We have been conducting research to develop a system for selecting DPC codes from patient discharge summaries using text mining techniques. The performance demonstrated thus far indicates that the classification system is sufficiently practical for real-world use. However, with the limited dataset used for training, the possibility of overfitting cannot be ruled out, necessitating performance evaluation with new data.In this study, we propose a scheme in which the classification system is constructed using discharge summaries from one fiscal year and evaluated using data from a different fiscal year. Following this scheme, we conducted experiments to test its validity.The results showed that the evaluation metrics obtained through cross-validation were less than those obtained using new data. This finding suggests that constructing the classification system while bias estimation is crucial for its successful implementation in hospital information systems.

We have been conducting research to develop a system for selecting DPC codes from patient discharge summaries using text mining techniques. The performance demonstrated thus far indicates that the classification system is sufficiently practical for real-world use. However, with the limited dataset used for training, the possibility of overfitting cannot be ruled out, necessitating performance evaluation with new data.In this study, we propose a scheme in which the classification system is constructed using discharge summaries from one fiscal year and evaluated using data from a different fiscal year. Following this scheme, we conducted experiments to test its validity.The results showed that the evaluation metrics obtained through cross-validation were less than those obtained using new data. This finding suggests that constructing the classification system while bias estimation is crucial for its successful implementation in hospital information systems.

This paper proposes visualization and simulation methods which estimates clinical indices from the data stored in the hospital information system (HIS). The method is executed as follows. First, we construct DWH where needed variables are extracted from HIS. Second, the indices (nursing needs: Score A, which consists of the histories of monitoring and procedures and Score B, which evaluates of patient’s physical condition) are calculated from extracted data. Third, chronological changes of the indices are visualized. Finally, by using the model of stochastic differential equations, the nursing care needs and its chronological changes during the admission period are estimated. We evaluated the method with the data in HIS (from 2017 to 2022). The results shows that the method correctly simulated the chronological change of nursing care needs.

We conducted an analysis of incident reports from Shimane University School of Medicine Hospital using text mining techniques to efficiently analyze the cases based on the keywords they contained. The analysis targeted the text data of incident reports filed between fiscal years 2017 and 2022, with a particular focus on reports related to personal information. We employed morphological analysis on the collected text data, organizing it word by word to facilitate easier analysis. Subsequently, techniques such as word clouds and cluster analysis were utilized. Based on the results obtained, we conducted a thorough analysis and evaluation of the issues highlighted in the incidents. It was found that incidents related to USB memory often involved them being inadvertently placed in pockets and sent for laundering. Additionally, document-related incidents could be categorized into six distinct clusters.

Last year we proposed the application of order trajectory curve analysis to hospital mangement, which will be useful to analyze the global behavior of hospital activities. This paper proposes multi-dimensional trajectories mining to analyze the temporal characteristics of hospital services. Order Trajectories mining method consists of the following two process. First the similarities between temporal trajectories of selected variables are calculated. Second, similarity-based analysis technique such as clustering and multidimensional scaling are applied. The method was evaluated on data on the number of orders extracted from hospital information system. The results showed that the method discovered several important characteristics of the divisions in the hospital..

The pandemic of COVID-19 reminds us of the basic important principles for prevention of infection: avoid the "Three Cs": closed spaces, crowded places and close contact settings. Outpatient clinics in Japan are typical examples of three Cs, where some kinds of decision support system are required to solve the above situation. This paper proposes data mining based patient navigation support system to prevent the Three Cs. Behind the systems, temporal data mining units plays an important role in providing temporal information to the patients, such as waiting time and human densities in the waiting rooms. It analyzes the data stored in hospital information systems, including patient information, logs of clinical orders. The analysis results show that several aspects of patients’ waiting are visualized by temporal data mining.

