A Protocol for Digitalized Collection of Traditional Chinese Medicine(TCM)Pulse Information Using Bionic Pulse Diagnosis Equipment

Pulse diagnosis equipment used in Traditional Chinese Medicine(TCM)has long been developed for collecting pulse information and in TCM research.However,it is still difficult to implement pulse taking automatically or efficiently in clinical practice.Here,we present a digital protocol for TCM pulse information collection based on bionic pulse diagnosis equipment,which ensures high efficiency,reliability and data integrity of pulse diagnosis information.A four-degree-of-freedom pulse taking platform together with a wrist bracket can satisfy the spatial positioning and angle requirements for individually adaptive pulse acquisition.Three-dimensional reconstruction of a wrist surface and an image localization model are combined to provide coordinates of the acquisition position and detection direction automatically.Three series elastic joints can not only simulate the TCM pulse taking method that“Three fingers in a straight line,the middle finger determining the‘Guan’location and finger pulp pressing on the radial artery,”but also simultaneously carry out the force-controlled multi-gradient pressing process.In terms of pulse information integrity,this proposed protocol can generate rich pulse information,including basic individual information,pulse localization distribution,multi-gradient dynamic pulse force time series,and objective pulse parameters,which can help establish the fundamental data sets that are required as the pulse phenotype for subsequent comprehensive analysis of pulse diagnosis.The implementation of this scheme is beneficial to promote the standardization of the digitalized collection of pulse information,the effectiveness of detecting abnormal health status,and the promotion of the fundamental and clinical research of TCM,such as TCM pulse phenomics.

Annotation and Joint Extraction of Scientific Entities and Relationships in NSFC Project Texts

Aiming at the lack of classification and good standard corpus in the task of joint entity and relationship extraction in the current Chinese academic field, this paper builds a dataset in management science that can be used for joint entity and relationship extraction, and establishes a deep learning model to extract entity and relationship information from scientific texts. With the definition of entity and relation classification, we build a Chinese scientific text corpus dataset based on the abstract texts of projects funded by the National Natural Science Foundation of China(NSFC) in 2018–2019. By combining the word2vec features with the clue word feature which is a kind of special style in scientific documents, we establish a joint entity relationship extraction model based on the Bi LSTM-CNN-CRF model for scientific information extraction. The dataset we constructed contains 13060 entities(not duplicated) and 9728 entity relation labels. In terms of entity prediction effect, the accuracy rate of the constructed model reaches 69.15%, the recall rate reaches 61.03%, and the F1 value reaches 64.83%. In terms of relationship prediction effect, the accuracy rate is higher than that of entity prediction, which reflects the effectiveness of the input mixed features and the integration of local features with CNN layer in the model.

Resolution of overlapping ambiguity strings based on maximum entropy model

The resolution of overlapping ambiguity strings(OAS)is studied based on the maximum entropy model.There are two model outputs,where either the first two characters form a word or the last two characters form a word.The features of the model include one word in con-text of OAS,the current OAS and word probability relation of two kinds of segmentation results.OAS in training text is found by the combination of the FMM and BMM segmen-tation method.After feature tagging they are used to train the maximum entropy model.The People Daily corpus of January 1998 is used in training and testing.Experimental results show a closed test precision of 98.64%and an open test precision of 95.01%.The open test precision is 3.76%better compared with that of the precision of common word probability method.