A Machine Learning Perspective on Personalized Medicine: An Automized, Comprehensive Knowledge Base with Ontology for Pattern Recognition
by
Frank Emmert-Streib 1,2,*
and Matthias Dehmer 3,4,5
Frank Emmert-Streib 1,2,* and Matthias Dehmer 3,4,5
1
Predictive Medicine and Data Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere 33720, Finland
2
Institute of Biosciences and Medical Technology, Tampere 33520, Finland
3
Department of Mechatronics and Biomedical Computer Science, UMIT, Hall in Tyrol 6060, Austria
4
College of Computer and Control Engineering, Nankai University, Tianjin 300071, China
5
Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Steyr Campus 4400, Austria
*
Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2019, 1(1), 149-156; https://doi.org/10.3390/make1010009
Received: 28 July 2018 / Revised: 24 August 2018 / Accepted: 4 September 2018 / Published: 8 September 2018
Personalized or precision medicine is a new paradigm that holds great promise for individualized patient diagnosis, treatment, and care. However, personalized medicine has only been described on an informal level rather than through rigorous practical guidelines and statistical protocols that would allow its robust practical realization for implementation in day-to-day clinical practice. In this paper, we discuss three key factors, which we consider dimensions that effect the experimental design for personalized medicine: (I) phenotype categories; (II) population size; and (III) statistical analysis. This formalization allows us to define personalized medicine from a machine learning perspective, as an automized, comprehensive knowledge base with an ontology that performs pattern recognition of patient profiles.
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Keywords:
machine learning; pattern recognition; personalized medicine; precision medicine; genomics
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MDPI and ACS Style
Emmert-Streib, F.; Dehmer, M. A Machine Learning Perspective on Personalized Medicine: An Automized, Comprehensive Knowledge Base with Ontology for Pattern Recognition. Mach. Learn. Knowl. Extr. 2019, 1, 149-156.
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