One of the most important task of data mining in hospital is to discover structured knowledge about decision making, which is useful for management of clinical process. However, most of the data in hospital information are stored without classification labels or meaning of clinical actions. Thus, unsupervised learning techniques are required for analysis. This paper proposes a method which induces a clinical pathway by using sample and attribute clustering of the histories of nursing orders stored in hospital information system. The method consists of the following five steps: first, frequencies of nursing orders are extracted from hospital information system as a dataset in which row and column represents nursing orders and days. Second, orders are classified into several groups by using sample clustering. Then, attributes clustering is applied to the data for feature selection. Fourth, for each sample and attribute clustering, the number of clusters are obtained from the sequence of the height values and according to the results of attribute clustering, the original dataset is decomposed into subtables. Then, the second to fourth steps will be repeated in a recursive way until the grouping of attributes (days) are stable. Finally, a new pathway will be constructed from all the induced results. The proposed method was evaluated on datasets extracted from a hospital information system. The experiment results show that the method is useful for construction of a clinical pathway when the distribution of length of stay is uni-modular.

Two of the most important roles of hospital information system (HIS) is to transfer clinical orders issued by doctors and nurses to other division and to store results of executed orders. Thus, the numbers of issued and executed orders will reflect the clinical activities in large hospitals. This paper proposes a visualization technique, called order trajectory analysis which visualize the temporal sequences of the number of orders. Then, clustering is applied to the order trajectory in order to show the similarities between clinical divisions.

In this paper, we propose a method to mine frequent temporal patterns from medical time series based on fuzzy ranged interval relations. We firstly introduce the concept of ranged interval relations and then extend it to fuzzy relations in order to make it possible to work with fuzziness of duration like days or weeks and to generate pattens associated with abstracted durations. Through the experiments on a synthetic dataset we demonstrate that our approach enables a sequence to simultaneously belong to multiple relations and that it is possible to control the level of concordance for a case to support a pattern by changing the threshold of membership grade.

This paper proposes a method which classifies discharge summaries stored in hospital information system, which consists of the following four steps. First, a term matrix of the set of summaries is induced by morphological analysis (RMecab). Next, correspondence analysis is applied to the term matrix and numerical values of two dimensional coordinates are assigned to each keyword and each concept. By measuring the euclidean distance between categories and keywords, keywords are ordered. Then, keywords are selected as attributes according to the rank, and training examples for classifiers will be generated. Finally, learning methods are applied to the training examples. Experimental validation shows that random forest achieved the best performance and deep learning (multiple layer perceptron) is the second best.

This paper proposes construction of clinical process plan from nursing order histories and discharge summaries stored in hospital information system. First, the system extracts subgrouping from clinical cases with the same Diagnostic Procedure Combination code (DPC) by mixture model clustering. Subgroups give different types of diseases with different temporal evolution. Then, classification models of each subgroup are constructed by the analysis of discharge summaries to capture the meaning of each subgroup. Finally, cases are classified by using the classification model and a clinical pathway is generated for each new subgroup. The proposed method was evaluated on the datasets extracted hospital information system, whose results show that plausible clinical pathways were obtained, compared with previously introduced methods.

Electronic patient records (EPR) are rich in texts, where almost all the decision making processes of medical staff are written. Thus, mining in EPR is important for acquision of decision making process and diagnosis. In this paper, as a first step, we focus on text mining for discharge summaries, which include the compact explanation for the patient's admission. a record of her complaints, physical findings, laboratory results and radiographic studies while hospitalized; a list of changes in her medications at discharge; and recommendations for follow up care. Text mining process consists of the following four processes: first, morphological analysis is applied to a set of summaries and a term matrix is generated. Second, correspond analysis is applied to the classification labels and the term matrix and generates two dimensional coordinates. By measuring the distances between categories and the assigned points, ranking of key words will be generated. Then, keywords are selected as attributes according to the rank, and training examples for classifiers will be generated. Finally, learning methods are applied to the training examples. Experimental validation shows that random forest achieved the best performance and the second best was the deep learner with a small difference, but decision tree methods with many keywords performed only a little worse than neural network or deep learning methods.

This paper proposes a method which induces a clinical pathway by using sample and attribute clustering of the histories of nursing orders stored in hospital information system. The method consists of the following five steps: first, frequencies of nursing orders are extracted from hospital information system. Second, orders are classified into several groups by using sample clustering. Then, attributes clustering is applied to the data for feature selection. Fourth, the method compares between generated functions for sample and attribute clustering which relate the number of clusters and calculated similarities. Fifth, if attribute clustering gives better performance with respect to the function, the dataset is decomposed into subtables by using the grouping of attribute clustering. Then, the first step will be repeated in a recursive way. After the grouping results are stable, a new pathway will be constructed from all the induced results. The method was applied to datasets of a disease extracted from a hospital information system. The results show that the proposed method is useful for construction of a clinical pathway.

