A new method of diagnosing constitutional types based on vocal and facial features for personalized medicine

The Relationship between the Second-Generation Antipsychotics Efficacy and the Traditional Chinese Medicine Body Constitutions in Patients with Schizophrenia

Background: Schizophrenia requires lifelong treatment; Second-generation Antipsychotics (SGAs) have become the most prescribed medication for schizophrenia patients. The efficacy of various SGAs treatment may differ in schizophrenia patients with various traditional Chinese medicine (TCM) body constitution (BC) types. Method: This study applied a longitudinal quantitative research design, where a total of 66 participants were recruited. The Positive and Negative Symptom Scale (PANSS) and the Clinical Global Impression (CGI) score were used to evaluate patients’ psychopathology status in hospitalization, and body constitution questionnaires were conducted by face-to-face interviews in the 1st, 3rd, and 6th week of hospitalization. Results: More than 60% of schizophrenia patients who were treated with SGAs were classified to have unbalanced BC types including Yin-Xu, Yang-Xu and Stasis. Generalized estimating equation analysis revealed significant time effects in CGI and PANSS score improvements in both unbalanced and gentleness (balance) BC types, but no significant changes in the group and group-time interaction in the CGI and PANSS scores in different BC type groups. Conclusions: Schizophrenia patients under SGAs treatment had a higher proportion of unbalanced BC types which may lead to poorer physical or mental statuses, such as overweight problems. Health care providers could apply interventions according to patients’ BC types for disease prevention.

Prediction of cold and heat patterns using anthropometric measures based on machine learning

OBJECTIVE

To examine the association of body shape with cold and heat patterns, to determine which anthropometric measure is the best indicator for discriminating between the two patterns, and to investigate whether using a combination of measures can improve the predictive power to diagnose these patterns.

METHODS

Based on a total of 4,859 subjects (3,000 women and 1,859 men), statistical analyses using binary logistic regression were performed to assess the significance of the difference and the predictive power of each anthropometric measure, and binary logistic regression and Naive Bayes with the variable selection technique were used to assess the improvement in the predictive power of the patterns using the combined measures.

RESULTS

In women, the strongest indicators for determining the cold and heat patterns among anthropometric measures were body mass index (BMI) and rib circumference; in men, the best indicator was BMI. In experiments using a combination of measures, the values of the area under the receiver operating characteristic curve in women were 0.776 by Naive Bayes and 0.772 by logistic regression, and the values in men were 0.788 by Naive Bayes and 0.779 by logistic regression.

CONCLUSIONS

Individuals with a higher BMI have a tendency toward a heat pattern in both women and men. The use of a combination of anthropometric measures can slightly improve the diagnostic accuracy. Our findings can provide fundamental information for the diagnosis of cold and heat patterns based on body shape for personalized medicine.

The Relationship between Body Weight Change and Body Constitutions of Traditional Chinese Medicine in Patients with Schizophrenia

Objective. To explore the relationship between body constitution (BC) types and weight change in patients with schizophrenia and who underwent second-generation antipsychotics (SGAs) treatment. Method. Body weight and waist circumference of eighty-five participants were measured for 6 consecutive weeks. Constitutions of Yin-Xu, Yang-Xu, and Stasis were assessed using the Body Constitution Questionnaire (BCQ). Results. Participants with body constitutions Yin-Xu (50.6%), Yang-Xu (49.4%), or Stasis (38.8%) exhibited worse physical condition and unhealthy daily habits, particularly in Stasis constitution. Moreover, Stasis constitution was significantly associated with several factors, including BMI, body weight, waist circumference, perception of stress, perception of health, staying up late, and less physical exercise. However, perception of stress showed significant difference in Yin-Xu, Yang-Xu, and Stasis. Generalized estimating equation (GEE) analysis revealed that significant time effects in body weight increase in the imbalanced BC types and gentleness BC type. SGAs induced weight gain in imbalanced BC type as well as gentleness BC type, especially treated with olanzapine. Conclusions. This is the first study to explore the longitudinal relationship between BC and weight gain in schizophrenia patients undergoing SGAs treatment. Health care providers should focus on weight gain problems in schizophrenia patients who underwent SGAs treatment.

Association of hypertension with physical factors of wrist pulse waves using a computational approach: a pilot study

BACKGROUND

The objectives of this pilot study were to examine the association between hypertension and physical factors of wrist pulse waves to avoid subjective diagnoses in Traditional Chinese Medicine (TCM) and Traditional Korean Medicine (TKM). An additional objective was to assess the predictive power of individual and combined physical factors in order to identify the degree of agreement between diagnosis accuracies using physical factors and using a sphygmomanometer in the prediction of hypertension.

METHODS

In total, 393 women aged 46 to 73 years participated in this study. Logistic regression (LR) and a naïve Bayes algorithm (NB) were used to assess statistically significant differences and the predictive power of hypertension, and a wrapper-based machine learning method was used to evaluate the predictive power of combinations of physical factors.

RESULTS

In both wrists, L-PPI and R-PPI (maximum pulse amplitudes in the left Gwan and right Gwan) were the factors most strongly associated with hypertension after adjusting for age and body mass index (p = <0.001, odds ratio (OR) = 2.006 on the left and p = <0.001, OR = 2.504 on the right), and the best predictors (NB-AUC = 0.692, LR-AUC = 0.7 on the left and NB-AUC = 0.759, LR-AUC = 0.763 on the right). Analyses of both individual and combined physical factors revealed that the predictive power of the physical factors in the right wrist was higher than for the left wrist. The predictive powers of the combined physical factors were higher than those of the best single predictors in both the left and right wrists.

