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Arif U, Zhang C, Chaudhary MW, Khalid HH. Optimizing lung cancer prediction: leveraging Kernel PCA with dendritic neural models. Comput Methods Biomech Biomed Engin 2024:1-14. [PMID: 39001715 DOI: 10.1080/10255842.2024.2374949] [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: 04/14/2024] [Accepted: 06/24/2024] [Indexed: 07/15/2024]
Abstract
Lung cancer is considered a cause of increased mortality rate due to delays in diagnostics. There is an urgent need to develop an effective lung cancer prediction model that will help in the early diagnosis of cancer and save patients from unnecessary treatments. The objective of the current paper is to meet the extensiveness measure by using collaborative feature selection and feature extraction methods to enhance the dendritic neural model (DNM) in comparison to traditional machine learning (ML) models with minimum features and boost the accuracy, precision, and sensitivity of lung cancer prediction. Comprehensive experiments on a dataset comprising 1000 lung cancer patients and 23 features obtained from Kaggle. Crucial features are identified, and the proposed method's effectiveness is evaluated using metrics such as accuracy, precision, F1 score, sensitivity, specificity, and confusion matrix against other ML models. Feature extraction techniques including Principal Component Analysis (PCA), Kernel PCA (K-PCA), and Uniform Manifold Approximation and Projection (UMAP) are employed to optimize model performance. PCA evaluated the DNM accuracy at 96.50%, precision at 96.64% and 97.45% sensitivity. K-PCA explained the DNM accuracy of 98.50%, precision rate of 99.42%, and 98.84% sensitivity and UMAP elaborated the DNM accuracy of 98%, precision of 98.82%, and 98.82% sensitivity. The K-PCA approach showed outstanding performance in enhancing the DNM model. Highlighting the DNM's accurate prediction of lung cancer. These results emphasize the potential of the DNM model to contribute positively to healthcare research by providing better predictive outcomes.
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Affiliation(s)
- Umair Arif
- Department of Statistics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China
| | - Chunxia Zhang
- Department of Statistics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China
| | - Muhammad Waqas Chaudhary
- Department of Statistics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China
- Department of Statistics, University of WAH, Rawalpindi, Pakistan
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Park JI, Park JW, Zhang K, Kim D. Advancing equity in breast cancer care: natural language processing for analysing treatment outcomes in under-represented populations. BMJ Health Care Inform 2024; 31:e100966. [PMID: 38955389 PMCID: PMC11218025 DOI: 10.1136/bmjhci-2023-100966] [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: 11/16/2023] [Accepted: 06/21/2024] [Indexed: 07/04/2024] Open
Abstract
OBJECTIVE The study aimed to develop natural language processing (NLP) algorithms to automate extracting patient-centred breast cancer treatment outcomes from clinical notes in electronic health records (EHRs), particularly for women from under-represented populations. METHODS The study used clinical notes from 2010 to 2021 from a tertiary hospital in the USA. The notes were processed through various NLP techniques, including vectorisation methods (term frequency-inverse document frequency (TF-IDF), Word2Vec, Doc2Vec) and classification models (support vector classification, K-nearest neighbours (KNN), random forest (RF)). Feature selection and optimisation through random search and fivefold cross-validation were also conducted. RESULTS The study annotated 100 out of 1000 clinical notes, using 970 notes to build the text corpus. TF-IDF and Doc2Vec combined with RF showed the highest performance, while Word2Vec was less effective. RF classifier demonstrated the best performance, although with lower recall rates, suggesting more false negatives. KNN showed lower recall due to its sensitivity to data noise. DISCUSSION The study highlights the significance of using NLP in analysing clinical notes to understand breast cancer treatment outcomes in under-represented populations. The TF-IDF and Doc2Vec models were more effective in capturing relevant information than Word2Vec. The study observed lower recall rates in RF models, attributed to the dataset's imbalanced nature and the complexity of clinical notes. CONCLUSION The study developed high-performing NLP pipeline to capture treatment outcomes for breast cancer in under-represented populations, demonstrating the importance of document-level vectorisation and ensemble methods in clinical notes analysis. The findings provide insights for more equitable healthcare strategies and show the potential for broader NLP applications in clinical settings.
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Affiliation(s)
- Jung In Park
- University of California Irvine, Irvine, California, USA
| | - Jong Won Park
- Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Kexin Zhang
- Donald Bren School of Information & Computer Sciences, University of California Irvine, Irvine, California, USA
| | - Doyop Kim
- Independent Researcher, Irvine, California, USA
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Luo Y, Mao C, Sanchez‐Pinto LN, Ahmad FS, Naidech A, Rasmussen L, Pacheco JA, Schneider D, Mithal LB, Dresden S, Holmes K, Carson M, Shah SJ, Khan S, Clare S, Wunderink RG, Liu H, Walunas T, Cooper L, Yue F, Wehbe F, Fang D, Liebovitz DM, Markl M, Michelson KN, McColley SA, Green M, Starren J, Ackermann RT, D'Aquila RT, Adams J, Lloyd‐Jones D, Chisholm RL, Kho A. Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system. Learn Health Syst 2024; 8:e10417. [PMID: 39036530 PMCID: PMC11257059 DOI: 10.1002/lrh2.10417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 07/23/2024] Open
Abstract
Introduction The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare. Methods We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk-natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross-pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively. Results Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer-reviewed publications. In 2022, our cross-discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare. Conclusions Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution-level learning health system.
