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EHR Data Analytics and Predictions: Machine Learning Methods
| Content Provider | Scilit |
|---|---|
| Author | Gu, Yuxuan Huang, Yuefan Ly, Vi K. Yaseen, Ashraf Miao, Hongyu |
| Copyright Year | 2020 |
| Description | The state-of-the-art machine learning algorithms and selected software packages are introduced in this chapter. Particularly, random forest and its variants, gradient boosting, XgBoost, and SVM methods are reviewed in detail. Example computer codes based on H2O, Caret, TPOT, or Auto-sklearn are provided to illustrate these machine learning methods with applications to EHR data analysis and prediction. We also evaluate the performance of different machine learning methods and software packages using real EHR data. We conclude this chapter with some recommendations on how to choose appropriate machine learning methods for EHR data analysis. Book Name: Statistics and Machine Learning Methods for EHR Data |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2019-0-08549-8&isbn=9781003030003&doi=10.1201/9781003030003-10&format=pdf |
| Ending Page | 293 |
| Page Count | 21 |
| Starting Page | 273 |
| DOI | 10.1201/9781003030003-10 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-12-09 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Statistics and Machine Learning Methods for EHR Data Nanotechnology Machine Learning Methods |
| Content Type | Text |
| Resource Type | Chapter |
