Haruko Iwata

Similarity-based behavior and process mining of medical practices

This paper presents data mining results in which the temporal behavior of global hospital activities is visualized. The results show that the reuse of stored data will provide a powerful tool for hospital management and lead to improvement of hospital services.

Characterizing Hospital Services Using Temporal Data Mining

Computerization of hospital information enables us to visualize and analyze temporal characteristics of hospital services, which can be viewed as a first step to improve and innovate clinical services. This paper proposes a temporal data mining process which consists of decision tree, clustering, MDS and three-dimensional trajectories mining and applied the method to datasets extracted from hospital information systems. The results show that the reuse of stored data will give a powerful tool to characterize medical services in the following ways: (1) Statistics and temporal characteristics of clinincal orders were visualized. (2) Divisions were classified in terms of temporal patterns of orders. (3) The temporal interval important to characterize the behavior of the divisions were evaluated. (4) Characterization of nursing orders showed the classification of nursing orders into disease-specific ones and patient- specific ones.

Mining Clinical Pathway Based on Clustering and Feature Selection

Schedule management of hospitalization is important to maintain or improve the quality of medical care and application of a clinical pathway is one of the important solutions for the management. This research proposed an data-oriented maintenance of existing clinical pathways by using data on histories of nursing orders. If there is no clinical pathway for a given disease, the method will induce a new clinical care plan from the data. The method was evaluated on 10 diseases. 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.

Maintenance and Discovery of Domain Knowledge for Nursing Care using Data in Hospital Information System

[Introduction] Schedule management of hospitalization is important to maintain or improve the quality of medical care and application of a clinical pathway is one of the important solutions for the management. Although several kinds of deductive methods for construction for a clinical pathway have been proposed, the customization is one of the important problems. This research proposed an inductive approach to support the customization of existing clinical pathways by using data on nursing actions stored in a hospital information system. [Method] The number of each nursing action applied to a given disease during the hospitalization was counted for each day as a temporal sequence. Temporal sequences were compared by using clustering and multidimensional scaling method in order to visualize the similarities between temporal patterns of clinical actions. [Results] Clustering and multidimensional scaling analysis classified these orders to one group necessary for the treatment for this DPC and the other specific to the status of a patient. The method was evaluated on data sets of ten frequent diseases extracted from hospital information system in Shimane University Hospital. Cataracta and Glaucoma were selected. Removing routine and poorly documented nursing actions, 46 items were selected for analysis. [Discussion] Counting data on executed nursing orders were analyzed as temporal sequences by using similarity-based analysis methods. The analysis classified the nursing actions into the two major groups: one consisted of orders necessary for the treatment and the other consisted of orders dependent on the status of admitted patients, including complicated diseases, such as DM or heart diseases. The method enabled us to inductive construction of standardized schedule management and detection of the conditions of patients difficult to apply the existing or induced clinical pathway.

Construction of Clinical Pathway based on Similarity-based Mining in Hospital Information System☆

Abstract This paper proposes construction of clinical care plan conducted by nurses by using data mining methods. The key idea is to summarize the history of nursing orders into numerical temporal sequences with admission dates, which is the best temporal granularity for thsis analysis. After extracting numerical temporal sequences on frequencies of nursing care, 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 date is estimated. The admission dates 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.

Data-Oriented Construction and Maintenance of Clinical Pathway Using Similarity-Based Data Mining Methods

Since hospital data include temporal trends of clinical symptoms and medical services, we can discover not only knowledge about temporal evolution of disease, but also one about medical practice from hospital information system, which will lead to data-oriented hospital management. This paper proposes temporal data mining process and applied the method to construction and revision of clinincal pathway by using temporal knowledge obtained from data. The results show that the reuse of stored data will give a powerful tool for maintenance and construction of clinincal pathway, which can be viewed as data-oriented management of nursing schedule.

Construction of clinical pathway from histories of clinical actions in hospital information system

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.

Clinical Schedule Management Using Similarity-Based Mining Methods

This paper proposes a temporal data mining method to maintain a clinical pathway used for schedule management of clinical care. The method consists of the following four steps: first, histories of nursing orders are extracted from hospital information system. Second, orders are classified into several groups by using clustering and multidimensional scaling method. Third, by using the information on groups, feature selection is applied to the data and important features for classification are extracted. Finally, original temporal data are split into several groups and the first step will be repeated. After the grouping results are stable, a new pathway is constructed based on the induced results. The method was applied to datasets of several diseases extracted from a hospital information system. The results show that the reuse of stored data will give a powerful tool for maintenance of clinical pathway, which can be viewed as data-oriented management of nursing schedule. The abstract goes here.

Exploratory temporal data mining process in hospital information systems

This paper proposes an exploratory temporal data mining process which aims at capturing behavior of medical staff. The process consists of the following four process. First, datasets will be extracted from hospital information systems through double-step datawarehousing. Second, similarities between temporal sequences are calculated from datasets. Third, data mining methods such as clustering, multidimensional scaling are applied for obtaining the class labels. Finally, other data mining methods, such as decision tree and correspondence analysis are applied to original data sets with the class labels.

Temporal data mining of order entry histories for characterization of medical practice

Since hospital data include temporal trends of clinical symptoms and medical services, we can discover not only knowledge about temporal evolution of disease, but also one about medical practice from hospital information system. This paper proposes temporal data mining process and applied the method to capture temporal knowledge about nursing practice. 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.