B, Zhang L, Chen Q, Jin Z, Liu S, Zhang S. Harnessing artificial intelligence to improve clinical trial design. COMMUNICATIONS MEDICINE 2023; 3:191. [PMID: 38129570 PMCID: PMC10739942 DOI: 10.1038/s43856-023-00425-3] [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: 05/13/2022] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
Zhang et al. discuss how artificial intelligence (AI) can be used to optimize clinical trial design and potentially boost the success rate of clinical trials. AI has unparalleled potential to leverage real-world data and unlock valuable insights for innovative trial design.
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Affiliation(s)
- Bin Zhang
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
| | - Lu Zhang
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
| | - Qiuying Chen
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
| | - Zhe Jin
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
| | - Shuyi Liu
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
| | - Shuixing Zhang
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China.
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Whitton J, Hunter A. Automated tabulation of clinical trial results: A joint entity and relation extraction approach with transformer-based language representations. Artif Intell Med 2023; 144:102661. [PMID: 37783549 DOI: 10.1016/j.artmed.2023.102661] [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: 07/01/2022] [Revised: 07/05/2023] [Accepted: 09/04/2023] [Indexed: 10/04/2023]
Abstract
Evidence-based medicine, the practice in which healthcare professionals refer to the best available evidence when making decisions, forms the foundation of modern healthcare. However, it relies on labour-intensive systematic reviews, where domain specialists must aggregate and extract information from thousands of publications, primarily of randomised controlled trial (RCT) results, into evidence tables. This paper investigates automating evidence table generation by decomposing the problem across two language processing tasks: named entity recognition, which identifies key entities within text, such as drug names, and relation extraction, which maps their relationships for separating them into ordered tuples. We focus on the automatic tabulation of sentences from published RCT abstracts that report the results of the study outcomes. Two deep neural net models were developed as part of a joint extraction pipeline, using the principles of transfer learning and transformer-based language representations. To train and test these models, a new gold-standard corpus was developed, comprising over 550 result sentences from six disease areas. This approach demonstrated significant advantages, with our system performing well across multiple natural language processing tasks and disease areas, as well as in generalising to disease domains unseen during training. Furthermore, we show these results were achievable through training our models on as few as 170 example sentences. The final system is a proof of concept that the generation of evidence tables can be semi-automated, representing a step towards fully automating systematic reviews.
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Affiliation(s)
- Jetsun Whitton
- Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.
| | - Anthony Hunter
- Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.
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Rani S, Jain A. Optimizing healthcare system by amalgamation of text processing and deep learning: a systematic review. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-25. [PMID: 37362695 PMCID: PMC10183315 DOI: 10.1007/s11042-023-15539-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 05/18/2022] [Accepted: 04/19/2023] [Indexed: 06/28/2023]
Abstract
The explosion of clinical textual data has drawn the attention of researchers. Owing to the abundance of clinical data, it is becoming difficult for healthcare professionals to take real-time measures. The tools and methods are lacking when compared to the amount of clinical data generated every day. This review aims to survey the text processing pipeline with deep learning methods such as CNN, RNN, LSTM, and GRU in the healthcare domain and discuss various applications such as clinical concept detection and extraction, medically aware dialogue systems, sentiment analysis of drug reviews shared online, clinical trial matching, and pharmacovigilance. In addition, we highlighted the major challenges in deploying text processing with deep learning to clinical textual data and identified the scope of research in this domain. Furthermore, we have discussed various resources that can be used in the future to optimize the healthcare domain by amalgamating text processing and deep learning.
