With the booming development of medical information technology and computer science, the medical services industry is gradually transiting from information technology to intelligence. The medical knowledge graph plays an important role in intelligent medical applications such as knowledge questions and answers and intelligent diagnosis, and is a key technology for promoting wise medical care and the basis for intelligent management of medical information. In order to fully exploit the great potential of knowledge graphs in the medical field, this paper focuses on five aspects: inter-drug relationship discovery, assisted diagnosis, personalized recommendation, decision support and intelligent prediction. The latest research progress on medical knowledge graphs is introduced, and relevant suggestions are made in light of the current challenges and problems faced by medical knowledge graphs to provide reference for promoting the wide application of medical knowledge graphs.
Pattern Recognition, Automated ; Medical Informatics

To increase efficiency of automated leucocyte pattern recognition using lower feature dimensions, a novel inter-class distinctive feature selection method for chromatic leucocyte images was proposed based on attribute hierarchical relationship. According to the attribute constraints in formal concept analysis, we established a knowledge representation and discovery method based on the hierarchical optimal diagram by defining attribute value and visual representation of optimized hierarchical relationship. It was applied to human peripheral blood leucocytes classification and 12 distinctive attributes were simplified from 60 inter-class attributes, which contributes significantly to reduced feature dimensions and efficient inter-class feature classification. Compared with the classical experimental data, the inter-class distinctive feature selection method based on hierarchical optimal diagram was proved to be usable and effective for six leucocyte pattern recognition.
Humans ; Leukocytes ; classification ; Pattern Recognition, Automated

A new fogging disinfection method was evaluated as a means of disinfecting ward rooms and operating theaters. A temporary room was established where the disinfection effect of fogging was examined. Based on the results, an automatic fogging disinfection unit was developed. This unit was then used in the disinfection of operating theaters, where its safety and effectiveness were examined. To evaluate the results of disinfection, bacterial culture tests were performed on the floor, walls and other areas of the operating theater, and the number of colony forming units was used as an index of effectiveness. Benzalkonium chloride, alkyldiaminoethylglycine, sodium hypochlorite, glutaral and acidic electrolytic water were used for the operating theaters. The average disinfection effect was 90% or better for all disinfectants, except acidic electrolytic water. The newly developed automatic fogging disinfection unit enables safe and effective disinfection, and may be suitable for disinfecting ward rooms and operating theaters.
Disinfection ; Pulmonary evaluation ; Unit ; Automated ; Water

OBJECTIVETo construct a high performance open source software engine based on IBM SPLASH algorithm for later research on pattern discovery.

METHODSGpat, which is based on SPLASH algorithm, was developed by using open source software.

RESULTSGNU Pattern (Gpat) software was developped, which efficiently implemented the core part of SPLASH algorithm. Full source code of Gpat was also available for other researchers to modify the program under the GNU license.

CONCLUSIONGpat is a successful implementation of SPLASH algorithm and can be used as a basic framework for later research on pattern recognition in biological sequences.

The image segmentation method we use for leucocytes is based on image distance transformation, combining the region and edge approach, taking full advantage of image information. According to the shape, texture and color appearance of cells, we select 22 feature values and measure them. The classifier is designed on the statistical classification. A test for recognizing 831 leucocytes in 560 images shows that the classification accuracy is 96%. Clinical experts confirm this system; for it can automatically recognize leucocytes by pattern recognition technique, and it is demonstrably valid in practice.
Algorithms ; Artificial Intelligence ; Humans ; Leukocytes ; cytology ; Pattern Recognition, Automated ; methods