This paper proposes an active mining process for improvement of quality of clinical process by using service logs in a hospital information system. First, datasets of temporal change of the number of orders are extracted from service logs stored in hospital information system. Then, since datasets of temporal change can be viewed as time-series of a statistic, clustering can be applied to the data. By using the groups obtained, datasets of command sequences are extracted from the logs and sequence mining process is applied. The results of sequence mining are interpreted with the results of clustering and hypothesis will be generated. The results show that the method improved the clinical process and waiting time in outpatient clinic.

This paper dicusses how to formalize medical diagnostic reasoing from the viewpoint of rule reasoning. Characteristics of rules shows that the rule model is closely related with rough set rule model. The important point is that medical diagnostic reasoning is characterized by focusing mechanism, composed of screening and differential diagnosis, which corresponds to upper approximation and lower approximation of a target concept in rough set theory. Furthremore, this paper focuses on detection of complications, which can be viewed as relations between rules of different diseases.

This paper proposes a method for construction of a clinical pathway based on attribute and sample clustering, called dual clustering. The method consists of the following five steps: first, histories of nursing orders are extracted from hospital information system. Second, orders are classified into several groups by using clustering on the pricipal components (sample clustering). Third, attributes clustering is applied to the data. Fourth, the method compares between generated functions for sample and attribute clustering which relate the number of clusters and calculated similarities. Fifth, if attribute clustering gives better performance with respect to the function, the dataset is decomposed into subtables by using the grouping of attribute clustering. Then, the first step will be repeated in a recursive way. After the grouping results are stable, a new pathway will be constructed from all the induced results. The method was applied to datasets of a disease extracted from a hospital information system. The results show that the proposed method is useful for construction of a clinical pathway.

Schedule management of hospitalization is important to maintain or improve the quality of medical care. Application of a clinical pathway has been proposed as one of the important solutions for the management. This research proposed an data-oriented maintenance and construction of clinical pathways by using data on histories of nursing orders stored in hospital information system. The method was evaluated on data extracted from a hospital information system. The results show that the reuse of stored data will give a powerful tool for management of nursing schedule and lead to improvement of hospital services.

This paper proposes granularity-based temporal data mining method which constructs clinical process conducted by nurses. The methods consist of three process. First, data on counting sum of executed orders are extracted from hospital informaton system with a given temporal granularity. Then, similarity-based methods, such as clustering and multidimensional scaling (MDS) are applied to the data and the labels for grouping are obtained. By using the labels, rule induction is applied, and classification power of each attribute is estimated. The attributes are sorted by an index of classification power, the original dataset is decomposed into subtables. Clustering, rule induction and table decomposition methods are applied to the subtables in a recursive way. The method was applied to datasets stored in hospital information system stored in 10 years. The results show that the reuse of stored data will give a powerful tool for construction of clinical process, which can be viewed as data-oriented management of nursing schedule.

This paper focuses on the degree of freedom and number of subdeterminants in a Pearson residual in a multiway contingency table. The results show that multidimensional residuals are represented as linear sum of determinants of 2 × 2 submatrices, which can be viewed as information granules measuring the degree of statistical dependence. Geometrical interpretation of Pearson residual is investigated. Furthermore, the number of subdeterminants in a residual is equal to the degree of freedom in χ2-test statistic. Since the way of calculation of the number of subdeterminants corresponds to the construction of a statistical model for a contingency table, it has been found that the combinatorics of the number subdeterminants is closely related with permutation of attributes in a given table, where symmetric group may play an important role.