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Affiliation(s)
- Somiya Rani
- Department of Computer Science and Engineering, NSUT East Campus (erstwhile AIACTR), Affiliated to Guru Gobind Singh Indraprastha University, Delhi, India
| | - Amita Jain
- Department of Computer Science and Engineering, Netaji Subhas University of Technology, Delhi, India
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Chow R, Midroni J, Kaur J, Boldt G, Liu G, Eng L, Liu FF, Haibe-Kains B, Lock M, Raman S. Use of artificial intelligence for cancer clinical trial enrollment: a systematic review and meta-analysis. J Natl Cancer Inst 2023; 115:365-374. [PMID: 36688707 PMCID: PMC10086633 DOI: 10.1093/jnci/djad013] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 12/13/2022] [Accepted: 01/11/2023] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND The aim of this study is to provide a comprehensive understanding of the current landscape of artificial intelligence (AI) for cancer clinical trial enrollment and its predictive accuracy in identifying eligible patients for inclusion in such trials. METHODS Databases of PubMed, Embase, and Cochrane CENTRAL were searched until June 2022. Articles were included if they reported on AI actively being used in the clinical trial enrollment process. Narrative synthesis was conducted among all extracted data: accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. For studies where the 2x2 contingency table could be calculated or supplied by authors, a meta-analysis to calculate summary statistics was conducted using the hierarchical summary receiver operating characteristics curve model. RESULTS Ten articles reporting on more than 50 000 patients in 19 datasets were included. Accuracy, sensitivity, and specificity exceeded 80% in all but 1 dataset. Positive predictive value exceeded 80% in 5 of 17 datasets. Negative predictive value exceeded 80% in all datasets. Summary sensitivity was 90.5% (95% confidence interval [CI] = 70.9% to 97.4%); summary specificity was 99.3% (95% CI = 81.8% to 99.9%). CONCLUSIONS AI demonstrated comparable, if not superior, performance to manual screening for patient enrollment into cancer clinical trials. As well, AI is highly efficient, requiring less time and human resources to screen patients. AI should be further investigated and implemented for patient recruitment into cancer clinical trials. Future research should validate the use of AI for clinical trials enrollment in less resource-rich regions and ensure broad inclusion for generalizability to all sexes, ages, and ethnicities.
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Affiliation(s)
- Ronald Chow
- Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
- Institute of Biomedical Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, ON, Canada
| | - Julie Midroni
- Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jagdeep Kaur
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
| | - Gabriel Boldt
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
| | - Geoffrey Liu
- Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Lawson Eng
- Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Fei-Fei Liu
- Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Michael Lock
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada
| | - Srinivas Raman
- Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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Yang S, Varghese P, Stephenson E, Tu K, Gronsbell J. Machine learning approaches for electronic health records phenotyping: a methodical review. J Am Med Inform Assoc 2023; 30:367-381. [PMID: 36413056 PMCID: PMC9846699 DOI: 10.1093/jamia/ocac216] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/27/2022] [Accepted: 10/27/2022] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE Accurate and rapid phenotyping is a prerequisite to leveraging electronic health records for biomedical research. While early phenotyping relied on rule-based algorithms curated by experts, machine learning (ML) approaches have emerged as an alternative to improve scalability across phenotypes and healthcare settings. This study evaluates ML-based phenotyping with respect to (1) the data sources used, (2) the phenotypes considered, (3) the methods applied, and (4) the reporting and evaluation methods used. MATERIALS AND METHODS We searched PubMed and Web of Science for articles published between 2018 and 2022. After screening 850 articles, we recorded 37 variables on 100 studies. RESULTS Most studies utilized data from a single institution and included information in clinical notes. Although chronic conditions were most commonly considered, ML also enabled the characterization of nuanced phenotypes such as social determinants of health. Supervised deep learning was the most popular ML paradigm, while semi-supervised and weakly supervised learning were applied to expedite algorithm development and unsupervised learning to facilitate phenotype discovery. ML approaches did not uniformly outperform rule-based algorithms, but deep learning offered a marginal improvement over traditional ML for many conditions. DISCUSSION Despite the progress in ML-based phenotyping, most articles focused on binary phenotypes and few articles evaluated external validity or used multi-institution data. Study settings were infrequently reported and analytic code was rarely released. CONCLUSION Continued research in ML-based phenotyping is warranted, with emphasis on characterizing nuanced phenotypes, establishing reporting and evaluation standards, and developing methods to accommodate misclassified phenotypes due to algorithm errors in downstream applications.
