التفاصيل البيبلوغرافية
| العنوان: |
Table_1_An integration engineering framework for machine learning in healthcare.pdf |
| المؤلفون: |
Azadeh Assadi, Peter C. Laussen, Andrew J. Goodwin, Sebastian Goodfellow, William Dixon, Robert W. Greer, Anusha Jegatheeswaran, Devin Singh, Melissa McCradden, Sara N. Gallant, Anna Goldenberg, Danny Eytan, Mjaye L. Mazwi |
| سنة النشر: |
2022 |
| المجموعة: |
Frontiers: Figshare |
| مصطلحات موضوعية: |
Health Information Systems (incl. Surveillance), Health Informatics, Mobile Technologies, Innovation and Technology Management, Integration engineering, artificial intelligence, machine learning, digital health, system of systems (SoS), human factors engineering (HFE), healthcare (MeSH) |
| الوصف: |
Background and Objectives Machine Learning offers opportunities to improve patient outcomes, team performance, and reduce healthcare costs. Yet only a small fraction of all Machine Learning models for health care have been successfully integrated into the clinical space. There are no current guidelines for clinical model integration, leading to waste, unnecessary costs, patient harm, and decreases in efficiency when improperly implemented. Systems engineering is widely used in industry to achieve an integrated system of systems through an interprofessional collaborative approach to system design, development, and integration. We propose a framework based on systems engineering to guide the development and integration of Machine Learning models in healthcare. Methods Applied systems engineering, software engineering and health care Machine Learning software development practices were reviewed and critically appraised to establish an understanding of limitations and challenges within these domains. Principles of systems engineering were used to develop solutions to address the identified problems. The framework was then harmonized with the Machine Learning software development process to create a systems engineering-based Machine Learning software development approach in the healthcare domain. Results We present an integration framework for healthcare Artificial Intelligence that considers the entirety of this system of systems. Our proposed framework utilizes a combined software and integration engineering approach and consists of four phases: (1) Inception, (2) Preparation, (3) Development, and (4) Integration. During each phase, we present specific elements for consideration in each of the three domains of integration: The Human, The Technical System, and The Environment. There are also elements that are considered in the interactions between these domains. Conclusion Clinical models are technical systems that need to be integrated into the existing system of systems in health care. A systems engineering ... |
| نوع الوثيقة: |
dataset |
| اللغة: |
unknown |
| Relation: |
https://figshare.com/articles/dataset/Table_1_An_integration_engineering_framework_for_machine_learning_in_healthcare_pdf/20429103 |
| DOI: |
10.3389/fdgth.2022.932411.s001 |
| الاتاحة: |
https://doi.org/10.3389/fdgth.2022.932411.s001
https://figshare.com/articles/dataset/Table_1_An_integration_engineering_framework_for_machine_learning_in_healthcare_pdf/20429103 |
| Rights: |
CC BY 4.0 |
| رقم الانضمام: |
edsbas.7CA77F06 |
| قاعدة البيانات: |
BASE |