This paper presents a cluster analysis method for multidimensional time-series data on clinical laboratory examinations. Our method represents the time series of test results as trajectories in multidimensional space, and compares their structural similarity by using the multiscale comparison technique. It enables us to find the part-to-part correspondences between two trajectories, taking into account the relationships between different tests. The resultant dissimilarity can be further used with clustering algorithms for finding the groups of similar cases. The method was applied to the cluster analysis of Albumin-Platelet data in the chronic hepatitis dataset. The results denonstrated that it could form interesting groups of cases that have high correspondence to the fibrotic stages.

In this paper, we focus on the application of knowledge engineering techniques to medical mobile communication network, where the Web intelligence technologies are used for an efficient interface of medical expert system. Then, the system was put on the Internet to provide an intelligent decision support in telemedicine and is now being evaluated by region medical home doctors. The results show that such an Internet-based medical decision support enables home doctors to take a quick action to the applied domain.

Data mining in time-series medical databases has been receiving considerable attention because it provides a way of revealing useful information hidden in the database, for example, relationships between the temporal course of examination results and the onset time of diseases. This article presents a new method for finding similar patterns in temporal sequences. The method is a hybridization of phase-constraint multiscale matching and rough clustering. Multiscale matching enables us to cross-scale a comparison of the sequences, namely, it enables us to compare temporal patterns by partially changing observation scales. Rough clustering enables us to construct interpretable clusters of the sequences even if their similarities are given as relative similarities. We combine these methods and cluster the sequences according to the multiscale similarity of patterns. Experimental results on the chronic hepatitis dataset showed that clusters demonstrating interesting temporal patterns were successfully discovered. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 737–757, 2005.

One of the most important problems with rule induction methods is that it is very difficult for domain experts to check millions of rules generated from large datasets. The discovery from these rules requires deep interpretation from domain knowledge. Although several solutions have been proposed in the studies on data mining and knowledge discovery, these studies are not focused on similarities between rules obtained. When one rule r/sub 1/ has reasonable features and the other rule r/sub 2/ with high similarity to r/sub 1/ includes unexpected factors, the relations between these rules will become a trigger to the discovery of knowledge. We propose a visualization approach to show the similar relations between rules based on multidimensional scaling, which assign a two-dimensional cartesian coordinate to each data point from the information about similarities between this data and others data. We evaluated this method on two medical data sets, whose experimental results show that knowledge useful for domain experts could be found.

One of the most important problems with rule induction methods is that they cannot extract rules, which plausibly represent expert's decision processes. Here, the characteristics of expert's rules are closely examined and a new approach to extract plausible rules is introduced, which consists of the following three procedures. First, the characterization of decision attributes (given classes) is extracted from databases and the concept hierarchy for given classes is calculated. Second, based on the hierarchy, rules for each hierarchical level are induced from data. Then, for each given class, rules for all the hierarchical levels are integrated into one rule.

Data mining in time-series medical databases has been receiving considerable attention since it provides a way of revealing useful information hidden in the database; for example relationships between temporal course of examination results and onset time of diseases. This paper presents a new method for finding similar patterns in temporal sequences. The method is a hybridization of phase-constraint multiscale matching and rough clustering. Multiscale matching enables us cross-scale comparison of the sequences, namely, it enable us to compare temporal patterns by partially changing observation scales. Rough clustering enable us to construct interpretable clusters of the sequences even if their similarities are given as relative similarities. We combine these methods and cluster the sequences according to multiscale similarity of patterns. Experimental results on the chronic hepatitis dataset showed that clusters demonstrating interesting temporal patterns were successfully discovered.

The paper presents a novel, rough set-based clustering method that enables the evaluation of classification knowledge simplicity during the clustering procedure. The method iteratively refines equivalence relations so that they become a more simple set of relations that give adequate coarse classification to the objects. At each step of the iteration, the importance of the equivalence relation is evaluated on the basis of the newly introduced measure, indiscernibility degree. An indiscernibility degree is defined as a ratio of equivalence relations that classify the two objects into the same equivalence class. If an equivalence relation has the ability to discern two objects that have a high indiscernibility degree, a very fine classification is performed and then modified to regard them as indiscernible objects. The refinement is repeated, decreasing the threshold level of indiscernibility degree, and finally simple clusters can be obtained. Experimental results on the artificial data shows that iterative refinement of equivalence relation leads to successful generation of coarse clusters that can be represented by simple knowledge.