Supervised Methods to Support Online Scientific Data Triage
| Autor: | Marc Queudot, Leila Kosseim, Hayda Almeida, Marie-Jean Meurs |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Computer science business.industry Feature selection computer.software_genre Machine learning Pipeline (software) Triage Task (project management) Support vector machine Pipeline transport 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Data sampling Biomedical data 030212 general & internal medicine Data mining Artificial intelligence business computer |
| Zdroj: | Lecture Notes in Business Information Processing ISBN: 9783319590400 MCETECH |
| DOI: | 10.1007/978-3-319-59041-7_13 |
| Popis: | This paper presents machine learning approaches based on supervised methods applied to triage of health and biomedical data. We discuss the applications of such approaches in three different tasks, and evaluate the usage of triage pipelines, as well as data sampling and feature selection methods to improve performance on each task. The scientific data triage systems are based on a generic and light pipeline, and yet flexible enough to perform triage on distinct data. The presented approaches were developed to be integrated as a part of web-based systems, providing real time feedback to health and biomedical professionals. All systems are publicly available as open-source. |
| Databáze: | OpenAIRE |
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