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Affiliation(s)
- Yuan Luo
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Chengsheng Mao
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Lazaro N. Sanchez‐Pinto
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Division of Critical Care, Department of PediatricsNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Stanley Manne Children's Research InstituteAnn & Robert H. Lurie Children's Hospital of ChicagoChicagoIllinoisUSA
| | - Faraz S. Ahmad
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Cardiology, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Andrew Naidech
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Neurocritical Care, Department of NeurologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Luke Rasmussen
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Jennifer A. Pacheco
- Center for Genetic MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Daniel Schneider
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
| | - Leena B. Mithal
- Stanley Manne Children's Research InstituteAnn & Robert H. Lurie Children's Hospital of ChicagoChicagoIllinoisUSA
- Division of Infectious Diseases, Department of PediatricsNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Scott Dresden
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of Emergency MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Kristi Holmes
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Galter Health Sciences LibraryNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Matthew Carson
- Galter Health Sciences LibraryNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Sanjiv J. Shah
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Cardiology, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Seema Khan
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of SurgeryNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Susan Clare
- Department of SurgeryNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Richard G. Wunderink
- Division of Critical Care, Department of PediatricsNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Pulmonary and Critical Care Division, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Huiping Liu
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of PharmacologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Division of Hematology and Oncology, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Theresa Walunas
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Division of General Internal Medicine, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Center for Health Information PartnershipsInstitute for Public Health and Medicine, Northwestern UniversityChicagoIllinoisUSA
- Department of Microbiology‐ImmunologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Lee Cooper
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Department of PathologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Feng Yue
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of PathologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Department of Biochemistry and Molecular GeneticsNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Firas Wehbe
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of SurgeryNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Deyu Fang
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of PathologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - David M. Liebovitz
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Division of General Internal Medicine, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Center for Health Information PartnershipsInstitute for Public Health and Medicine, Northwestern UniversityChicagoIllinoisUSA
| | - Michael Markl
- Department of RadiologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Kelly N. Michelson
- Division of Critical Care, Department of PediatricsNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Stanley Manne Children's Research InstituteAnn & Robert H. Lurie Children's Hospital of ChicagoChicagoIllinoisUSA
- Center for Bioethics and Medical Humanities, Institute for Public Health and MedicineNorthwestern UniversityChicagoIllinoisUSA
| | - Susanna A. McColley
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Stanley Manne Children's Research InstituteAnn & Robert H. Lurie Children's Hospital of ChicagoChicagoIllinoisUSA
- Division of Pulmonary and Sleep Medicine, Department of PediatricsNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Marianne Green
- Division of General Internal Medicine, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Justin Starren
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Ronald T. Ackermann
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Division of General Internal Medicine, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Institute for Public Health and MedicineNorthwestern UniversityChicagoIllinoisUSA
| | - Richard T. D'Aquila
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Division of Infectious Diseases, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - James Adams
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of Emergency MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Donald Lloyd‐Jones
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Epidemiology, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Rex L. Chisholm
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Department of SurgeryNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Center for Health Information PartnershipsInstitute for Public Health and Medicine, Northwestern UniversityChicagoIllinoisUSA
| | - Abel Kho
- Northwestern University Clinical and Translational Sciences InstituteChicagoIllinoisUSA
- Institute for Augmented Intelligence in MedicineNorthwestern UniversityChicagoIllinoisUSA
- Division of Health and Biomedical Informatics, Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Division of General Internal Medicine, Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Center for Health Information PartnershipsInstitute for Public Health and Medicine, Northwestern UniversityChicagoIllinoisUSA
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Molinet B, Marro S, Cabrio E, Villata S. Explanatory argumentation in natural language for correct and incorrect medical diagnoses. J Biomed Semantics 2024; 15:8. [PMID: 38816758 PMCID: PMC11138001 DOI: 10.1186/s13326-024-00306-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 04/12/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND A huge amount of research is carried out nowadays in Artificial Intelligence to propose automated ways to analyse medical data with the aim to support doctors in delivering medical diagnoses. However, a main issue of these approaches is the lack of transparency and interpretability of the achieved results, making it hard to employ such methods for educational purposes. It is therefore necessary to develop new frameworks to enhance explainability in these solutions. RESULTS In this paper, we present a novel full pipeline to generate automatically natural language explanations for medical diagnoses. The proposed solution starts from a clinical case description associated with a list of correct and incorrect diagnoses and, through the extraction of the relevant symptoms and findings, enriches the information contained in the description with verified medical knowledge from an ontology. Finally, the system returns a pattern-based explanation in natural language which elucidates why the correct (incorrect) diagnosis is the correct (incorrect) one. The main contribution of the paper is twofold: first, we propose two novel linguistic resources for the medical domain (i.e, a dataset of 314 clinical cases annotated with the medical entities from UMLS, and a database of biological boundaries for common findings), and second, a full Information Extraction pipeline to extract symptoms and findings from the clinical cases and match them with the terms in a medical ontology and to the biological boundaries. An extensive evaluation of the proposed approach shows the our method outperforms comparable approaches. CONCLUSIONS Our goal is to offer AI-assisted educational support framework to form clinical residents to formulate sound and exhaustive explanations for their diagnoses to patients.
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Affiliation(s)
- Benjamin Molinet
- Université Côte d'Azur, CNRS, Inria, I3S, Rte des Lucioles, Sophia Antipolis, 06900, Alpes-Maritimes, France.
| | - Santiago Marro
- Université Côte d'Azur, CNRS, Inria, I3S, Rte des Lucioles, Sophia Antipolis, 06900, Alpes-Maritimes, France
| | - Elena Cabrio
- Université Côte d'Azur, CNRS, Inria, I3S, Rte des Lucioles, Sophia Antipolis, 06900, Alpes-Maritimes, France
| | - Serena Villata
- Université Côte d'Azur, CNRS, Inria, I3S, Rte des Lucioles, Sophia Antipolis, 06900, Alpes-Maritimes, France
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Nunez JJ, Leung B, Ho C, Ng RT, Bates AT. Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing. COMMUNICATIONS MEDICINE 2024; 4:69. [PMID: 38589545 PMCID: PMC11001970 DOI: 10.1038/s43856-024-00495-x] [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: 12/23/2023] [Accepted: 03/28/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Patients with cancer often have unmet psychosocial needs. Early detection of who requires referral to a counsellor or psychiatrist may improve their care. This work used natural language processing to predict which patients will see a counsellor or psychiatrist from a patient's initial oncology consultation document. We believe this is the first use of artificial intelligence to predict psychiatric outcomes from non-psychiatric medical documents. METHODS This retrospective prognostic study used data from 47,625 patients at BC Cancer. We analyzed initial oncology consultation documents using traditional and neural language models to predict whether patients would see a counsellor or psychiatrist in the 12 months following their initial oncology consultation. RESULTS Here, we show our best models achieved a balanced accuracy (receiver-operating-characteristic area-under-curve) of 73.1% (0.824) for predicting seeing a psychiatrist, and 71.0% (0.784) for seeing a counsellor. Different words and phrases are important for predicting each outcome. CONCLUSION These results suggest natural language processing can be used to predict psychosocial needs of patients with cancer from their initial oncology consultation document. Future research could extend this work to predict the psychosocial needs of medical patients in other settings.
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Affiliation(s)
- John-Jose Nunez
- BC Cancer, Vancouver, BC, Canada.
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada.
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.
| | | | | | - Raymond T Ng
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Alan T Bates
- BC Cancer, Vancouver, BC, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
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Ahumada R, Dunstan J, Rojas M, Peñafiel S, Paredes I, Báez P. Automatic Detection of Distant Metastasis Mentions in Radiology Reports in Spanish. JCO Clin Cancer Inform 2024; 8:e2300130. [PMID: 38194615 PMCID: PMC10793975 DOI: 10.1200/cci.23.00130] [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: 07/13/2023] [Revised: 10/12/2023] [Accepted: 11/08/2023] [Indexed: 01/11/2024] Open
Abstract
PURPOSE A critical task in oncology is extracting information related to cancer metastasis from electronic health records. Metastasis-related information is crucial for planning treatment, evaluating patient prognoses, and cancer research. However, the unstructured way in which findings of distant metastasis are often written in radiology reports makes it difficult to extract information automatically. The main aim of this study was to extract distant metastasis findings from free-text imaging and nuclear medicine reports to classify the patient status according to the presence or absence of distant metastasis. MATERIALS AND METHODS We created a distant metastasis annotated corpus using positron emission tomography-computed tomography and computed tomography reports of patients with prostate, colorectal, and breast cancers. Entities were labeled M1 or M0 according to affirmative or negative metastasis descriptions. We used a named entity recognition model on the basis of a bidirectional long short-term memory model and conditional random fields to identify entities. Mentions were subsequently used to classify whole reports into M1 or M0. RESULTS The model detected distant metastasis mentions with a weighted average F1 score performance of 0.84. Whole reports were classified with an F1 score of 0.92 for M0 documents and 0.90 for M1 documents. CONCLUSION These results show the usefulness of the model in detecting distant metastasis findings in three different types of cancer and the consequent classification of reports. The relevance of this study is to generate structured distant metastasis information from free-text imaging reports in Spanish. In addition, the manually annotated corpus, annotation guidelines, and code are freely released to the research community.
