より詳細な情報
医療および臨床研究
Clusters of Comorbidities Based on Validated Objective Measurements and Systemic Inflammation in Patients with Chronic Obstructive Pulmonary Disease
Vanfleteren, Spruit, Groenen, Gaffron, van Empel, Bruijnzee, Rutten, Roodt, Wouters and Franssen (2013) American Journal of Respiratory and Critical Care Medicine 187(7):728-735
The co-occurrence of clinically important comorbidities in patients with chronic obstructive pulmonary disease (COPD) and the prevalence of multimorbidity in the pathophysiology of COPD were analyzed. Viscovery Profiler was used to identify comorbidity clusters in a data set of COPD patients and to characterize the clusters in terms of health status, clinical outcomes, and systemic inflammation.
Anomaly Detection in Emergency Call Data – the First Step to the Intelligent Emergency Call System Management
Klement and Snášel (2009) International Conference on Intelligent Networking and Collaborative Systems (INCoS), Barcelona, Spain
Past experiences in emergency calls are exploited to create a feedback to the emergency call taking process and a base for management decision support to improve the emergency response agencies organization and effectiveness. Viscovery SOMine was used to visualize the emergency call taking information system database characteristics and discover further knowledge therein.
Combining data mining and case-based reasoning for intelligent decision support for pathology ordering by general practitioners
Zhuang, Churilov, Burstein, and Sikaris (2009) European Journal of Operational Research, 195(3):662-675
In this paper a novel methodology for integrating data mining and case-based reasoning for decision support for pathology ordering is proposed. It is demonstrated how this methodology can facilitate intelligent decision support that is both patient-oriented and deeply rooted in practical peer-group evidence. It is illustrated how knowledge extracted through data mining with Kohonen’s self-organizing maps constitutes the base that, with further assistance of the modern data visualization tool Viscovery SOMine and online processing interfaces, can facilitate more informed evidential decision making by doctors in the area of pathology ordering.
Using supervised and unsupervised techniques to determine groups of patients with different doctor-patient stability
Siew, Churilov, Smith-Miles and Sturmberg (2008) Lecture Notes in Computer Science 5012:715-722
Similarities between any groupings found between unsupervised classification (SOFM) using SOMine and supervised (Classification and Regression Trees - CART) were compared and used to identify insights into factors associated with doctor-patient stability. Both methods resulted in many similar groupings indicating that self-perceived health and age are important indicators of stability. Profiles of patients that are at risk were identified.
Combining data mining and discrete event simulation for a value-added view of a hospital emergency department
Ceglowski, Churilov and Wasserthiel (2007) Journal of the Operational Research Society 58:246-254
Datea from treatments given to emergency patients were clustered using SOMine. Analysis revealed treatments related to injury (e.g., tetanus injections, dressings, sutures) and treatments related to illness (e.g., arterial blood gases, echocardiograms, and intravenous drug infusion. The results provide insight into the complex relationship between patient urgency, treatment and disposal, and the occurences of queues for treatment.
An Investigation of Emergency Department Overcrowding using Data Mining and Simulation: A Patient Treatment Type Perspective
Ceglowski, (2006) PhD Thesis, Monash University, Australia
To analyze the problem of overcrowding in emergency departments, homogenous clusters of patient treatment with similar activities were identified. Techniques from the dissociated methods of Data Mining and Management Science were combined within the hypothesis and experimentation framework of the scientific method. Viscovery SOMine has been used for discovery of patient treatment patterns. The clusters were combined with patient urgency and disposition to create “patient treatment types” that were tracked through the emergency department.
