Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support “Learning Health Systems” with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S215335392300161X |
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| _version_ | 1846122082486714368 |
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| author | Ricardo Gonzalez Ashirbani Saha Clinton J.V. Campbell Peyman Nejat Cynthia Lokker Andrew P. Norgan |
| author_facet | Ricardo Gonzalez Ashirbani Saha Clinton J.V. Campbell Peyman Nejat Cynthia Lokker Andrew P. Norgan |
| author_sort | Ricardo Gonzalez |
| collection | DOAJ |
| description | This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support “Learning Health Systems” with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support ''Learning Health Systems'' by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented. |
| format | Article |
| id | doaj-art-1a405e680f494562b672927c3248bd2c |
| institution | Kabale University |
| issn | 2153-3539 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj-art-1a405e680f494562b672927c3248bd2c2024-12-15T06:15:05ZengElsevierJournal of Pathology Informatics2153-35392024-12-0115100347Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunitiesRicardo Gonzalez0Ashirbani Saha1Clinton J.V. Campbell2Peyman Nejat3Cynthia Lokker4Andrew P. Norgan5DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada; Division of Computational Pathology and Artificial Intelligence, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States; Corresponding author at: DeGroote School of Business, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S4M4, Canada.Department of Oncology, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada; Escarpment Cancer Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Ontario, CanadaWilliam Osler Health System, Brampton, Ontario, Canada; Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, CanadaDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United StatesHealth Information Research Unit, Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, CanadaDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United StatesThis paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support “Learning Health Systems” with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support ''Learning Health Systems'' by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented.http://www.sciencedirect.com/science/article/pii/S215335392300161XPathologyArtificial intelligenceMachine learningLearning health systemImage processingComputer-assisted |
| spellingShingle | Ricardo Gonzalez Ashirbani Saha Clinton J.V. Campbell Peyman Nejat Cynthia Lokker Andrew P. Norgan Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities Journal of Pathology Informatics Pathology Artificial intelligence Machine learning Learning health system Image processing Computer-assisted |
| title | Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities |
| title_full | Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities |
| title_fullStr | Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities |
| title_full_unstemmed | Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities |
| title_short | Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities |
| title_sort | seeing the random forest through the decision trees supporting learning health systems from histopathology with machine learning models challenges and opportunities |
| topic | Pathology Artificial intelligence Machine learning Learning health system Image processing Computer-assisted |
| url | http://www.sciencedirect.com/science/article/pii/S215335392300161X |
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