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Affiliation(s)
- Ricardo Ahumada
- Center of Medical Informatics and Telemedicine, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Jocelyn Dunstan
- Department of Computer Science & the Institute for Mathematical Computing, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Matías Rojas
- Center for Mathematical Modeling—CNRS IRL 2807, Faculty of Physical and Mathematical Sciences, University of Chile, Santiago, Chile
| | - Sergio Peñafiel
- Unidad de Informática Médica y Data Science, Departamento de Investigación del Cáncer, Instituto Oncológico Fundación Arturo López Pérez, Santiago, Chile
| | - Inti Paredes
- Unidad de Informática Médica y Data Science, Departamento de Investigación del Cáncer, Instituto Oncológico Fundación Arturo López Pérez, Santiago, Chile
| | - Pablo Báez
- Center of Medical Informatics and Telemedicine, Faculty of Medicine, University of Chile, Santiago, Chile
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Szekér S, Fogarassy G, Vathy-Fogarassy Á. A general text mining method to extract echocardiography measurement results from echocardiography documents. Artif Intell Med 2023; 143:102584. [PMID: 37673570 DOI: 10.1016/j.artmed.2023.102584] [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: 01/03/2022] [Revised: 03/08/2023] [Accepted: 05/16/2023] [Indexed: 09/08/2023]
Abstract
BACKGROUND In everyday medical practice, the results of cardiac ultrasound examinations are generally recorded in unstructured text, from which extracting relevant information is an important and challenging task. This paper presents a generally applicable language and corpus-independent text mining method for extracting and structuring numerical measurement results and their descriptions from echocardiography reports. METHOD The developed method is based on generally applicable text mining preprocessing activities, it automatically identifies and standardizes the descriptions of the cardiac ultrasound measures, and it stores the extracted and standardized measurement descriptions with their measurement results in a structured form for later usage. The method does not contain any regular expression-based search and does not rely on information about the structure of the document. RESULTS The method has been tested on a document set containing more than 20,000 echocardiographic reports by examining the efficiency of extracting 12 echocardiography parameters considered important by experts. The method extracted and structured the echocardiography parameters under the study with good sensitivity (lowest value: 0.775, highest value: 1.0, average: 0.904) and excellent specificity (for all cases 1.0). The F1 score ranged between 0.873 and 1.0, and its average value was 0.948. CONCLUSION The presented case study has shown that the proposed method can extract measurement results from echocardiography documents with high confidence without performing a direct search or having detailed information about the data recording habits. Furthermore, it effectively handles spelling errors, abbreviations and the highly varied terminology used in descriptions. As it does not rely on any information related to the structure or the language of the documents or data recording habits, it can be applied for processing any free-text written medical texts.
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Affiliation(s)
- Szabolcs Szekér
- Department of Computer Science and Systems Technology, University of Pannonia, Veszprém, Hungary
| | - György Fogarassy
- 1st Department of Cardiology, State Hospital for Cardiology, Balatonfüred, Hungary
| | - Ágnes Vathy-Fogarassy
- Department of Computer Science and Systems Technology, University of Pannonia, Veszprém, Hungary.
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Liu K, Xie X, Yan J, Zhang S, Zhang H. An adsorption isotherm identification method based on CNN-LSTM neural network. J Mol Model 2023; 29:301. [PMID: 37651008 DOI: 10.1007/s00894-023-05704-3] [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: 05/27/2023] [Accepted: 08/21/2023] [Indexed: 09/01/2023]
Abstract
CONTEXT The morphology of adsorption isotherms embodies a wealth of information regarding various adsorption mechanisms, rendering the classification and identification methodologies predicated on the shape of adsorption isotherms indispensably crucial. While research on classification techniques has been extensively developed, traditional methods of adsorption isotherm identification grapple with inefficiencies and a high margin of error. Neural network-based methodologies for adsorption isotherm identification serve as a countermeasure to these shortcomings, as they facilitate swift online identification while delivering precise results. In this paper, we deploy a hybrid of convolutional neural networks (CNN) and long short-term memory (LSTM) networks for the identification of adsorption isotherms. Extensive theoretical adsorption isotherms are generated via adsorption equations, forming a comprehensive training database, thereby circumventing the need for time-consuming and costly repetitive experiments. The F1-score, receiver operating characteristic (ROC) curves, and area under the ROC curve (AUC) are introduced as criteria to evaluate the identification performance and generalization ability of the model during the testing phase. The results highlight the model's superlative performance in the task of adsorption isotherm identification, with accuracy rates of 100% in both the training and validation sets. The mean F1-score obtained from the testing set reached 0.8885, with both macro-average and micro-average AUC exceeding 0.95. METHOD PyCharm was employed as an experimental and testing platform, with Python 3.9 serving as the programming language. TensorFlow 2.11.0 and Keras 2.10.0 were harnessed for the training and testing of CNN-LSTM, while numpy 1.21.5 and scipy 1.81 were utilized for the creation of training and validation datasets.
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Affiliation(s)
- Kaidi Liu
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xiaohan Xie
- School of Computer Science, Northwestern Polytechnical University, Xian, 710119, China
| | - Juanting Yan
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Sizong Zhang
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Hui Zhang
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
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Sukhadia SS, Muller KE, Workman AA, Nagaraj SH. Machine Learning-Based Prediction of Distant Recurrence in Invasive Breast Carcinoma Using Clinicopathological Data: A Cross-Institutional Study. Cancers (Basel) 2023; 15:3960. [PMID: 37568776 PMCID: PMC10416932 DOI: 10.3390/cancers15153960] [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: 06/09/2023] [Revised: 07/19/2023] [Accepted: 07/19/2023] [Indexed: 08/13/2023] Open
Abstract
Breast cancer is the most common type of cancer worldwide. Alarmingly, approximately 30% of breast cancer cases result in disease recurrence at distant organs after treatment. Distant recurrence is more common in some subtypes such as invasive breast carcinoma (IBC). While clinicians have utilized several clinicopathological measurements to predict distant recurrences in IBC, no studies have predicted distant recurrences by combining clinicopathological evaluations of IBC tumors pre- and post-therapy with machine learning (ML) models. The goal of our study was to determine whether classification-based ML techniques could predict distant recurrences in IBC patients using key clinicopathological measurements, including pathological staging of the tumor and surrounding lymph nodes assessed both pre- and post-neoadjuvant therapy, response to therapy via standard-of-care imaging, and binary status of adjuvant therapy administered to patients. We trained and tested four clinicopathological ML models using a dataset (144 and 17 patients for training and testing, respectively) from Duke University and validated the best-performing model using an external dataset (8 patients) from Dartmouth Hitchcock Medical Center. The random forest model performed better than the C-support vector classifier, multilayer perceptron, and logistic regression models, yielding AUC values of 1.0 in the testing set and 0.75 in the validation set (p < 0.002) across both institutions, thereby demonstrating the cross-institutional portability and validity of ML models in the field of clinical research in cancer. The top-ranking clinicopathological measurement impacting the prediction of distant recurrences in IBC were identified to be tumor response to neoadjuvant therapy as evaluated via SOC imaging and pathology, which included tumor as well as node staging.
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Affiliation(s)
- Shrey S. Sukhadia
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD 4059, Australia
- Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH 03766, USA; (K.E.M.); (A.A.W.)
| | - Kristen E. Muller
- Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH 03766, USA; (K.E.M.); (A.A.W.)
| | - Adrienne A. Workman
- Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH 03766, USA; (K.E.M.); (A.A.W.)