Knowledge Discovery through Mining Emergency Department Data
Ceglowski, Churilov and Wasserthiel (2005) Proceedings of the 38th Annual Hawaii International Conference on System Sciences 2005, Hawaii, USA
The complexity of hospital emergency department operations limits comprehension and inhibits efforts to improve efficiency. Attempts have been made to reduce the complexity by streaming patients into similar classes of treatment or grouping them into similar cases. These have not successfully modeled the treatment of patients. Data mining techniques were employed to reduce the complexity of hospital emergency department operations by streaming patients into similar classes of treatment or grouping them into similar cases. Viscovery SOMine was used to generate data models. The combination of a process philosophy with data mining resulted in the discovery of definitive “treatment pathways”. These pathways comprehensively model treatment of patients.
Analysis of hippocampal atrophy in alcoholic patients by a Kohonen feature map
Kurth, Wegerer, Reulbach, Lewczuk, Kornhuber, Steinhoff, and Bleich (2004) Clinical Neuroscience and Neuropathology Neuroreport, 15(2):367-371
The correlation of hippocampal volume with homocysteine, folate, vitamin B12 and B6 content in alcoholic patients and healthy controls was examined by applying a Kohonen feature map (KFM) and conventional statistics. The KFM proved to be a sensitive tool for visualization of statistical correlations in data sets, even when no further statistical information was available.
Using data mining techniques to identify groups of patients with different consultation satisfaction in general practice.
Siew, Churilov, Smith and Sturmberg (2004) The Sixth International Conference on Optimization: Techniques and Applications (ICOTA6 2004)
SOMine was used to identify groups of patients with different levels of satisfaction. Results showed that doctor-patient communication is the most important variable in predicting satisfaction. Other important factors are patients’ knowledge of their doctor, doctor-patient stability, patient’s age, and consultation difficulty and length. This study identified profiles of patients that put them at risk of poor satisfaction, which might be useful to physicians.
Emosys: a system to study hematological diseases
Starita, Rossi, Caracciolo, and Petrini (2004) Proceeding (417) Biomedical Engineering 2004, Innsbruck, Austria
The web-based Emosys system was developed both for managing and studying hematological diseases. The system is made of independent modules for the clinical management, for the collection of data, and for research on these diseases. Data mining techniques based on neural networks were used to confirm known results and/or to find interesting, unknown relationships among the data. The network was trained and the resulting map was graphically analyzed with Viscovery SOMine. The system was subjected to validation at the clinical site.
A neural clustering approach to iso-resource grouping for acute healthcare in Australia
Siew, Smith, Churilov and Ibrahim (2002) Proceedings of the 35th Hawaii International Conference on System Sciences 2002, Hawaii, USA
The Case Mix funding formula is the most widely used approach for classifying patients according to diagnostic related groups (DRGs). Although it is clinically meaningful, experience suggests that DRG groupings do not necessarily present a sound basis for relevant knowledge generation. An alternative grouping of the patients based on a neural clustering approach is proposed, which generates homogeneous groups of patients with similar resource utilization characteristics. Features of the data and the dependencies between the variables were identified and evaluated from the SOMine map.
Clinical-pathological classification of glioblastomas investigated by a non-supervised neural network
Iglesias-Rozas, Camara, and Schwemmle (2000) Electronic Journal of Pathology and Histology 6(2):2
Using a variant of unsupervised neural networks, the ability to reproduce a clinical-pathological classification of patients with glioblastomas was examined. This resulting self-organizing map provides a powerful means to visualize and analyze complex data sets without prior statistical knowledge and allows a specific visual evaluation of new treatments and a more effective comparison with established tumor management.
Light microscope heterogeneity of human glioblastomas investigated with an unsupervised neural network (SOM)
Iglesias-Rozas and Grieshaber (2000) Electronic Journal of Pathology and Histology 6(4):2
As an alternative to statistical evaluation of histological variability of glioblastomas, 1266 human glioblastomas were investigated to discover whether they can be correctly classified using self-organizing maps generated with Viscovery SOMine. Five clusters of glioblastomas with a maximum significance were found. A useful classification, comparable to the classification suggested by the World Health Organization, as well as the visualization of multidimensional histological features of human glioblastomas was achieved. The data can be used to improve patient management.
