Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison
There is growing enthusiasm for the application of machine learning (ML) and artificial intelligence (AI) techniques to clinical research and practice. However, instructions on how to develop robust high-quality ML and AI in medicine are scarce. In this paper, we provide a practical example of techn...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
01 November 2022
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| In: |
BMC medical research methodology
Year: 2022, Volume: 22, Pages: 1-15 |
| ISSN: | 1471-2288 |
| DOI: | 10.1186/s12874-022-01758-8 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1186/s12874-022-01758-8
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| Author Notes: | André Pfob, Sheng-Chieh Lu and Chris Sidey-Gibbons |
| Summary: | There is growing enthusiasm for the application of machine learning (ML) and artificial intelligence (AI) techniques to clinical research and practice. However, instructions on how to develop robust high-quality ML and AI in medicine are scarce. In this paper, we provide a practical example of techniques that facilitate the development of high-quality ML systems including data pre-processing, hyperparameter tuning, and model comparison using open-source software and data. |
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| Item Description: | Gesehen am 15.02.2023 |
| Physical Description: | Online Resource |
| ISSN: | 1471-2288 |
| DOI: | 10.1186/s12874-022-01758-8 |