A framework for implementing machine learning in healthcare based on the concepts of preconditions and postconditions
Machine learning is a powerful tool that can be used to solve a wide range of problems in various applications and industries. The healthcare sector has faced specific challenges that have kept machine learning algorithms from becoming as widely and quickly adopted as in other industries. Data acces...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-11-01
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| Series: | Healthcare Analytics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442523000229 |
| _version_ | 1827917386721787904 |
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| author | Colin MacKay William Klement Peter Vanberkel Nathan Lamond Robin Urquhart Matthew Rigby |
| author_facet | Colin MacKay William Klement Peter Vanberkel Nathan Lamond Robin Urquhart Matthew Rigby |
| author_sort | Colin MacKay |
| collection | DOAJ |
| description | Machine learning is a powerful tool that can be used to solve a wide range of problems in various applications and industries. The healthcare sector has faced specific challenges that have kept machine learning algorithms from becoming as widely and quickly adopted as in other industries. Data access and management challenges, ethical considerations, safety, and physician and patient perception present bigger barriers to implementation than model performance. In this paper, we propose adapting and customizing the concept of preconditions and postconditions from software engineering to develop a framework based on required clinical parameters and expected clinical output that will help bridge identified gaps in the implementation of machine learning tools in health care. |
| first_indexed | 2024-03-13T03:28:04Z |
| format | Article |
| id | doaj.art-212b73f19cbb4a198e0f385936b1f49f |
| institution | Directory Open Access Journal |
| issn | 2772-4425 |
| language | English |
| last_indexed | 2024-03-13T03:28:04Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj.art-212b73f19cbb4a198e0f385936b1f49f2023-06-25T04:44:12ZengElsevierHealthcare Analytics2772-44252023-11-013100155A framework for implementing machine learning in healthcare based on the concepts of preconditions and postconditionsColin MacKay0William Klement1Peter Vanberkel2Nathan Lamond3Robin Urquhart4Matthew Rigby5Division of Otolaryngology – Head & Neck Surgery, Nova Scotia Health, Halifax, Canada; Interdisciplinary Ph.D. Department, Dalhousie University, Halifax, Canada; Correspondence to: QEII Health Sciences Centre, Rm 3191 Dickson Centre, 5820 University Ave., Halifax, NS, B3H 2Y9, Canada.Faculty of Computer Science, Dalhousie University, Halifax, Canada; The Ottawa Hospital Research Institute, Ottawa, ON, Canada; Thoracic Surgery, The Ottawa Hospital, Ottawa, ON, CanadaDepartment of Industrial Engineering, Dalhousie University, Halifax, CanadaDivision of Medical Oncology, Nova Scotia Health, Halifax, CanadaDepartment of Community Health and Epidemiology, Dalhousie University, Halifax, Canada; Department of Surgery, Dalhousie University, Halifax, CanadaDivision of Otolaryngology – Head & Neck Surgery, Nova Scotia Health, Halifax, Canada; Department of Surgery, Dalhousie University, Halifax, CanadaMachine learning is a powerful tool that can be used to solve a wide range of problems in various applications and industries. The healthcare sector has faced specific challenges that have kept machine learning algorithms from becoming as widely and quickly adopted as in other industries. Data access and management challenges, ethical considerations, safety, and physician and patient perception present bigger barriers to implementation than model performance. In this paper, we propose adapting and customizing the concept of preconditions and postconditions from software engineering to develop a framework based on required clinical parameters and expected clinical output that will help bridge identified gaps in the implementation of machine learning tools in health care.http://www.sciencedirect.com/science/article/pii/S2772442523000229Machine learningHealthcarePreconditionsPostconditionsRequired clinical parametersExpected clinical output |
| spellingShingle | Colin MacKay William Klement Peter Vanberkel Nathan Lamond Robin Urquhart Matthew Rigby A framework for implementing machine learning in healthcare based on the concepts of preconditions and postconditions Healthcare Analytics Machine learning Healthcare Preconditions Postconditions Required clinical parameters Expected clinical output |
| title | A framework for implementing machine learning in healthcare based on the concepts of preconditions and postconditions |
| title_full | A framework for implementing machine learning in healthcare based on the concepts of preconditions and postconditions |
| title_fullStr | A framework for implementing machine learning in healthcare based on the concepts of preconditions and postconditions |
| title_full_unstemmed | A framework for implementing machine learning in healthcare based on the concepts of preconditions and postconditions |
| title_short | A framework for implementing machine learning in healthcare based on the concepts of preconditions and postconditions |
| title_sort | framework for implementing machine learning in healthcare based on the concepts of preconditions and postconditions |
| topic | Machine learning Healthcare Preconditions Postconditions Required clinical parameters Expected clinical output |
| url | http://www.sciencedirect.com/science/article/pii/S2772442523000229 |
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