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Affiliation(s)
- Siyue Yang
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | | | - Ellen Stephenson
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Karen Tu
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Jessica Gronsbell
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
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Culié D, Schiappa R, Contu S, Scheller B, Villarme A, Dassonville O, Poissonnet G, Bozec A, Chamorey E. Validation and Improvement of a Convolutional Neural Network to Predict the Involved Pathology in a Head and Neck Surgery Cohort. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:12200. [PMID: 36231500 PMCID: PMC9564535 DOI: 10.3390/ijerph191912200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/19/2022] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
The selection of patients for the constitution of a cohort is a major issue for clinical research (prospective studies and retrospective studies in real life). Our objective was to validate in real life conditions the use of a Deep Learning process based on a neural network, for the classification of patients according to the pathology involved in a head and neck surgery department. 24,434 Electronic Health Records (EHR) from the first visit between 2000 and 2020 were extracted. More than 6000 EHR were manually classified in ten groups of interest according to the reason for consultation with a clinical relevance. A convolutional neural network (TensorFlow, previously reported by Hsu et al.) was then used to predict the group of patients based on their pathology, using two levels of classification based on clinically relevant criteria. On the first and second level of classification, macro-average performances were: 0.95, 0.83, 0.85, 0.97, 0.84 and 0.93, 0.76, 0.83, 0.96, 0.79 for accuracy, recall, precision, specificity and F1-score versus accuracy, recall and precision of 0.580, 580 and 0.582 for Hsu et al., respectively. We validated this model to predict the pathology involved and to constitute clinically relevant cohorts in a tertiary hospital. This model did not require a preprocessing stage, was used in French and showed equivalent or better performances than other already published techniques.
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Affiliation(s)
- Dorian Culié
- Head and Neck Surgery Department, Antoine Laccassagne Center, 06100 Nice, France
- Epidemiology, Biostatistics and Health Data Department, Antoine Laccassagne Center, 06100 Nice, France
| | - Renaud Schiappa
- Epidemiology, Biostatistics and Health Data Department, Antoine Laccassagne Center, 06100 Nice, France
| | - Sara Contu
- Epidemiology, Biostatistics and Health Data Department, Antoine Laccassagne Center, 06100 Nice, France
| | - Boris Scheller
- Head and Neck Surgery Department, Antoine Laccassagne Center, 06100 Nice, France
- Epidemiology, Biostatistics and Health Data Department, Antoine Laccassagne Center, 06100 Nice, France
| | - Agathe Villarme
- Head and Neck Surgery Department, Antoine Laccassagne Center, 06100 Nice, France
| | - Olivier Dassonville
- Head and Neck Surgery Department, Antoine Laccassagne Center, 06100 Nice, France
| | - Gilles Poissonnet
- Head and Neck Surgery Department, Antoine Laccassagne Center, 06100 Nice, France
| | - Alexandre Bozec
- Head and Neck Surgery Department, Antoine Laccassagne Center, 06100 Nice, France
- Epidemiology, Biostatistics and Health Data Department, Antoine Laccassagne Center, 06100 Nice, France
| | - Emmanuel Chamorey
- Epidemiology, Biostatistics and Health Data Department, Antoine Laccassagne Center, 06100 Nice, France
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Farina E, Nabhen JJ, Dacoregio MI, Batalini F, Moraes FY. An overview of artificial intelligence in oncology. Future Sci OA 2022; 8:FSO787. [PMID: 35369274 PMCID: PMC8965797 DOI: 10.2144/fsoa-2021-0074] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 01/19/2022] [Indexed: 11/23/2022] Open
Abstract
Cancer is associated with significant morbimortality globally. Advances in screening, diagnosis, management and survivorship were substantial in the last decades, however, challenges in providing personalized and data-oriented care remain. Artificial intelligence (AI), a branch of computer science used for predictions and automation, has emerged as potential solution to improve the healthcare journey and to promote precision in healthcare. AI applications in oncology include, but are not limited to, optimization of cancer research, improvement of clinical practice (eg., prediction of the association of multiple parameters and outcomes - prognosis and response) and better understanding of tumor molecular biology. In this review, we examine the current state of AI in oncology, including fundamentals, current applications, limitations and future perspectives.
