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Irritable bowel syndrome (IBS) is one of the most common functional bowel disorders (FBD), which is characterized by recurrent abdominal pain, abdominal bloating/distention associated with defecation or changed bowel habits. Currently, there is no evidence of obvious anatomic or physiologic abnormalities on the routine diagnostic examinations. There are multiple pathological factors involved in IBS responsible for its heterogeneous nature, although the exact etiology and pathology of IBS are not well known and it is disappointed to develop biomarkers for this disorder. These factors including low-grade inflammation, activation of immune system, changed intestinal microorganism, small intestinal bacterial overgrowth (SIBO), malabsorption of bile acid (BA), increased number of mast cells (MCs). Current pharmacologic treatment for IBS focuses on alleviation of its symptoms, but not on the elimination of its cause. Although there are a lot of conventional chemical medicines for IBS available, due to the limited clinical benefits, high medical expenses and severe side effects, many IBS patients have turned to alternative medicine, particularly Chinese herbal medicine (CHM). Chinese herbal therapies have been used for thousand years in eastern Asia and have been provided that they are effective in relieving symptoms among IBS patients. Generally, traditional Chinese herbal formulae (CHF) consisting of CHM can be easily adjusted in accordance with concrete conditions, which means the treatment is based on syndrome differentiation and varied from individual to individual. Meanwhile, CHF/CHM containing many different ingredients may act on multiple sites/pathways with potential synergistic effects and chemical reactions.

Objective. To explore the data characteristics of tongue and pulse of non-small-cell lung cancer with Qi deficiency syndrome and Yin deficiency syndrome, establish syndrome classification model based on data of tongue and pulse by using machine learning methods, and evaluate the feasibility of syndrome classification based on data of tongue and pulse. Methods. We collected tongue and pulse of non-small-cell lung cancer patients with Qi deficiency syndrome ( n = 163 ), patients with Yin deficiency syndrome ( n = 174 ), and healthy controls ( n = 185 ) using intelligent tongue diagnosis analysis instrument and pulse diagnosis analysis instrument, respectively. We described the characteristics and examined the correlation of data of tongue and pulse. Four machine learning methods, namely, random forest, logistic regression, support vector machine, and neural network, were used to establish the classification models based on symptom, tongue and pulse, and symptom and tongue and pulse, respectively. Results. Significant difference indices of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll, and the tongue coating texture indices including TC-CON, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indices of pulse diagnosis were t4 and t5. The classification performance of each model based on different datasets was as follows: tongue and pulse < symptom < symptom and tongue and pulse. The neural network model had a better classification performance for symptom and tongue and pulse datasets, with an area under the ROC curves and accuracy rate which were 0.9401 and 0.8806. Conclusions. It was feasible to use tongue data and pulse data as one of the objective diagnostic basis in Qi deficiency syndrome and Yin deficiency syndrome of non-small-cell lung cancer.

BackgroundFatigue is a common and subjective symptom, which is associated with many diseases and suboptimal health status. A reliable and evidence-based approach is lacking to distinguish disease fatigue and non-disease fatigue. This study aimed to establish a method for early differential diagnosis of fatigue, which can be used to distinguish disease fatigue from non-disease fatigue, and to investigate the feasibility of characterizing fatigue states in a view of tongue and pulse data analysis.MethodsTongue and Face Diagnosis Analysis-1 (TFDA-1) instrument and Pulse Diagnosis Analysis-1 (PDA-1) instrument were used to collect tongue and pulse data. Four machine learning models were used to perform classification experiments of disease fatigue vs. non-disease fatigue.ResultsThe results showed that all the four classifiers over “Tongue &amp; Pulse” joint data showed better performances than those only over tongue data or only over pulse data. The model accuracy rates based on logistic regression, support vector machine, random forest, and neural network were (85.51 ± 1.87)%, (83.78 ± 4.39)%, (83.27 ± 3.48)% and (85.82 ± 3.01)%, and with Area Under Curve estimates of 0.9160 ± 0.0136, 0.9106 ± 0.0365, 0.8959 ± 0.0254 and 0.9239 ± 0.0174, respectively.ConclusionThis study proposed and validated an innovative, non-invasive differential diagnosis approach. Results suggest that it is feasible to characterize disease fatigue and non-disease fatigue by using objective tongue data and pulse data.

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