| | - Shivashankar H. Nagaraj
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD 4059, Australia
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El Haji H, Souadka A, Patel BN, Sbihi N, Ramasamy G, Patel BK, Ghogho M, Banerjee I. Evolution of Breast Cancer Recurrence Risk Prediction: A Systematic Review of Statistical and Machine Learning-Based Models. JCO Clin Cancer Inform 2023; 7:e2300049. [PMID: 37566789 DOI: 10.1200/cci.23.00049] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/11/2023] [Accepted: 06/14/2023] [Indexed: 08/13/2023] Open
Abstract
PURPOSE Selection of appropriate adjuvant therapy to ultimately reduce the risk of breast cancer (BC) recurrence is a challenge for medical oncologists. Several automated risk prediction models have been developed using retrospective clinical data and have evolved significantly over the years in terms of predictors of recurrence, data usage, and predictive techniques (statistical/machine learning [ML]). METHODS Following PRISMA guidelines, we performed a systematic literature review of the aforementioned statistical and ML models published between January 2008 and December 2022 through searching five digital databases-PubMed, ScienceDirect, Scopus, Cochrane, and Web of Science. The comprehensive search yielded a total of 163 papers and after a screening process focusing on papers that dealt exclusively with statistical/ML methods, only 23 papers were deemed appropriate for further analysis. We benchmarked the studies on the basis of development, evaluation metrics, and validation strategy with an added emphasis on racial diversity of patients included in the studies. RESULTS In total, 30.4% of the included studies use statistical techniques, while 69.6% are ML-based. Among these, traditional ML models (support vector machines, decision tree, logistic regression, and naïve Bayes) are the most frequently used (26.1%) along with deep learning (26.1%). Deep learning and ensemble learning provide the most accurate predictions (AUC = 0.94 each). CONCLUSION ML-based prediction models exhibit outstanding performance, yet their practical applicability might be hindered by limited interpretability and reduced generalization. Moreover, predictive models for BC recurrence often focus on limited variables related to tumor, treatment, molecular, and clinical features. Imbalanced classes and the lack of open-source data sets impede model development and validation. Furthermore, existing models predominantly overlook African and Middle Eastern populations, as they are trained and validated mainly on Caucasian and Asian patients.
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Affiliation(s)
- Hasna El Haji
- Department of Radiology, Mayo Clinic, Phoenix, AZ
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ
- International University of Rabat, TICLab, Rabat, Morocco
| | - Amine Souadka
- Surgical Oncology Department, National Institute of Oncology, Mohammed V University in Rabat, Rabat, Morocco
| | - Bhavik N Patel
- Department of Radiology, Mayo Clinic, Phoenix, AZ
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ
| | - Nada Sbihi
- International University of Rabat, TICLab, Rabat, Morocco
| | - Gokul Ramasamy
- Department of Radiology, Mayo Clinic, Phoenix, AZ
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ
| | | | - Mounir Ghogho
- International University of Rabat, TICLab, Rabat, Morocco
- University of Leeds, Faculty of Engineering, Leeds, United Kingdom
| | - Imon Banerjee
- Department of Radiology, Mayo Clinic, Phoenix, AZ
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ
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11
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Fanizzi A, Pomarico D, Rizzo A, Bove S, Comes MC, Didonna V, Giotta F, La Forgia D, Latorre A, Pastena MI, Petruzzellis N, Rinaldi L, Tamborra P, Zito A, Lorusso V, Massafra R. Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancer. Sci Rep 2023; 13:8575. [PMID: 37237020 DOI: 10.1038/s41598-023-35344-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
For endocrine-positive Her2 negative breast cancer patients at an early stage, the benefit of adding chemotherapy to adjuvant endocrine therapy is not still confirmed. Several genomic tests are available on the market but are very expensive. Therefore, there is the urgent need to explore novel reliable and less expensive prognostic tools in this setting. In this paper, we shown a machine learning survival model to estimate Invasive Disease-Free Events trained on clinical and histological data commonly collected in clinical practice. We collected clinical and cytohistological outcomes of 145 patients referred to Istituto Tumori "Giovanni Paolo II". Three machine learning survival models are compared with the Cox proportional hazards regression according to time-dependent performance metrics evaluated in cross-validation. The c-index at 10 years obtained by random survival forest, gradient boosting, and component-wise gradient boosting is stabled with or without feature selection at approximately 0.68 in average respect to 0.57 obtained to Cox model. Moreover, machine learning survival models have accurately discriminated low- and high-risk patients, and so a large group which can be spared additional chemotherapy to hormone therapy. The preliminary results obtained by including only clinical determinants are encouraging. The integrated use of data already collected in clinical practice for routine diagnostic investigations, if properly analyzed, can reduce time and costs of the genomic tests.
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Affiliation(s)
- Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Domenico Pomarico
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Alessandro Rizzo
- Struttura Semplice Dipartimentale di Oncologia Per la Presa in Carico Globale del Paziente Oncologico "Don Tonino Bello", I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Samantha Bove
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Francesco Giotta
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Agnese Latorre
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Maria Irene Pastena
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Nicole Petruzzellis
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Lucia Rinaldi
- Struttura Semplice Dipartimentale di Oncologia Per la Presa in Carico Globale del Paziente Oncologico "Don Tonino Bello", I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Alfredo Zito
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
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12
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Tyagi N, Bhushan B. Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions. WIRELESS PERSONAL COMMUNICATIONS 2023; 130:857-908. [PMID: 37168438 PMCID: PMC10019426 DOI: 10.1007/s11277-023-10312-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/25/2023] [Indexed: 05/13/2023]
Abstract
Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP's recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP's open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities.
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Affiliation(s)
- Nemika Tyagi
- Department of Computer Science and Engineering School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh 201310 India
| | - Bharat Bhushan
- Department of Computer Science and Engineering School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh 201310 India
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13
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Li C, Weng Y, Zhang Y, Wang B. A Systematic Review of Application Progress on Machine Learning-Based Natural Language Processing in Breast Cancer over the Past 5 Years. Diagnostics (Basel) 2023; 13:diagnostics13030537. [PMID: 36766641 PMCID: PMC9913934 DOI: 10.3390/diagnostics13030537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 02/04/2023] Open
Abstract
Artificial intelligence (AI) has been steadily developing in the medical field in the past few years, and AI-based applications have advanced cancer diagnosis. Breast cancer has a massive amount of data in oncology. There has been a high level of research enthusiasm to apply AI techniques to assist in breast cancer diagnosis and improve doctors' efficiency. However, the wise utilization of tedious breast cancer-related medical care is still challenging. Over the past few years, AI-based NLP applications have been increasingly proposed in breast cancer. In this systematic review, we conduct the review using preferred reporting items for systematic reviews and meta-analyses (PRISMA) and investigate the recent five years of literature in natural language processing (NLP)-based AI applications. This systematic review aims to uncover the recent trends in this area, close the research gap, and help doctors better understand the NLP application pipeline. We first conduct an initial literature search of 202 publications from Scopus, Web of Science, PubMed, Google Scholar, and the Association for Computational Linguistics (ACL) Anthology. Then, we screen the literature based on inclusion and exclusion criteria. Next, we categorize and analyze the advantages and disadvantages of the different machine learning models. We also discuss the current challenges, such as the lack of a public dataset. Furthermore, we suggest some promising future directions, including semi-supervised learning, active learning, and transfer learning.
