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This study performs an Affinity Analysis ondiagnosis and prescription data in order to discover cooccurrencerelationships among diagnosis and pharmaceuticalactive ingredients prescribed to different patient groups. Theanalysis data collected during consecutive visits of 4,473 patients in a 3 years period, focused on patients suffering byhypertension and/or hypercholesterolemia and appliedassociation...
With the advent of the big data era, data mining technology has gradually become mature, association rules analysis is also applied in many fields. Web log mining is an important way to do some personalized services and achieve Web personalize. Apriori algorithm is a classical algorithm of association rules, but it has a lot of shortcomings. In recent years, the improvement about Apriori algorithm...
Tibetan Medicine plays an important role in traditional Chinese Medical Science. For the inheritance of Tibetan medical science and disease prevention, the main method is to summarize and study the medication and diagnostic rules by using data mining technology, which is still in the early stage. In this paper, firstly, a standard knowledge base for plateau stomach illness has been constructed by...
As the mechanism of the effective traditional Chinese medical science treatment remains confounding, researchers seek to analyze the therapies using traditional Chinese medicine (TCM) with advanced techniques. In this paper we take advantage of the technique of data mining with the software SPSS (Statistical Product and Service Solutions), in particular the Apriori algorithm for association rules...
Objective: Analysis and Data Mining the treatment laws of Coronary Heart Disease by famous old TCM doctor Wang Xing-kuan. Explore the methods of famous old TCM doctor's experience. Method: To collect 143 effective cases and 267 diagnoses, and establish Coronary Heart Disease clinic database, the study investigated the inherent laws of Treating Coronary Heart Disease by Prof. Wang. Result : In the...
Behind the large number of data contains abundant special mining resources in Hospital information system (HIS). Association Rule mining technology can be used to mine hidden information, provide the actual and effective evidence for the hospital administrator, This article described the association rules mining theory and the algorithm in detail, and Research and demonstration on association rules...
This paper provides a survey of data mining methods that have been commonly applied to real-world TCM clinical data in recent years, and sets forth the requirements of data mining on real-world TCM clinical diagnosis and treatment data, in order to provide reference for better analyzing the syndrome differentiation and treatment principle hidden in the massive TCM clinical data in the future.
Currently data mining techniques and health/medical informatics are still new. Data mining researchers start paying more attention on these matters. Association Rule is one of important methods in data mining. By discovering data association, new useful information can be obtained. In this paper, a researcher has presented a basic method of discovering an association of diabetes mellitus with complication...
The burden of lifestyle diseases such as diabetes have reached epidemic proportions since the last decade in India. An estimated 75 million people in India would become diabetic by 2025. However, the existing healthcare infrastructure is inadequate to meet the demands of this exploding population. Provisioning a web-based patient support system that helps in patient centered decision making and physician...
As the association rules mining algorithm, Apriori algorithm is gotten a lot of application used for its easy use. However, it often encountered some problem as low mining efficiency, too many invalid rules acquired and the rules of pattern mining disorder. In this paper, an algorithm called R_Apriori which improved from Apriori algorithm is designed for above problems. It is necessary to build syndrome...
The objective of this work is to develop and implement a computer-aided decision support system for an automated diagnosis and classification of ultrasound kidney images. This approach combines automatically extracted low-level features from images with high-level knowledge given by a specialist in order to suggest a diagnosis of a new kidney image. The proposed method distinguishes three kidney categories...
Currently, as a effort to reduce a rate of death by cardiovascular diseases, a lot of researches have been studied regarding real-time diagnosis system. So, we implement a prototype which is contained of stream data processor and incremental data mining module for automatic diagnosis of cardiovascular diseases. In the prototype, (i)ECG signal data which is transported from body-attached sensor is...
This research uses association rule generation and classification techniques to support decision making, by considering a data set of diabetes type 1 & type 2 patients. There are advanced and reliable data mining techniques which leads to the discovery of unseen and useful information. The main focus of this research is to identify the yet undiscovered decision factors of diabetes which increases...
This paper presents an efficient real-time knowledge base architecture for multi-agent based patient diagnostic system for chronic disease management, basically, the early detection of Inflammation of urinary bladder and Nephritis of renal pelvis origin diseases. The model integrates information stored heterogeneous and geographically distributed healthcare centers. The paper presents two main contributions...
The development of database technology has solved the memory and retrieval of substantive data, but the biomedicine database existing the phenomenon of “data rich, information poor”. In order to solve the problem of Knowledge Discovery in Database, great importance has been continuously attached to the data mining. In this paper, we elaborate the Particularities and Key issues of data mining in biomedicine,...
In medical qualitative research, medical researchers analyze historical patient data to verify known relationships and to discover unknown relationships among medical attributes. All the existing algorithms to solve this problem use measures which are asymmetric measure, so only one direction of the rule (P -> Q or Q->P) is taken into account. However, medical researchers are interested to find...
Conventional positive association rules are the patterns that occur frequently together. These patterns represent what decisions are routinely made based on a set of facts. Irregular association rules are the patterns that represent what decisions are rarely made based on the same set of facts. Many domains like Healthcare, Banking etc need the irregular rule to improve their system. In this paper,...
In this paper, we propose a method to constrain and summarize optimal risk and preventive patterns in medical data using risk and preventive set with weight (RPSW) algorithm. The proposed method was tested by two benchmark medical data sets. The experiments show that the number of attribute-value items of the constraints was significant reduced and the results are understandable and intuitive.
First, the meaning of data mining from EMR with a view to discover valuable knowledge, as well as current problems is introduced. Then, several possible storage methods for EMR are deeply analyzed and studied. On this basis, the solution of storage XML EMR with DB2/9.5 hybrid database is presented. The XQ-Apriori algorithm based on classic Apriori algorithm and XML query language XQuery, as well as...
The discovery of knowledge from medical databases is important in order to make effective medical diagnosis. The aim of data mining is extract the information from database and generate clear and understandable description of patterns. In this study we have introduced a new approach to generate association rules on numeric data. We propose a modified equal width binning interval approach to discretizing...
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