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Affiliation(s)
- Eduardo Farina
- Department of Radiology, Federal University of São Paulo, SP, 04021-001, Brazil; Diagnósticos da America SA (Dasa), 05425-020, Brazil
| | - Jacqueline J Nabhen
- School of Medicine, Federal University of Paraná, Curitiba, PR, 80060-000, Brazil
| | - Maria Inez Dacoregio
- School of Medicine, State University of Centro-Oeste, Guarapuava, PR, 85040-167, Brazil
| | - Felipe Batalini
- Department of Medicine, Division of Medical Oncology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Fabio Y Moraes
- Department of Oncology, Division of Radiation Oncology, Queen's University, Kingston, ON, K7L 3N6, Canada
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Idnay B, Dreisbach C, Weng C, Schnall R. A systematic review on natural language processing systems for eligibility prescreening in clinical research. J Am Med Inform Assoc 2021; 29:197-206. [PMID: 34725689 PMCID: PMC8714283 DOI: 10.1093/jamia/ocab228] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 08/30/2021] [Accepted: 10/04/2021] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE We conducted a systematic review to assess the effect of natural language processing (NLP) systems in improving the accuracy and efficiency of eligibility prescreening during the clinical research recruitment process. MATERIALS AND METHODS Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards of quality for reporting systematic reviews, a protocol for study eligibility was developed a priori and registered in the PROSPERO database. Using predetermined inclusion criteria, studies published from database inception through February 2021 were identified from 5 databases. The Joanna Briggs Institute Critical Appraisal Checklist for Quasi-experimental Studies was adapted to determine the study quality and the risk of bias of the included articles. RESULTS Eleven studies representing 8 unique NLP systems met the inclusion criteria. These studies demonstrated moderate study quality and exhibited heterogeneity in the study design, setting, and intervention type. All 11 studies evaluated the NLP system's performance for identifying eligible participants; 7 studies evaluated the system's impact on time efficiency; 4 studies evaluated the system's impact on workload; and 2 studies evaluated the system's impact on recruitment. DISCUSSION NLP systems in clinical research eligibility prescreening are an understudied but promising field that requires further research to assess its impact on real-world adoption. Future studies should be centered on continuing to develop and evaluate relevant NLP systems to improve enrollment into clinical studies. CONCLUSION Understanding the role of NLP systems in improving eligibility prescreening is critical to the advancement of clinical research recruitment.
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Affiliation(s)
- Betina Idnay
- School of Nursing, Columbia University, New York, New York, USA
- Department of Neurology, Columbia University, New York, New York, USA
| | - Caitlin Dreisbach
- Data Science Institute, Columbia University, New York, New York, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Rebecca Schnall
- School of Nursing, Columbia University, New York, New York, USA
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12
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Chen Z, Lin L, Wu C, Li C, Xu R, Sun Y. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond) 2021; 41:1100-1115. [PMID: 34613667 PMCID: PMC8626610 DOI: 10.1002/cac2.12215] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/10/2021] [Accepted: 09/01/2021] [Indexed: 12/12/2022] Open
Abstract
Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti-cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI-powered cancer care.
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Affiliation(s)
- Zi‐Hang Chen
- Department of Radiation OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
- Zhongshan School of MedicineSun Yat‐sen UniversityGuangzhouGuangdong510080P. R. China
| | - Li Lin
- Department of Radiation OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| | - Chen‐Fei Wu
- Department of Radiation OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| | - Chao‐Feng Li
- Artificial Intelligence LaboratoryState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| | - Rui‐Hua Xu
- Department of Medical OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| | - Ying Sun
- Department of Radiation OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
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Yang LWY, Ng WY, Foo LL, Liu Y, Yan M, Lei X, Zhang X, Ting DSW. Deep learning-based natural language processing in ophthalmology: applications, challenges and future directions. Curr Opin Ophthalmol 2021; 32:397-405. [PMID: 34324453 DOI: 10.1097/icu.0000000000000789] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is the fourth industrial revolution in mankind's history. Natural language processing (NLP) is a type of AI that transforms human language, to one that computers can interpret and process. NLP is still in the formative stages of development in healthcare, with promising applications and potential challenges in its applications. This review provides an overview of AI-based NLP, its applications in healthcare and ophthalmology, next-generation use case, as well as potential challenges in deployment. RECENT FINDINGS The integration of AI-based NLP systems into existing clinical care shows considerable promise in disease screening, risk stratification, and treatment monitoring, amongst others. Stakeholder collaboration, greater public acceptance, and advancing technologies will continue to shape the NLP landscape in healthcare and ophthalmology. SUMMARY Healthcare has always endeavored to be patient centric and personalized. For AI-based NLP systems to become an eventual reality in larger-scale applications, it is pertinent for key stakeholders to collaborate and address potential challenges in application. Ultimately, these would enable more equitable and generalizable use of NLP systems for the betterment of healthcare and society.