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Affiliation(s)
- Chengtai Li
- School of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China
| | - Ying Weng
- School of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China
- Correspondence:
| | - Yiming Zhang
- School of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China
| | - Boding Wang
- Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo 315010, China
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14
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Pates KM, Shang Z, Periselneris J, Shah A. Breaking the mould, a first parse at natural language processing in aspergillosis diagnosis. J Thorac Dis 2023; 15:17-21. [PMID: 36794142 PMCID: PMC9922601 DOI: 10.21037/jtd-22-1393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 11/25/2022] [Indexed: 01/11/2023]
Affiliation(s)
- Katharine M. Pates
- Department of Respiratory Medicine, King’s College Hospital NHS Foundation Trust, London, UK
| | | | - Jimstan Periselneris
- Department of Respiratory Medicine, King’s College Hospital NHS Foundation Trust, London, UK
| | - Anand Shah
- Royal Brompton and Harefield Clinical Group, Guy’s and St. Thomas’ NHS Foundation Trust, London, UK
- Medical Research Council (MRC) Centre of Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
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15
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Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology. Diagnostics (Basel) 2023; 13:diagnostics13020286. [PMID: 36673096 PMCID: PMC9857980 DOI: 10.3390/diagnostics13020286] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 12/24/2022] [Accepted: 01/05/2023] [Indexed: 01/15/2023] Open
Abstract
In the era of big data, text-based medical data, such as electronic health records (EHR) and electronic medical records (EMR), are growing rapidly. EHR and EMR are collected from patients to record their basic information, lab tests, vital signs, clinical notes, and reports. EHR and EMR contain the helpful information to assist oncologists in computer-aided diagnosis and decision making. However, it is time consuming for doctors to extract the valuable information they need and analyze the information from the EHR and EMR data. Recently, more and more research works have applied natural language processing (NLP) techniques, i.e., rule-based, machine learning-based, and deep learning-based techniques, on the EHR and EMR data for computer-aided diagnosis in oncology. The objective of this review is to narratively review the recent progress in the area of NLP applications for computer-aided diagnosis in oncology. Moreover, we intend to reduce the research gap between artificial intelligence (AI) experts and clinical specialists to design better NLP applications. We originally identified 295 articles from the three electronic databases: PubMed, Google Scholar, and ACL Anthology; then, we removed the duplicated papers and manually screened the irrelevant papers based on the content of the abstract; finally, we included a total of 23 articles after the screening process of the literature review. Furthermore, we provided an in-depth analysis and categorized these studies into seven cancer types: breast cancer, lung cancer, liver cancer, prostate cancer, pancreatic cancer, colorectal cancer, and brain tumors. Additionally, we identified the current limitations of NLP applications on supporting the clinical practices and we suggest some promising future research directions in this paper.
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16
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Chan RC, To CKC, Cheng KCT, Yoshikazu T, Yan LLA, Tse GM. Artificial intelligence in breast cancer histopathology. Histopathology 2023; 82:198-210. [PMID: 36482271 DOI: 10.1111/his.14820] [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: 08/01/2022] [Revised: 09/22/2022] [Accepted: 09/28/2022] [Indexed: 12/13/2022]
Abstract
This is a review on the use of artificial intelligence for digital breast pathology. A systematic search on PubMed was conducted, identifying 17,324 research papers related to breast cancer pathology. Following a semimanual screening, 664 papers were retrieved and pursued. The papers are grouped into six major tasks performed by pathologists-namely, molecular and hormonal analysis, grading, mitotic figure counting, ki-67 indexing, tumour-infiltrating lymphocyte assessment, and lymph node metastases identification. Under each task, open-source datasets for research to build artificial intelligence (AI) tools are also listed. Many AI tools showed promise and demonstrated feasibility in the automation of routine pathology investigations. We expect continued growth of AI in this field as new algorithms mature.
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Affiliation(s)
- Ronald Ck Chan
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Chun Kit Curtis To
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Ka Chuen Tom Cheng
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Tada Yoshikazu
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Lai Ling Amy Yan
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Gary M Tse
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
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17
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Kline A, Wang H, Li Y, Dennis S, Hutch M, Xu Z, Wang F, Cheng F, Luo Y. Multimodal machine learning in precision health: A scoping review. NPJ Digit Med 2022; 5:171. [PMID: 36344814 PMCID: PMC9640667 DOI: 10.1038/s41746-022-00712-8] [Citation(s) in RCA: 69] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 10/14/2022] [Indexed: 11/09/2022] Open
Abstract
Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.
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Affiliation(s)
- Adrienne Kline
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Hanyin Wang
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Yikuan Li
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Saya Dennis
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Meghan Hutch
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Cornell University, New York, 10065, NY, USA
| | - Fei Wang
- Department of Population Health Sciences, Cornell University, New York, 10065, NY, USA
| | - Feixiong Cheng
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, 44195, OH, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA.
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18
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Li Y, Wu X, Yang P, Jiang G, Luo Y. Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:850-866. [PMID: 36462630 PMCID: PMC10025752 DOI: 10.1016/j.gpb.2022.11.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 10/03/2022] [Accepted: 11/17/2022] [Indexed: 12/03/2022]
Abstract
The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. In this review, we provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy, including early detection, auxiliary diagnosis, prognosis prediction, and immunotherapy practice. Moreover, we highlight the challenges and opportunities for future applications of machine learning in lung cancer.
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Affiliation(s)
- Yawei Li
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Xin Wu
- Department of Medicine, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Ping Yang
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905 / Scottsdale, AZ 85259, USA
| | - Guoqian Jiang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN 55905, USA
| | - Yuan Luo
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
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Mazo C, Aura C, Rahman A, Gallagher WM, Mooney C. Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review. J Pers Med 2022; 12:1496. [PMID: 36143281 PMCID: PMC9500690 DOI: 10.3390/jpm12091496] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/05/2022] [Accepted: 09/09/2022] [Indexed: 12/31/2022] Open
Abstract
Breast cancer is the most common disease among women, with over 2.1 million new diagnoses each year worldwide. About 30% of patients initially presenting with early stage disease have a recurrence of cancer within 10 years. Predicting who will have a recurrence and who will not remains challenging, with consequent implications for associated treatment. Artificial intelligence strategies that can predict the risk of recurrence of breast cancer could help breast cancer clinicians avoid ineffective overtreatment. Despite its significance, most breast cancer recurrence datasets are insufficiently large, not publicly available, or imbalanced, making these studies more difficult. This systematic review investigates the role of artificial intelligence in the prediction of breast cancer recurrence. We summarise common techniques, features, training and testing methodologies, metrics, and discuss current challenges relating to implementation in clinical practice. We systematically reviewed works published between 1 January 2011 and 1 November 2021 using the methodology of Kitchenham and Charter. We leveraged Springer, Google Scholar, PubMed, and IEEE search engines. This review found three areas that require further work. First, there is no agreement on artificial intelligence methodologies, feature predictors, or assessment metrics. Second, issues such as sampling strategies, missing data, and class imbalance problems are rarely addressed or discussed. Third, representative datasets for breast cancer recurrence are scarce, which hinders model validation and deployment. We conclude that predicting breast cancer recurrence remains an open problem despite the use of artificial intelligence.
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Affiliation(s)
- Claudia Mazo
- UCD School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Claudia Aura
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Arman Rahman
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, D04 V1W8 Dublin, Ireland
| | - William M. Gallagher
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Catherine Mooney
- UCD School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland
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20
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Li Z, Wang X, Xu M, Li Y, Wang Y, Chen Y, Li S, Li Z, Yang J, Tang C, Xiong F, Jian W, He P, Zhan Y, Zheng J, Ye F. Development and clinical application of an electronic health record quality control system for pulmonary aspergillosis based on guidelines and natural language processing technology. J Thorac Dis 2022; 14:3398-3407. [PMID: 36245604 PMCID: PMC9562533 DOI: 10.21037/jtd-22-532] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 08/19/2022] [Indexed: 11/26/2022]
Abstract
Background There are considerable differences in the diagnosis and treatment of pulmonary aspergillosis (PA) between specialized hospitals and primary hospitals or developed areas and underdeveloped areas in China. There is a lack of electronic systems that assist respiratory physicians in standardizing the diagnosis and treatment of PA. Methods We extracted 26 quality control points from the latest guidelines related to PA, and developed a PA quality control system of electronic health record (EHR) based on natural language processing (NLP) techniques. We obtained PA patient records in the Department of Respiratory Medicine of the First Affiliated Hospital of Guangzhou Medical University to verify the effectiveness of the system comparing with manually evaluation of respiratory experts. Results We successfully developed quality control system of PA; 699 PA medical records from EHR of the First Affiliated Hospital of Guangzhou Medical University between January 2015 and March 2020 were obtained and assessed by the system; 162 defects were found, which included 19 medical records with diagnostic defects, 76 medical records with examination defects, and 80 medical records with treatment defects; 200 medical records were sampled for validation, and found that the sensitivity and accuracy of quality control system for pulmonary aspergillosis (QCSA) were 0.99 and 0.96, F1 value was 0.85, and the recall rate was 0.77 compared with experts' evaluation. Conclusions Our system successfully uses medical guidelines and NLP technology to detect defects in the diagnosis and treatment of PA, which helps to improve the management quality of PA patients.
