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Novikava N, Redjdal A, Bouaud J, Seroussi B. Clinical Decision Support Systems Applied to the Management of Breast Cancer Patients: A Scoping Review. Stud Health Technol Inform 2023; 305:353-356. [PMID: 37387037 DOI: 10.3233/shti230503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Breast cancer is the most commonly diagnosed cancer worldwide, and its burden has been rising over the past decades. A significant advance in healthcare is the integration of Clinical Decision Support Systems (CDSSs) into medical practice, which support healthcare professionals improving clinical decisions, leading to recommended patient-specific treatments and enhanced patient care. Breast cancer CDSSs are thus currently expanding, whether applied to screening, diagnostic, therapeutic or follow-up tasks. We conducted a scoping review to study their availability and use in practice. Except risk calculators, very few CDSSs are currently routinely used.
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Affiliation(s)
- Natallia Novikava
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, F-75006 Paris, France
| | - Akram Redjdal
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, F-75006 Paris, France
| | - Jacques Bouaud
- AP-HP, DRCI, Paris, France
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, F-75006 Paris, France
| | - Brigitte Seroussi
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, F-75006 Paris, France
- AP-HP, Hôpital Tenon, Paris, France
- APREC, Paris, France
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Azarpira M, Redjdal A, Bouaud J, Seroussi B. Methods Used to Compare Narrative Clinical Practice Guidelines: A Scoping Review. Stud Health Technol Inform 2022; 295:304-307. [PMID: 35773869 DOI: 10.3233/shti220723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Guideline-based clinical decision support systems (CDSSs) need the most recent evidence for reliable performance, making the provision of regularly updated clinical practice guidelines (CPGs) a major issue. Some international guidelines are renewed in short intervals and can be used for checking the status of given national guidelines with regard to the most recent evidence. Considering the volume of medical data and the number of CPGs published, computerized comparison of clinical guidelines can be an effective method. We performed a scoping review to evaluate the methods used for comparing two CPGs. We searched for methods for extracting CPG components and for methods used for comparing CPGs at different levels of abstraction. In each case, computerized and semi-computerized methods were recognized. Expert knowledge has yet a determinant role for assessing the comparisons, this role being more prominent for the extraction of semantic rules and the resolution of inconsistencies.
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Affiliation(s)
- Mohammadreza Azarpira
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, F-75006 Paris, France
| | - Akram Redjdal
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, F-75006 Paris, France
| | - Jacques Bouaud
- AP-HP, DRCI, Paris, France
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, F-75006 Paris, France
| | - Brigitte Seroussi
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, F-75006 Paris, France
- AP-HP, Hôpital Tenon, Paris, France
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Redjdal A, Bouaud J, Gligorov J, Séroussi B. Comparison of MetaMap, cTAKES, SIFR, and ECMT to Annotate Breast Cancer Patient Summaries. Stud Health Technol Inform 2022; 290:187-191. [PMID: 35672997 DOI: 10.3233/shti220058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Most clinical texts including breast cancer patient summaries (BCPSs) are elaborated as narrative documents difficult to process by decision support systems. Annotators have been developed to extract the relevant content of such documents, e.g., MetaMap and cTAKES, that work with the English language and perform concept mapping using UMLS, SIFR and ECMT, that work for the French language and provide concepts using various terminologies. We compared the four annotators on a sample of 25 French BCPSs, pre-processed to manage acronyms and translated in English. We observed that MetaMap extracted the largest number of UMLS concepts (15,458), followed by SIFR (3,784), ECMT (1,962), and cTAKES (1,769). Each annotator extracted specific valuable information, not proposed by the other annotators. Considered as complementary, all annotators should be used in sequence to optimize the results.