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Affiliation(s)
| | - Wei Yan Ng
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Li Lian Foo
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Yong Liu
- Institute of High Performance Computing, A STAR
| | - Ming Yan
- Institute of High Performance Computing, A STAR
| | | | | | - Daniel Shu Wei Ting
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
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14
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von Itzstein MS, Hullings M, Mayo H, Beg MS, Williams EL, Gerber DE. Application of Information Technology to Clinical Trial Evaluation and Enrollment: A Review. JAMA Oncol 2021; 7:1559-1566. [PMID: 34236403 DOI: 10.1001/jamaoncol.2021.1165] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Importance As cancer treatment has become more individualized, oncologic clinical trials have become more complex. Increasingly numerous and stringent eligibility criteria frequently include tumor molecular or genomic characteristics that may not be readily identified in medical records, rendering it difficult to best match clinical trials with clinical sites and to identify potentially eligible patients once a clinical trial has been selected and activated. Partly because of these factors, enrollment rates for cancer clinical trials remain low, creating delays and increased costs for drug development. Information technology (IT) platforms have been applied to the implementation and conduct of clinical trials to improve efficiencies in several medical fields, and these platforms have recently been introduced to oncologic studies. Observations This review summarizes cancer and noncancer studies that used IT platforms for assistance with clinical trial site selection, patient recruitment, and patient screening. The review does not address the use of IT in other aspects of clinical research, such as wearable physical activity monitors or telehealth visits. A large number of IT platforms (which may be patient facing, site or investigator facing, or sponsor facing) are now commercially available. These applications use artificial intelligence and/or natural language processing to identify and summarize protocol eligibility criteria, institutional patient populations, and individual electronic health records. Although there is an expanding body of literature examining the role of this technology, relatively few studies to date have been performed in oncologic settings. Conclusions and Relevance This review found that an increasing number and variety of IT platforms were available to assist in the planning and conduct of clinical trials. Because oncologic clinical care and clinical trial protocols are particularly complex, nuanced, and individualized, published experience with this technology in other fields may not be fully applicable to cancer settings. The extent to which these services will overcome ongoing and increasing challenges in cancer clinical research remains unclear.
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Affiliation(s)
- Mitchell S von Itzstein
- Department of Internal Medicine, Division of Hematology-Oncology, The University of Texas Southwestern Medical Center, Dallas.,Harold C. Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas
| | - Melanie Hullings
- Harold C. Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas
| | - Helen Mayo
- Southwestern Health Sciences Digital Library and Learning Center, The University of Texas, Dallas
| | - M Shaalan Beg
- Department of Internal Medicine, Division of Hematology-Oncology, The University of Texas Southwestern Medical Center, Dallas.,Harold C. Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas
| | - Erin L Williams
- Harold C. Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas
| | - David E Gerber
- Department of Internal Medicine, Division of Hematology-Oncology, The University of Texas Southwestern Medical Center, Dallas.,Harold C. Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas.,Department of Population and Data Sciences, The University of Texas, Southwestern Medical Center, Dallas
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15
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Bitterman DS, Miller TA, Mak RH, Savova GK. Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer. Int J Radiat Oncol Biol Phys 2021; 110:641-655. [PMID: 33545300 DOI: 10.1016/j.ijrobp.2021.01.044] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 12/22/2020] [Accepted: 01/23/2021] [Indexed: 02/07/2023]
Abstract
Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language processing algorithms convert unstructured free text data into structured data that can be extracted and analyzed at scale. In medicine, this unlocking of the rich, expressive data within clinical free text in electronic medical records will help untap the full potential of big data for research and clinical purposes. Recent major NLP algorithmic advances have significantly improved the performance of these algorithms, leading to a surge in academic and industry interest in developing tools to automate information extraction and phenotyping from clinical texts. Thus, these technologies are poised to transform medical research and alter clinical practices in the future. Radiation oncology stands to benefit from NLP algorithms if they are appropriately developed and deployed, as they may enable advances such as automated inclusion of radiation therapy details into cancer registries, discovery of novel insights about cancer care, and improved patient data curation and presentation at the point of care. However, challenges remain before the full value of NLP is realized, such as the plethora of jargon specific to radiation oncology, nonstandard nomenclature, a lack of publicly available labeled data for model development, and interoperability limitations between radiation oncology data silos. Successful development and implementation of high quality and high value NLP models for radiation oncology will require close collaboration between computer scientists and the radiation oncology community. Here, we present a primer on artificial intelligence algorithms in general and NLP algorithms in particular; provide guidance on how to assess the performance of such algorithms; review prior research on NLP algorithms for oncology; and describe future avenues for NLP in radiation oncology research and clinics.