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Affiliation(s)
- Zhengtu Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xidong Wang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Mengke Xu
- Guangzhou Tianpeng Technology Co., Ltd., Guangzhou, China
| | - Yongming Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yinguang Wang
- Guangzhou Tianpeng Technology Co., Ltd., Guangzhou, China
| | - Yijun Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Shaoqiang Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zhun Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jinglu Yang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Chun Tang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Fangshu Xiong
- Guangzhou Tianpeng Technology Co., Ltd., Guangzhou, China
| | - Wenhua Jian
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Peimei He
- Guangzhou Tianpeng Technology Co., Ltd., Guangzhou, China
| | - Yangqing Zhan
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jinping Zheng
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Feng Ye
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
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21
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Pan LC, Wu XR, Lu Y, Zhang HQ, Zhou YL, Liu X, Liu SL, Yan QY. Artificial intelligence empowered Digital Health Technologies in Cancer Survivorship Care: a scoping review. Asia Pac J Oncol Nurs 2022; 9:100127. [PMID: 36176267 PMCID: PMC9513729 DOI: 10.1016/j.apjon.2022.100127] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/29/2022] [Indexed: 12/03/2022] Open
Abstract
Objective The objectives of this systematic review are to describe features and specific application scenarios for current cancer survivorship care services of Artificial intelligence (AI)-driven digital health technologies (DHTs) and to explore the acceptance and briefly evaluate its feasibility in the application process. Methods Search for literatures published from 2010 to 2022 on sites MEDLINE, IEEE-Xplor, PubMed, Embase, Cochrane Central Register of Controlled Trials and Scopus systematically. The types of literatures include original research, descriptive study, randomized controlled trial, pilot study, and feasible or acceptable study. The literatures above described current status and effectiveness of digital medical technologies based on AI and used in cancer survivorship care services. Additionally, we use QuADS quality assessment tool to evaluate the quality of literatures included in this review. Results 43 studies that met the inclusion criteria were analyzed and qualitatively synthesized. The current status and results related to the application of AI-driven DHTs in cancer survivorship care were reviewed. Most of these studies were designed specifically for breast cancer survivors’ care and focused on the areas of recurrence or secondary cancer prediction, clinical decision support, cancer survivability prediction, population or treatment stratified, anti-cancer treatment-induced adverse reaction prediction, and so on. Applying AI-based DHTs to cancer survivors actually has shown some positive outcomes, including increased motivation of patient-reported outcomes (PROs), reduce fatigue and pain levels, improved quality of life, and physical function. However, current research mostly explored the technology development and formation (testing) phases, with limited-scale population, and single-center trial. Therefore, it is not suitable to draw conclusions that the effectiveness of AI-based DHTs in supportive cancer care, as most of applications are still in the early stage of development and feasibility testing. Conclusions While digital therapies are promising in the care of cancer patients, more high-quality studies are still needed in the future to demonstrate the effectiveness of digital therapies in cancer care. Studies should explore how to develop uniform standards for measuring patient-related outcomes, ensure the scientific validity of research methods, and emphasize patient and health practitioner involvement in the development and use of technology.
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Affiliation(s)
- Lu-Chen Pan
- Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Xiao-Ru Wu
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Ying Lu
- Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Han-Qing Zhang
- Health Science Center, Yangtze University, Jinzhou 434023, China
| | - Yao-Ling Zhou
- Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xue Liu
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Sheng-Lin Liu
- Department of Medical Engineering, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Corresponding authors.
| | - Qiao-Yuan Yan
- Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Corresponding authors.
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22
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de Anda-Trasviña A, Nieto-Garibay A, Gutiérrez J. Natural language report of the composting process status using linguistic perception. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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23
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Zhu Z, Li J, Huang J, Li Z, Zhang H, Chen S, Zhong Q, Xie Y, Hu S, Wang Y, Wang D, Yu G. An intelligent prediagnosis system for disease prediction and examination recommendation based on electronic medical record and a medical-semantic-aware convolution neural network (MSCNN) for pediatric chronic cough. Transl Pediatr 2022; 11:1216-1233. [PMID: 35958012 PMCID: PMC9360821 DOI: 10.21037/tp-22-275] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 07/10/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Due to the phenotypic similarities among different pediatric respiratory diseases with chronic cough, primary doctors often misdiagnose and the misuse of examinations is prevalent. In the pre-diagnosis stage, the patients' chief complaints and other information in the electronic medical record (EMR) provide a powerful reference for respiratory experts to make preliminary disease judgment and examination plan. In this paper, we proposed an intelligent prediagnosis system to predict disease diagnosis and recommend examinations based on EMR text. METHODS We examined the clinical notes of 178,293 children with chronic cough symptoms from retrospective EMR data. The dataset is split into 7:3 for training and testing. From the testing set, we also extract 5% of samples for validation. We proposed a medical-semantic-aware convolution neural network (MSCNN) framework that can accomplish two downstream tasks from the same medical language model through transfer learning. First, a medical language model based on the word2vec algorithm was built to generate embeddings for the text data. Then, text convolutional neural network (TextCNN) was used to build models for disease prediction and examination recommendation. RESULTS We implemented 5 algorithms for disease prediction. In the disease prediction task, our algorithm outperformed the baseline methods on all metrics, with a top-1 accuracy (AC) of 0.68 and a top-3 AC of 0.923 on the testing set. By adding data enhancement, the top-3 AC reached 0.926. In the examination recommendation task, the overall AC on the testing set was 0.93 and the macro average (MA) F1-score was 0.88. The average area under the curve (AUC) on the training set was 0.97 while on the testing set it was 0.86. CONCLUSIONS We constructed an intelligent prediagnosis system with an MSCNN framework that can predict diseases and make examination recommendations based on EMR data. Our approach achieved good results on a retrospective clinical dataset and thus has great potential for the application of automated diagnosis assist in clinical practice during pre-diagnosis stage, which will provide help for primary level doctors or doctors in basic-level hospitals. Due to the generality of the proposed framework, it can be straight forwardly extended to prediagnosis for other diseases.
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Affiliation(s)
- Zhu Zhu
- Department of Data and Information, The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,National Clinical Research Center for Child Health, Hangzhou, China
| | - Jing Li
- Department of Data and Information, The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,National Clinical Research Center for Child Health, Hangzhou, China
| | - Jian Huang
- Department of Data and Information, The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,National Clinical Research Center for Child Health, Hangzhou, China
| | - Zheming Li
- Department of Data and Information, The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,National Clinical Research Center for Child Health, Hangzhou, China
| | | | - Siyu Chen
- Avain (Hangzhou) Technology Co., Ltd., Hangzhou, China
| | - Qianhui Zhong
- Avain (Hangzhou) Technology Co., Ltd., Hangzhou, China
| | - Yulan Xie
- Avain (Hangzhou) Technology Co., Ltd., Hangzhou, China
| | - Shasha Hu
- Department of Data and Information, The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,National Clinical Research Center for Child Health, Hangzhou, China
| | - Yinshuo Wang
- National Clinical Research Center for Child Health, Hangzhou, China.,Department of Pulmonology, The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Dejian Wang
- Department of R&D, Hangzhou Healink Technology, Hangzhou, China
| | - Gang Yu
- Department of Data and Information, The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,National Clinical Research Center for Child Health, Hangzhou, China.,Polytechnic Institute, Zhejiang University, Hangzhou, China
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24
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Wang B, Wang Y, Chen Y, Gao M, Ren J, Guo Y, Situ C, Qi Y, Zhu H, Li Y, Guo X. DeepSCP: utilizing deep learning to boost single-cell proteome coverage. Brief Bioinform 2022; 23:6598882. [DOI: 10.1093/bib/bbac214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/20/2022] [Accepted: 05/06/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computational methods. In this study, we present a novel framework DeepSCP, utilizing deep learning to boost SCP coverage. DeepSCP constructs a series of features of peptide-spectrum matches (PSMs) by predicting the retention time based on the multiple SCP sample sets and fragment ion intensities based on deep learning, and predicts PSM labels with an optimized-ensemble learning model. Evaluation of DeepSCP on public and in-house SCP datasets showed superior performances compared with other state-of-the-art methods. DeepSCP identified more confident peptides and proteins by controlling q-value at 0.01 using target–decoy competition method. As a convenient and low-cost computing framework, DeepSCP will help boost single-cell proteome identification and facilitate the future development and application of single-cell proteomics.