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Affiliation(s)
- Akram Redjdal
- Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMRS_1142, LIMICS, Paris, France
| | - Jacques Bouaud
- AP-HP, DRCI, Paris, France
- Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMRS_1142, LIMICS, Paris, France
| | - Joseph Gligorov
- Sorbonne Université, Institut Universitaire de Cancérologie, Paris, France
- AP-HP, Hôpital Tenon, Paris, France
| | - Brigitte Séroussi
- Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMRS_1142, LIMICS, Paris, France
- AP-HP, Hôpital Tenon, Paris, France
- APREC, Paris, France
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Le Thien MA, Redjdal A, Bouaud J, Séroussi B. Using Machine Learning on Imbalanced Guideline Compliance Data to Optimize Multidisciplinary Tumour Board Decision Making for the Management of Breast Cancer Patients. Stud Health Technol Inform 2022; 290:787-788. [PMID: 35673125 DOI: 10.3233/shti220186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Complex breast cancer cases that need further multidisciplinary tumor board (MTB) discussions should have priority in the organization of MTBs. In order to optimize MTB workflow, we attempted to predict complex cases defined as non-compliant cases despite the use of the decision support system OncoDoc, through the implementation of machine learning procedures and algorithms (Decision Trees, Random Forests, and XGBoost). F1-score after cross-validation, sampling implementation, with or without feature selection, did not exceed 40%.
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Affiliation(s)
- My-Anh Le Thien
- Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMR S_1142, LIMICS, Paris, France
| | - Akram Redjdal
- Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMR S_1142, LIMICS, Paris, France
| | - Jacques Bouaud
- AP-HP, DRCI, Paris, France
- Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMR S_1142, LIMICS, Paris, France
| | - Brigitte Séroussi
- Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMR S_1142, LIMICS, Paris, France
- AP-HP, Hôpital Tenon, Paris, France
- APREC, Paris, France
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Redjdal A, Bouaud J, Gligorov J, Seroussi B. Using Machine Learning and Deep Learning Methods to Predict the Complexity of Breast Cancer Cases. Stud Health Technol Inform 2022; 294:78-82. [PMID: 35612020 DOI: 10.3233/shti220400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In many countries, the management of cancer patients must be discussed in multidisciplinary tumor boards (MTBs). These meetings have been introduced to provide a collaborative and multidisciplinary approach to cancer care. However, the benefits of MTBs are now being challenged because there are a lot of cases and not enough time to discuss all the of them. During the evaluation of the guideline-based clinical decision support system (CDSS) of the DESIREE project, we found that for some clinical cases, the system did not produce recommendations. We assumed that these cases were complex clinical cases and needed deeper MTB discussions. In this work, we trained and tested several machine learning and deep learning algorithms on a labelled sample of 298 breast cancer patient summaries, to predict the complexity of a breast cancer clinical case. XGboost and multi-layer perceptron were the models with the best result, with an F1 score of 83%.
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Affiliation(s)
- Akram Redjdal
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, F-75006 Paris, France
| | - Jacques Bouaud
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, F-75006 Paris, France
| | - Joseph Gligorov
- Sorbonne Université, Institut Universitaire de Cancérologie, Paris, France.,AP-HP, Hôpital Tenon, Paris, France
| | - Brigitte Seroussi
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, F-75006 Paris, France.,AP-HP, Hôpital Tenon, Paris, France.,APREC, Paris, France
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Le Thien MA, Redjdal A, Bouaud J, Seroussi B. Deep Learning, a Not so Magical Problem Solver: A Case Study with Predicting the Complexity of Breast Cancer Cases. Stud Health Technol Inform 2021; 287:144-148. [PMID: 34795099 DOI: 10.3233/shti210834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Using guideline-based clinical decision support systems (CDSSs) has improved clinical practice, especially during multidisciplinary tumour boards (MTBs) in cancer patient management. However, MTBs have been reported to be overcrowded, with limited time to discuss all cases. Complex breast cancer cases that need further MTB discussions should have priority in the organization of MTBs. In order to optimize MTB workflow, we attempted to predict complex cases defined as non-compliant cases despite the use of the decision support system OncoDoc. After previously obtaining insufficient performance with machine learning algorithms, we tested Multi Layer Perceptron for classification, compared various samplers to compensate data imbalance combined with cross- validation, and optimized all models with hyperparameter tuning and feature selection with no improvement and lacklustre results (F1-score: 31.4%).