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Affiliation(s)
- Danielle S Bitterman
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, Massachusetts; Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts; Artificial Intelligence in Medicine Program, Brigham and Women's Hospital, Boston, Massachusetts.
| | - Timothy A Miller
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts
| | - Raymond H Mak
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, Massachusetts; Artificial Intelligence in Medicine Program, Brigham and Women's Hospital, Boston, Massachusetts
| | - Guergana K Savova
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts
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Weissler EH, Zhang J, Lippmann S, Rusincovitch S, Henao R, Jones WS. Use of Natural Language Processing to Improve Identification of Patients With Peripheral Artery Disease. Circ Cardiovasc Interv 2020; 13:e009447. [PMID: 33040585 DOI: 10.1161/circinterventions.120.009447] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Peripheral artery disease (PAD) is underrecognized, undertreated, and understudied: each of these endeavors requires efficient and accurate identification of patients with PAD. Currently, PAD patient identification relies on diagnosis/procedure codes or lists of patients diagnosed or treated by specific providers in specific locations and ways. The goal of this research was to leverage natural language processing to more accurately identify patients with PAD in an electronic health record system compared with a structured data-based approach. METHODS The clinical notes from a cohort of 6861 patients in our health system whose PAD status had previously been adjudicated were used to train, test, and validate a natural language processing model using 10-fold cross-validation. The performance of this model was described using the area under the receiver operating characteristic and average precision curves; its performance was quantitatively compared with an administrative data-based least absolute shrinkage and selection operator (LASSO) approach using the DeLong test. RESULTS The median (SD) of the area under the receiver operating characteristic curve for the natural language processing model was 0.888 (0.009) versus 0.801 (0.017) for the LASSO-based approach alone (DeLong P<0.0001). The median (SD) of the area under the precision curve was 0.909 (0.008) versus 0.816 (0.012) for the structured data-based approach. When sensitivity was set at 90%, the precision for LASSO was 65% and the machine learning approach was 74%, while the specificity for LASSO was 41% and for the machine learning approach was 62%. CONCLUSIONS Using a natural language processing approach in addition to partial cohort preprocessing with a LASSO-based model, we were able to meaningfully improve our ability to identify patients with PAD compared with an approach using structured data alone. This model has potential applications to both interventions targeted at improving patient care as well as efficient, large-scale PAD research. Graphic Abstract: A graphic abstract is available for this article.
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Affiliation(s)
- E Hope Weissler
- Division of Vascular and Endovascular Surgery (E.H.W.), Duke University School of Medicine, Durham, NC
| | - Jikai Zhang
- Department of Biostatistics and Bioinformatics (J.Z., R.H.), Duke University School of Medicine, Durham, NC
| | - Steven Lippmann
- Department of Population Health Sciences (S.L., W.S.J.), Duke University School of Medicine, Durham, NC
| | | | - Ricardo Henao
- Department of Biostatistics and Bioinformatics (J.Z., R.H.), Duke University School of Medicine, Durham, NC.,Duke Forge (S.R., R.H.), Duke University School of Medicine, Durham, NC
| | - W Schuyler Jones
- Department of Population Health Sciences (S.L., W.S.J.), Duke University School of Medicine, Durham, NC.,Division of Cardiology (W.S.J.), Duke University School of Medicine, Durham, NC
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