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Affiliation(s)
- Bing Wang
- School of Medicine , Southeast University, Nanjing 210009 , China
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Yue Wang
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Yu Chen
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Mengmeng Gao
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Jie Ren
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Yueshuai Guo
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Chenghao Situ
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Yaling Qi
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Hui Zhu
- Department of Clinical Laboratory , Sir Run Run Hospital, Nanjing Medical University, Nanjing 211166 , China
| | - Yan Li
- School of Medicine , Southeast University, Nanjing 210009 , China
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Xuejiang Guo
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
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Calisto FM, Santiago C, Nunes N, Nascimento JC. BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions. Artif Intell Med 2022; 127:102285. [DOI: 10.1016/j.artmed.2022.102285] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/15/2022] [Accepted: 03/21/2022] [Indexed: 01/19/2023]
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26
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Gui H, Tseng B, Hu W, Wang SY. Looking for low vision: Predicting visual prognosis by fusing structured and free-text data from electronic health records. Int J Med Inform 2022; 159:104678. [PMID: 34999410 PMCID: PMC8792365 DOI: 10.1016/j.ijmedinf.2021.104678] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/24/2021] [Accepted: 12/25/2021] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Low vision rehabilitation improves quality-of-life for visually impaired patients, but referral rates fall short of national guidelines. Automatically identifying, from electronic health records (EHR), patients with poor visual prognosis could allow targeted referrals to low vision services. The purpose of this study was to build and evaluate deep learning models that integrate EHR data that is both structured and free-text to predict visual prognosis. METHODS We identified 5547 patients with low vision (defined as best documented visual acuity (VA) less than 20/40) on ≥ 1 encounter from EHR from 2009 to 2018, with ≥ 1 year of follow-up from the earliest date of low vision, who did not improve to greater than 20/40 over 1 year. Ophthalmology notes on or prior to the index date were extracted. Structured data available from the EHR included demographics, billing and procedure codes, medications, and exam findings including VA, intraocular pressure, corneal thickness, and refraction. To predict whether low vision patients would still have low vision a year later, we developed and compared deep learning models that used structured inputs and free-text progress notes. We compared three different representations of progress notes, including 1) using previously developed ophthalmology domain-specific word embeddings, and representing medical concepts from notes as 2) named entities represented by one-hot vectors and 3) named entities represented as embeddings. Standard performance metrics including area under the receiver operating curve (AUROC) and F1 score were evaluated on a held-out test set. RESULTS Among the 5547 low vision patients in our cohort, 40.7% (N = 2258) never improved to better than 20/40 over one year of follow-up. Our single-modality deep learning model based on structured inputs was able to predict low vision prognosis with AUROC of 80% and F1 score of 70%. Deep learning models utilizing named entity recognition achieved an AUROC of 79% and F1 score of 63%. Deep learning models further augmented with free-text inputs using domain-specific word embeddings, were able to achieve AUROC of 82% and F1 score of 69%, outperforming all single- and multiple-modality models representing text with biomedical concepts extracted through named entity recognition pipelines. DISCUSSION Free text progress notes within the EHR provide valuable information relevant to predicting patients' visual prognosis. We observed that representing free-text using domain-specific word embeddings led to better performance than representing free-text using extracted named entities. The incorporation of domain-specific embeddings improved the performance over structured models, suggesting that domain-specific text representations may be especially important to the performance of predictive models in highly subspecialized fields such as ophthalmology.
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Affiliation(s)
- Haiwen Gui
- Stanford University School of Medicine, Stanford, United States
| | - Benjamin Tseng
- Byers Eye Institute, Department of Ophthalmology, Stanford University, Palo Alto, United States
| | - Wendeng Hu
- Byers Eye Institute, Department of Ophthalmology, Stanford University, Palo Alto, United States
| | - Sophia Y Wang
- Byers Eye Institute, Department of Ophthalmology, Stanford University, Palo Alto, United States.
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27
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Automated Breast Cancer Detection Models Based on Transfer Learning. SENSORS 2022; 22:s22030876. [PMID: 35161622 PMCID: PMC8838322 DOI: 10.3390/s22030876] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/28/2021] [Accepted: 01/19/2022] [Indexed: 02/06/2023]
Abstract
Breast cancer is among the leading causes of mortality for females across the planet. It is essential for the well-being of women to develop early detection and diagnosis techniques. In mammography, focus has contributed to the use of deep learning (DL) models, which have been utilized by radiologists to enhance the needed processes to overcome the shortcomings of human observers. The transfer learning method is being used to distinguish malignant and benign breast cancer by fine-tuning multiple pre-trained models. In this study, we introduce a framework focused on the principle of transfer learning. In addition, a mixture of augmentation strategies were used to prevent overfitting and produce stable outcomes by increasing the number of mammographic images; including several rotation combinations, scaling, and shifting. On the Mammographic Image Analysis Society (MIAS) dataset, the proposed system was evaluated and achieved an accuracy of 89.5% using (residual network-50) ResNet50, and achieved an accuracy of 70% using the Nasnet-Mobile network. The proposed system demonstrated that pre-trained classification networks are significantly more effective and efficient, making them more acceptable for medical imaging, particularly for small training datasets.
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28
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Sundus KI, Hammo BH, Al-Zoubi MB, Al-Omari A. Solving the multicollinearity problem to improve the stability of machine learning algorithms applied to a fully annotated breast cancer dataset. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.101088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022] Open
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29
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Shi X, Huang X, Chen R, Li Y, Xu Y, Zhang W, Zhu Q, Zha X, Wang J. The transcribed ultraconserved element uc.51 promotes the proliferation and metastasis of breast cancer by stabilizing NONO. Clin Exp Metastasis 2021; 38:551-571. [PMID: 34714466 DOI: 10.1007/s10585-021-10128-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 10/15/2021] [Indexed: 12/22/2022]
Abstract
Long noncoding RNAs have recently emerged as significant contributors to cancers, including breast cancer (BC). One class of long noncoding RNAs called transcribed ultraconserved regions (T-UCRs) is highly conserved in many species and closely related to diverse physiological and pathological processes. However, the function of T-UCRs in BC remains largely unclear. In this study, we identified uc.51, a T-UCR that is overexpressed in both BC tissues and cell lines and is correlated with larger tumor size. Loss- and gain-of-function assays were performed in vitro and demonstrated that uc.51 promotes the proliferation, migration, and invasion of BC cells. Mechanistically, non-POU domain-containing octamer-binding protein (NONO) was found to physically interact with uc.51 by RNA pulldown followed by mass spectrometry. This interaction was further verified by RNA immunoprecipitation. Moreover, uc.51 positively regulated the expression of NONO, maintained its stability through the ubiquitin-proteasome system, and activated the phosphorylation of CREB. Rescue experiments demonstrated that NONO overexpression compensated for the attenuated influence on BC progression resulting from downregulation of uc.51, indicating that NONO functions downstream of uc.51. In vivo functional experiments also revealed a positive correlation between uc.51 expression and tumor size. Ki-67 and NONO levels in the lv-uc.51-shRNA group were decreased compared with those in the lv-con-shRNA group, according to the immunohistochemical staining results, and a decreased incidence of distant metastasis was observed in the lv-uc.51-shRNA group in the xenograft model. Collectively, our results reveal a substantial role for the uc.51-NONO axis in BC progression and indicate that the uc.51-NONO axis has potential to be a therapeutic target for BC.