CONCLUSION

We suggested new physical factors related to the sum of the area on the particular region of pulse waves in both wrists. L-PPI and R-PPI among all variables used in this study were good indicators of hypertension. Our findings support the quantification and objectification of pulse patterns and disease in TCM and TKM for complementary and alternative medicine.

Recent progress and prospects in Sasang constitutional medicine: a traditional type of physiome-based treatment

The history of the constitution perspective in medical care dates back thousands of years and extends from the East to the West. Among the various forms of constitutional medicine, Sasang constitutional medicine (SCM) is a holistic, tailored medical approach that is based on a well-structured theoretical system that includes physiopathological disciplines. Scientific evidence has demonstrated that SCM typology has a constitution-specific basis in anthropometrics, physiological characteristics, disease vulnerability, and genetic origins. Furthermore, the recent rise of systems biology, which requires whole body modeling, uses a state-of-the-art approach in interpreting the holistic spirit of Oriental medicine. This article aims to provide an overview of the recent achievements in SCM research and to discuss how the concept of balance in SCM may contribute to the development of large scale modeling in systems biology.

Constitution-specific features of perspiration and skin visco-elasticity in SCM

BACKGROUND

Human skin properties have been used as an important diagnostic component in traditional medicine as they change with health conditions. Sasang constitutional medicine (SCM) puts emphasis on the recognition of the constitution-specific skin features prior to the diagnostic decision of health. In this work, in search of skin-characteristics effectively reflecting SCM features, we compared several skin properties such as perspiration, visco-elasticity, elasticity, and elasticity hysteresis, in several candidate body parts.

METHODS

We conducted a clinical study in which a total of 111 healthy females aged 50 - 70 years participated with their Sasang constitution (SC) types determined objectively by the Sasang constitutional analytic tool. Perspiration on the skin surface was estimated by using a capacitance sensor to measure the amount of moisture on the palm, forehead, and philtrum before and after a heating stimulus. We acquired the visco-elasticity, elasticity, and elasticity hysteresis at the forearm by Dermalab's elasticity sensing device. An analysis of covariance (ANCOVA) was conducted to evaluate the effect of SC on the nine skin features acquired.

RESULTS

The visco-elasticity of the forearm of the Soeum-in (SE) group was significantly lower than that of the Taeeum-in (TE) group (F = 68.867, p < 0.001), whereas the elasticity hysteresis of the SE group was higher than that of the TE group (F = 10.364, p < 0.01). The TE group had more perspiration on the forehead than the SE group (F = 9.050, p < 0.01). The SE group had a large perspiration difference between the philtrum and the forehead compared with the TE group (F = 7.892, p < 0.01).

CONCLUSIONS

We found four significant skin features that reflect the inherent constitutional attributes of the TE and SE groups in accordance with SCM literature; the visco-elasticity, elasticity hysteresis, perspiration on the forehead and philtrum. Our findings are based on a novel interpretation of the SCM literature and will contribute to developing the constitutional health status evaluation system in SCM.

Classifying human voices by using hybrid SFX time-series preprocessing and ensemble feature selection

Voice biometrics is one kind of physiological characteristics whose voice is different for each individual person. Due to this uniqueness, voice classification has found useful applications in classifying speakers' gender, mother tongue or ethnicity (accent), emotion states, identity verification, verbal command control, and so forth. In this paper, we adopt a new preprocessing method named Statistical Feature Extraction (SFX) for extracting important features in training a classification model, based on piecewise transformation treating an audio waveform as a time-series. Using SFX we can faithfully remodel statistical characteristics of the time-series; together with spectral analysis, a substantial amount of features are extracted in combination. An ensemble is utilized in selecting only the influential features to be used in classification model induction. We focus on the comparison of effects of various popular data mining algorithms on multiple datasets. Our experiment consists of classification tests over four typical categories of human voice data, namely, Female and Male, Emotional Speech, Speaker Identification, and Language Recognition. The experiments yield encouraging results supporting the fact that heuristically choosing significant features from both time and frequency domains indeed produces better performance in voice classification than traditional signal processing techniques alone, like wavelets and LPC-to-CC.

A novel method for classifying body mass index on the basis of speech signals for future clinical applications: a pilot study

Obesity is a serious public health problem because of the risk factors for diseases and psychological problems. The focus of this study is to diagnose the patient BMI (body mass index) status without weight and height measurements for the use in future clinical applications. In this paper, we first propose a method for classifying the normal and the overweight using only speech signals. Also, we perform a statistical analysis of the features from speech signals. Based on 1830 subjects, the accuracy and AUC (area under the ROC curve) of age- and gender-specific classifications ranged from 60.4 to 73.8% and from 0.628 to 0.738, respectively. We identified several features that were significantly different between normal and overweight subjects (P < 0.05). Also, we found compact and discriminatory feature subsets for building models for diagnosing normal or overweight individuals through wrapper-based feature subset selection. Our results showed that predicting BMI status is possible using a combination of speech features, even though significant features are rare and weak in age- and gender-specific groups and that the classification accuracy with feature selection was higher than that without feature selection. Our method has the potential to be used in future clinical applications such as automatic BMI diagnosis in telemedicine or remote healthcare.