To explore the methods of the explicitation of implicit knowledge and the construction of knowledge graph on moxibustion in medical case records of ZHOU Mei-sheng's Jiusheng. The medical case records data of Jiusheng was collected, the frequency statistic was analyzed based on Python3.8.6, complex network analysis was performed using Gephi9.2 software, community analysis was performed by the ancient and modern medical case cloud platform V2.3.5, and analysis and verification of correlation graph and weight graph were proceed by Neo4j3.5.25 image database. The disease systems with frequency≥10 % were surgery, ophthalmology and otorhinolaryngology, locomotor, digestive and respiratory systems. The diseases under the disease system were mainly carbuncle, arthritis, lumbar disc herniation and headache. The commonly used moxibustion methods were fumigating moxibustion, blowing moxibustion, direct moxibustion and warming acupuncture. The core prescription of points obtained by complex network analysis included Yatong point, Zhiyang(GV 9), Sanyinjiao(SP 6), Dazhui(GV 14), Zusanli(ST 36), Lingtai(GV 10), Xinshu(BL 15), Zhijian point and Hegu(LI 4), which were basically consistent with high-frequency points. A total of 6 communities were obtained by community analysis, corresponding to different diseases. Through the analysis of correlation graph, 13 pairs of strong association rule points were obtained. The correlation between Zhiyang(GV 9)-Dazhui(GV 14) and Yatong point-Lingtai(GV 10) was the strongest. The acupoints with high correlation with Yatong point were Zhiyang(GV 9), Lingtai(GV 10), Dazhui(GV 14), Zusanli(ST 36) and Sanyinjiao(SP 6). In the weight graph of the high-frequency disease system, the relationship of the first weight of the surgery system disease was fumigating moxibustion-carbuncle-Yatong point, and the relationship of the first weight of the ophthalmology and otorhinolaryngology system disease was blowing moxibustion-laryngitis-Hegu (LI 4). The results of correlation graph and weight graph are consistent with the results of data mining, which can be used as an effective way to study the knowledge base of moxibustion diagnosis and treatment in the future.
Humans ; Moxibustion ; Carbuncle ; Pattern Recognition, Automated ; Acupuncture Therapy ; Acupuncture Points

OBJECTIVES: Acquiring temporal information is important because knowledge in clinical narratives is time-sensitive. In this paper, we describe an approach that can be used to extract the temporal information found in Korean clinical narrative texts. METHODS: We developed a two-stage system, which employs an exhaustive text analysis phase and a temporal expression recognition phase. Since our target document may include tokens that are made up of both Korean and English text joined together, the minimal semantic units are analyzed and then separated from the concatenated phrases and linguistic derivations within a token using a corpus-based approach to decompose complex tokens. A finite state machine is then used on the minimal semantic units in order to find phrases that possess time-related information. RESULTS: In the experiment, the temporal expressions within Korean clinical narratives were extracted using our system. The system performance was evaluated through the use of 100 discharge summaries from Seoul National University Hospital containing a total of 805 temporal expressions. Our system scored a phrase-level precision and recall of 0.895 and 0.919, respectively. CONCLUSIONS: Finding information in Korean clinical narrative is challenging task, since the text is written in both Korean and English and frequently omits syntactic elements and word spacing, which makes it extremely noisy. This study presents an effective method that can be used to aquire the temporal information found in Korean clinical documents.
Automatic Data Processing ; Linguistics ; Medical Informatics ; Medical Records ; Multilingualism ; Pattern Recognition, Automated ; Semantics

In order to recognize people by their gait, we propose a pattern recognizing method based on kinematics and kinetics parameters. The feature extraction methods of joint angle and vertical ground reaction force (VGRF) were given. 14 healthy male subjects participated in this experiment. The experimental results showed that the correct classification rates (CCR) was 87.1% at k = 1 and 90% at k = 3 (k-nearest neighbor) based on joint angle recognition; the CCR was 85.7% at k = 1 and 80% at k = 3 based on VGRF recognition. The multivariate analysis of the experimental data proved the feasibility of gait recognition. The principal component analysis and curves of VGRF also showed that the instant of foot strike plays an important role in gait recognition.
Adult ; Biomechanical Phenomena ; Gait ; physiology ; Humans ; Kinetics ; Male ; Pattern Recognition, Automated

Recognition by gait is a new field for the biometric recognition technology. Its aim is to recognize people and detect physiological, pathological and mental characters by their walk style. The use of gait as a biometric for human identification is promising. The technique of gait recognition, as an attractive research area of biomedical information detection, attracts more and more attention. In this paper is introduced a survey of the basic theory, existing gait recognition methods and potential prospects. The latest progress and key factors of research difficulties are analyzed, and future researches are envisaged.
Algorithms ; Artificial Intelligence ; Biometry ; methods ; Gait ; physiology ; Humans ; Models, Biological ; Pattern Recognition, Automated ; methods

Feature selection (FS) techniques have become an important tool in bioinformatics field. The core algorithm of it is to select the hidden significant data with low-dimension from high-dimensional data space, and thus to analyse the basic built-in rule of the data. The data of bioinformatics fields are always with high-dimension and small samples, so the research of FS algorithm in the bioinformatics fields has great foreground. In this article, we make the interested reader aware of the possibilities of feature selection, provide basic properties of feature selection techniques, and discuss their uses in the sequence analysis, microarray analysis, mass spectra analysis etc. Finally, the current problems and the prospects of feature selection algorithm in the application of bioinformatics is also discussed.
Algorithms ; Artificial Intelligence ; Computational Biology ; methods ; Computer Simulation ; Models, Biological ; Pattern Recognition, Automated ; methods