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Affiliation(s)
- My-Anh Le Thien
- Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMR S_1142, LIMICS, Paris, France
| | - Akram Redjdal
- Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMR S_1142, LIMICS, Paris, France
| | - Jacques Bouaud
- AP-HP, DRCI, Paris, France
- Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMR S_1142, LIMICS, Paris, France
| | - Brigitte Seroussi
- Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMR S_1142, LIMICS, Paris, France
- AP-HP, Hôpital Tenon, Paris, France
- APREC, Paris, France
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Redjdal A, Bouaud J, Gligorov J, Séroussi B. Are Semantic Annotators Able to Extract Relevant Complexity-Related Concepts from Clinical Notes? Stud Health Technol Inform 2021; 287:153-157. [PMID: 34795101 DOI: 10.3233/shti210836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Clinical decision support systems (CDSSs) implementing cancer clinical practice guidelines (CPGs) have the potential to improve the compliance of decisions made by multidisciplinary tumor boards (MTB) with CPGs. However, guideline-based CDSSs do not cover complex cases and need time for discussion. We propose to learn how to predict complex cancer cases prior to MTBs from breast cancer patient summaries (BCPSs) resuming clinical notes. BCPSs being unstructured natural language textual documents, we implemented four semantic annotators (ECMT, SIFR, cTAKES, and MetaMap) to assess whether complexity-related concepts could be extracted from clinical notes. On a sample of 24 BCPSs covering 35 complexity reasons, ECMT and MetaMap were the most efficient systems with a performance rate of 60% (21/35) and 49% (17/35), respectively. When using the four annotators in sequence, 69% of complexity reasons were extracted (24/35 reasons).
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Affiliation(s)
- Akram Redjdal
- Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMRS_1142, LIMICS, Paris, France
| | - Jacques Bouaud
- Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMRS_1142, LIMICS, Paris, France.,AP-HP, DRCI, Paris, France
| | - Joseph Gligorov
- Sorbonne Université, Institut Universitaire de Cancérologie, Paris, France.,AP-HP, Hôpital Tenon, Paris, France
| | - Brigitte Séroussi
- Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMRS_1142, LIMICS, Paris, France.,AP-HP, Hôpital Tenon, Paris, France.,APREC, Paris, France
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Redjdal A, Bouaud J, Guézennec G, Gligorov J, Seroussi B. Creating Synthetic Patients to Address Interoperability Issues: A Case Study with the Management of Breast Cancer Patients. Stud Health Technol Inform 2020; 275:177-181. [PMID: 33227764 DOI: 10.3233/shti200718] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Interoperability issues are common in biomedical informatics. Reusing data generated from a system in another system, or integrating an existing clinical decision support system (CDSS) in a new organization is a complex task due to recurrent problems of concept mapping and alignment. The GL-DSS of the DESIREE project is a guideline-based CDSS to support the management of breast cancer patients. The knowledge base is formalized as an ontology and decision rules. OncoDoc is another CDSS applied to breast cancer management. The knowledge base is structured as a decision tree. OncoDoc has been routinely used by the multidisciplinary tumor board physicians of the Tenon Hospital (Paris, France) for three years leading to the resolution of 1,861 exploitable decisions. Because we were lacking patient data to assess the DESIREE GL-DSS, we investigated the option of reusing OncoDoc patient data. Taking into account that we have two CDSSs with two formalisms to represent clinical practice guidelines and two knowledge representation models, we had to face semantic and structural interoperability issues. This paper reports how we created 10,681 synthetic patients to solve these issues and make OncoDoc data re-usable by the GL-DSS of DESIREE.
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Affiliation(s)
- Akram Redjdal
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, UMR S_1142, LIMICS, Paris, France
| | - Jacques Bouaud
- AP-HP, DRCI, Paris, France
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, UMR S_1142, LIMICS, Paris, France
| | - Gilles Guézennec
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, UMR S_1142, LIMICS, Paris, France
| | - Joseph Gligorov
- AP-HP, Hôpital Tenon, Paris, France
- Sorbonne Université, Institut Universitaire de Cancérologie, Paris, France
| | - Brigitte Seroussi
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, UMR S_1142, LIMICS, Paris, France
- AP-HP, Hôpital Tenon, Paris, France
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