ArticleJournal of biomedicine & biotechnology2012
A new method of diagnosing constitutional types based on vocal and facial features for personalized medicine.
Article in Journal of biomedicine & biotechnology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The Relationship between the Second-Generation Antipsychotics Efficacy and the Traditional Chinese Medicine Body Constitutions in Patients with Schizophrenia.Healthcare (Basel, Switzerland) · 2021Article
- Prediction of cold and heat patterns using anthropometric measures based on machine learning.Chinese journal of integrative medicine · 2018Article
- The Relationship between Body Weight Change and Body Constitutions of Traditional Chinese Medicine in Patients with Schizophrenia.Evidence-based complementary and alternative medicine : eCAM · 2016Article
- Association of hypertension with physical factors of wrist pulse waves using a computational approach: a pilot study.BMC complementary and alternative medicine · 2015Article
- Constitution-specific features of perspiration and skin visco-elasticity in SCM.BMC complementary and alternative medicine · 2014Article
- A novel method for classifying body mass index on the basis of speech signals for future clinical applications: a pilot study.Evidence-based complementary and alternative medicine : eCAM · 2013Article
- Classifying human voices by using hybrid SFX time-series preprocessing and ensemble feature selection.BioMed research international · 2013Article
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Authors and funding
5 authors.
Funding
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Abstract
The aim of the present study is to develop an accurate constitution diagnostic method based solely on the individual's physical characteristics, irrespective of psychologic traits, characteristics of clinical medicine, and genetic factors. In this paper, we suggest a novel method for diagnosing constitutional types using only speech and face characteristics. Based on 514 subjects, the area under the receiver operating characteristics curve (AUC) values of classification models in age and gender groups ranged from 0.64 to 0.89. We identified significant features showing statistical differences among three constitutional types by performing statistical analysis. Also, we selected a compact and discriminative feature subset for constitution diagnosis in each age and gender group. Our method may support the direction of improved diagnosis prediction and will serve to develop a personal and automatic constitution diagnosis software for improvement of the effectiveness of prescribed medications and development of personalized medicine.
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