Opportunities and Challenges for Machine Learning in Rare Diseases
Rare diseases (RDs) are complicated health conditions that are difficult to be managed at several levels. The scarcity of available data chiefly determines an intricate scenario even for experts and specialized clinicians, which in turn leads to the so called “diagnostic odyssey” for the patient. Th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-10-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2021.747612/full |
| _version_ | 1818587450898907136 |
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| author | Sergio Decherchi Elena Pedrini Marina Mordenti Andrea Cavalli Andrea Cavalli Luca Sangiorgi |
| author_facet | Sergio Decherchi Elena Pedrini Marina Mordenti Andrea Cavalli Andrea Cavalli Luca Sangiorgi |
| author_sort | Sergio Decherchi |
| collection | DOAJ |
| description | Rare diseases (RDs) are complicated health conditions that are difficult to be managed at several levels. The scarcity of available data chiefly determines an intricate scenario even for experts and specialized clinicians, which in turn leads to the so called “diagnostic odyssey” for the patient. This situation calls for innovative solutions to support the decision process via quantitative and automated tools. Machine learning brings to the stage a wealth of powerful inference methods; however, matching the health conditions with advanced statistical techniques raises methodological, technological, and even ethical issues. In this contribution, we critically point to the specificities of the dialog of rare diseases with machine learning techniques concentrating on the key steps and challenges that may hamper or create actionable knowledge and value for the patient together with some on-field methodological suggestions and considerations. |
| first_indexed | 2024-12-16T09:09:04Z |
| format | Article |
| id | doaj.art-455a4f9ea3d644699ea66a2e0aea8ac3 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-12-16T09:09:04Z |
| publishDate | 2021-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-455a4f9ea3d644699ea66a2e0aea8ac32022-12-21T22:37:01ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2021-10-01810.3389/fmed.2021.747612747612Opportunities and Challenges for Machine Learning in Rare DiseasesSergio Decherchi0Elena Pedrini1Marina Mordenti2Andrea Cavalli3Andrea Cavalli4Luca Sangiorgi5Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, ItalyDepartment of Rare Skeletal Disorders, IRCCS Istituto Ortopedico Rizzoli, Bologna, ItalyDepartment of Rare Skeletal Disorders, IRCCS Istituto Ortopedico Rizzoli, Bologna, ItalyComputational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, ItalyDepartment of Pharmacy and Biotechnology (FaBiT), Alma Mater Studiorum – University of Bologna, Bologna, ItalyDepartment of Rare Skeletal Disorders, IRCCS Istituto Ortopedico Rizzoli, Bologna, ItalyRare diseases (RDs) are complicated health conditions that are difficult to be managed at several levels. The scarcity of available data chiefly determines an intricate scenario even for experts and specialized clinicians, which in turn leads to the so called “diagnostic odyssey” for the patient. This situation calls for innovative solutions to support the decision process via quantitative and automated tools. Machine learning brings to the stage a wealth of powerful inference methods; however, matching the health conditions with advanced statistical techniques raises methodological, technological, and even ethical issues. In this contribution, we critically point to the specificities of the dialog of rare diseases with machine learning techniques concentrating on the key steps and challenges that may hamper or create actionable knowledge and value for the patient together with some on-field methodological suggestions and considerations.https://www.frontiersin.org/articles/10.3389/fmed.2021.747612/fullmachine learningrare diseasedisease registryopen dataclinical decision support system |
| spellingShingle | Sergio Decherchi Elena Pedrini Marina Mordenti Andrea Cavalli Andrea Cavalli Luca Sangiorgi Opportunities and Challenges for Machine Learning in Rare Diseases Frontiers in Medicine machine learning rare disease disease registry open data clinical decision support system |
| title | Opportunities and Challenges for Machine Learning in Rare Diseases |
| title_full | Opportunities and Challenges for Machine Learning in Rare Diseases |
| title_fullStr | Opportunities and Challenges for Machine Learning in Rare Diseases |
| title_full_unstemmed | Opportunities and Challenges for Machine Learning in Rare Diseases |
| title_short | Opportunities and Challenges for Machine Learning in Rare Diseases |
| title_sort | opportunities and challenges for machine learning in rare diseases |
| topic | machine learning rare disease disease registry open data clinical decision support system |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2021.747612/full |
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