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Affiliation(s)
- Xiaoqing Shi
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210000, China
| | - Xiaofeng Huang
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210000, China
| | - Rui Chen
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210000, China
| | - Yan Li
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210000, China
| | - Yinggang Xu
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210000, China
| | - Weiwei Zhang
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210000, China
| | - Qiannan Zhu
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210000, China
| | - Xiaoming Zha
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210000, China.
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, 210000, China.
| | - Jue Wang
- Department of Breast Disease, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210000, China.
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, 210000, China.
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Hufnagl P. [EMPAIA-ecosystem for pathology diagnostics with AI assistance]. DER PATHOLOGE 2021; 42:135-141. [PMID: 34919184 DOI: 10.1007/s00292-021-01029-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/03/2021] [Indexed: 11/26/2022]
Abstract
Applications of deep learning and other artificial intelligence techniques play an increasing role in pathological research. In contrast to research, applications in clinical routine are rare so far, although the first certified solutions have already been established (analysis of prostate sections, ER, PR, and Her2 in breast cancer). Besides the still low use of virtual microscopy in practice, there are a number of hurdles that stand in the way of a rapid diffusion of AI applications. The EMPAIA project has a goal of removing these hurdles. The path taken to build an ecosystem for this purpose is intended to exemplify that the introduction of AI applications in image-based diagnostics is possible on a broad basis if the existing hurdles are removed in a joint, moderated process. The components of the EMPAIA ecosystem and its strategy will be described, and reference will be made to the technical solution approaches.
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Affiliation(s)
- Peter Hufnagl
- Institut für Pathologie (Rudolf-Virchow-Haus), Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Deutschland.
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Yasnitsky LN, Dumler AA, Cherepanov FM, Yasnitsky VL, Uteva NA. Capabilities of neural network technologies for extracting new medical knowledge and enhancing precise decision making for patients. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2021. [DOI: 10.1080/23808993.2021.1993595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Leonid N. Yasnitsky
- Department of Applied Mathematics and Informatics, Perm State University, Perm, Russia
- Department of Information Technology in Business, Higher School of Economics, Perm, Russia
| | - Andrey A. Dumler
- Department of Propedeutics of Internal Diseases No. 1, Perm State Medical University, Perm, Russia
| | - Fedor M. Cherepanov
- Department of Applied Informatics, Perm State University for Humanities and Pedagogy, Perm, Russia
| | - Vitaly L. Yasnitsky
- Department of Computational Mathematics, Mechanics and Biomechanics, Perm National Research Polytechnic University, Perm, Russia
| | - Natalia A. Uteva
- Department of Propedeutics of Internal Diseases No. 1, Perm State Medical University, Perm, Russia
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Li J, Zhou Z, Dong J, Fu Y, Li Y, Luan Z, Peng X. Predicting breast cancer 5-year survival using machine learning: A systematic review. PLoS One 2021; 16:e0250370. [PMID: 33861809 PMCID: PMC8051758 DOI: 10.1371/journal.pone.0250370] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 04/06/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Accurately predicting the survival rate of breast cancer patients is a major issue for cancer researchers. Machine learning (ML) has attracted much attention with the hope that it could provide accurate results, but its modeling methods and prediction performance remain controversial. The aim of this systematic review is to identify and critically appraise current studies regarding the application of ML in predicting the 5-year survival rate of breast cancer. METHODS In accordance with the PRISMA guidelines, two researchers independently searched the PubMed (including MEDLINE), Embase, and Web of Science Core databases from inception to November 30, 2020. The search terms included breast neoplasms, survival, machine learning, and specific algorithm names. The included studies related to the use of ML to build a breast cancer survival prediction model and model performance that can be measured with the value of said verification results. The excluded studies in which the modeling process were not explained clearly and had incomplete information. The extracted information included literature information, database information, data preparation and modeling process information, model construction and performance evaluation information, and candidate predictor information. RESULTS Thirty-one studies that met the inclusion criteria were included, most of which were published after 2013. The most frequently used ML methods were decision trees (19 studies, 61.3%), artificial neural networks (18 studies, 58.1%), support vector machines (16 studies, 51.6%), and ensemble learning (10 studies, 32.3%). The median sample size was 37256 (range 200 to 659820) patients, and the median predictor was 16 (range 3 to 625). The accuracy of 29 studies ranged from 0.510 to 0.971. The sensitivity of 25 studies ranged from 0.037 to 1. The specificity of 24 studies ranged from 0.008 to 0.993. The AUC of 20 studies ranged from 0.500 to 0.972. The precision of 6 studies ranged from 0.549 to 1. All of the models were internally validated, and only one was externally validated. CONCLUSIONS Overall, compared with traditional statistical methods, the performance of ML models does not necessarily show any improvement, and this area of research still faces limitations related to a lack of data preprocessing steps, the excessive differences of sample feature selection, and issues related to validation. Further optimization of the performance of the proposed model is also needed in the future, which requires more standardization and subsequent validation.
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Affiliation(s)
- Jiaxin Li
- School of Nursing, Jilin University, Jilin, China
| | - Zijun Zhou
- Breast Surgery, Jilin Province Tumor Hospital, Jilin, China
| | - Jianyu Dong
- School of Nursing, Jilin University, Jilin, China
| | - Ying Fu
- School of Nursing, Jilin University, Jilin, China
| | - Yuan Li
- School of Nursing, Jilin University, Jilin, China
| | - Ze Luan
- School of Nursing, Jilin University, Jilin, China
| | - Xin Peng
- School of Nursing, Jilin University, Jilin, China
- * E-mail:
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Cardoso MJ, Houssami N, Pozzi G, Séroussi B. Artificial intelligence (AI) in breast cancer care - Leveraging multidisciplinary skills to improve care. Breast 2020; 56:110-113. [PMID: 33308879 PMCID: PMC7982546 DOI: 10.1016/j.breast.2020.11.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Affiliation(s)
- Maria Joao Cardoso
- Breast Unit, Champalimaud Clinical Center, Champalimaud Foundation, Lisbon, Portugal; Faculdade de Medicina da Universidade Nova de Lisboa, Nova Medical School, Lisbon, Portugal
| | - Nehmat Houssami
- Sydney School of Public Health, Faculty of Medicine and Health, Fisher Road, The University of Sydney, New South Wales, Australia
| | - Giuseppe Pozzi
- DEIB, Politecnico di Milano, P.za L. da Vinci 32, I-20133, Milano, Italy
| | - Brigitte Séroussi
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, LIMICS UMR_S 1142, F-75006, Paris, France; Assistance Publique - Hôpitaux de Paris, Département de Santé Publique, F-75020, Paris, France
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Cardoso MJ, Houssami N, Pozzi G, Séroussi B. Artificial Intelligence (AI) in Breast Cancer Care - Leveraging multidisciplinary skills to improve care. Artif Intell Med 2020. [DOI: 10.1016/j.artmed.2020.102000] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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