Authenticity discrimination of Pulsatillae Radix based on dry-process REIMS fingerprinting combined with machine learning / 中国中药杂志

In this study, rapid evaporative ionization mass spectrometry(REIMS) fingerprints of 388 samples of roots of Pulsatilla chinensis(PC) and its common counterfeits, roots of P. cernua and roots of Anemone tomentosa were analyzed based on REIMS combined with machine learning. The samples were determined by REIMS through dry burning, and the REIMS data underwent cluster analysis, similarity analysis(SA), and principal component analysis(PCA). After dimensionality reduction by PCA, the data were analyzed by similarity analysis and self-organizating map(SOM), followed by modeling. The results indicated that the REIMS fingerprints of the samples showed the characteristics of variety differences and the SOM model could accurately distinguish PC, P. cernua, and A. tomentosa. REIMS combined with machine learning algorithm has a broad application prospect in the field of traditional Chinese medicine.

Data dimensionality reduction for hospital efficiency measurement / 中华医院管理杂志

A comparison was made for the correlation and application scope of the statistical methods commonly used by hospitals for their efficiency measurement.Hospital data processed with PCA (principal component analysis)for dimension reduction were used in a correlation analysis for the results of ratio analysis (RA),stochastic frontier analysis(SFA)and data envelopment analysis(DEA).The authors hold that the RA can expediently display the order of hospital efficiency,the SFA demands a stricter premise yet presents more stable results,while the DEA boasts greater relative advantages and thus suitable for processing hospital efficiency measurement tasks of multi-input and multi-output indexes.