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Method Article
Emily Chia-Yu Su
(1) Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wu-Xing Street, Taipei 11031, Taiwan. (2) Clinical Big Data Research Center, Taipei Medical University Hospital, 250 Wu-Xing Street, Taipei 11031, Taiwan. (3) Research Center for Artificial Intelligence in Medicine, Taipei Medical University, 250 Wu-Xing Street, Taipei 11031, Taiwan.
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4801-5159
License:
This work is licensed under a CC BY 4.0 License. Read Full License
We proposed a learning algorithm for human to conduct literature and data mining for causal factor discovery. The applicability is to select features for a machine learning prediction model, including but not limited to that using real-world, time-varying data from electronic health records. This protocol is relatively quick to find potentially actionable predictors for a clinical prediction while dealing with high dimensionality in big data. However, this protocol might not find a potentially novel cause, since this only exhaustively examines the existing evidences in a single study. The key stages consisted of systematic human learning, causal diagram construction, data preprocessing, causal inference modeling, and development and validation of a prediction model to describe the explainability.
Keywords
causal diagram, feature selection, machine learning, medical history, electronic health record
This is a list of supplementary files associated with this preprint. Click to download.
- suppprotocolshl.pdf
Supplementary information for source code description
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Associated Publications
Prognostication for prelabor rupture of membranes and the time of delivery in nationwide insured women: development, validation, and deployment
by Herdiantri Sufriyana, Yu-Wei Wu, Emily Chia-Yu Su
An open repository of community-contributed protocols sponsored by Nature Research.
Learn more about Protocol Exchange
Method Article
Emily Chia-Yu Su
(1) Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wu-Xing Street, Taipei 11031, Taiwan. (2) Clinical Big Data Research Center, Taipei Medical University Hospital, 250 Wu-Xing Street, Taipei 11031, Taiwan. (3) Research Center for Artificial Intelligence in Medicine, Taipei Medical University, 250 Wu-Xing Street, Taipei 11031, Taiwan.
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4801-5159
Version History
CURRENT STATUS: Posted
Version 1
Posted 13 Oct, 2021
No community comments so far
Metrics
Comments: 0
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
We proposed a learning algorithm for human to conduct literature and data mining for causal factor discovery. The applicability is to select features for a machine learning prediction model, including but not limited to that using real-world, time-varying data from electronic health records. This protocol is relatively quick to find potentially actionable predictors for a clinical prediction while dealing with high dimensionality in big data. However, this protocol might not find a potentially novel cause, since this only exhaustively examines the existing evidences in a single study. The key stages consisted of systematic human learning, causal diagram construction, data preprocessing, causal inference modeling, and development and validation of a prediction model to describe the explainability.
This is a list of supplementary files associated with this preprint. Click to download.
- suppprotocolshl.pdf
Supplementary information for source code description
Loading...
Associated Publications
Prognostication for prelabor rupture of membranes and the time of delivery in nationwide insured women: development, validation, and deployment
by Herdiantri Sufriyana, Yu-Wei Wu, Emily Chia-Yu Su