This online library contains more than 200 scientific articles covering diverse applications in exploratory data mining, clustering and predictive analytics. It represents a selection of about 30% of the articles that became known to the Viscovery team until March 2023.

With this collection, you can draw on the wealth of experience Viscovery users have gained through the use of self-organizing maps in a variety of application areas.

If you have published an article containing a Viscovery analysis and would like to have it cited in one of the categories below, please contact us at

Select a category from the more than 200 articles in our library:

Multimodal predictions of treatment outcome in major depression: A comparison of data-driven predictors with importance ratings by clinicians

Rost, N., Dwyer, D. B., Gaffron, S., Rechberger, S., Maier, D., Binder, E. B., & BrĂĽckl, T. M. (2023). Journal of Affective Disorders.

The aim of this article is to find reliable models to predict treatment outcomes for patients with major depressive disorder. To this end, multiple outcome data were clustered using Viscovery´s SOM-Ward algorithm and with a consensus clustering approach utilizing a Python k-medoids algorithm. The resulting cluster models are in good accordance with each other and define useful outcome classes. In a second step, supervised machine learning methods, namely logistic regression and random forest, were used to predict outcome classes based on the patients´ baseline assessments.

Differential outcomes following 4 weeks of Aclidinium/Formoterol in patients with COPD: A reanalysis of the ACTIVATE study

Koopman, M., Franssen, F. M., Gaffron, S., Watz, H., Troosters, T., Garcia-Aymerich, J., ... & Spruit, M. A. (2022). International journal of chronic obstructive pulmonary disease, 517-533.

To better assess therapy with long-acting bronchodilators on spirometric outcomes, exercise performance and long term physical activity, multiple outcome metrics were collected for COPD patients in a randomized, double-blind, placebo controlled study for Aclidinium/Formoterol therapy. Viscovery SOMine is used to find typical response profiles and to compare the patients in the treatment group with the placebo group.

Clusters of comorbidities in fibrotic hypersensitivity pneumonitis

Prior, T. S., Wälscher, J., Gross, B., Bendstrup, E., & Kreuter, M. (2022). Respiratory Research, 23(1), 368.

Comorbidities with fibrotic hypersensitivity pneumonitis and their impact on survival are studied in this article. Viscovery SOMine is used to identify and analyze comorbidity clusters. Cox regressions are used to analyze survival rates for patients in the different comorbidity clusters. Particularly interesting are the observable differences between this cohort of patients and the cohort with unclassifiable interstitial lung disease studied by Prior et. al. (c.f. paper below).

Comorbidities in unclassifiable interstitial lung disease

Prior, T. S., Hyldgaard, C., Torrisi, S. E., Kronborg-White, S., Ganter, C., Bendstrup, E., & Kreuter, M. (2022). Respiratory Research, 23(1), 59.

Comorbidities with unclassifiable interstitial lung disease and their impact on survival are studied in this article. Viscovery SOMine is used to identify and analyze comorbidity clusters. Cox regressions are used to analyze survival rates for patients with specific individual comorbidities and for the comorbidity clusters.

Multidimensional outcome assessment of pulmonary rehabilitation in traits-based clusters of COPD patients

Augustin, I. M., Franssen, F. M., Houben-Wilke, S., Janssen, D. J., Gaffron, S., Pennings, H. J., ... & Spruit, M. A. (2022). PloS one, 17(2), e0263657.

In this study, the outcome of pulmonary rehabilitation for COPD patients is assessed using multiple metrics that include pulmonary, performance and psychological traits. Viscovery SOMine is used to define an overall outcome measure from those metrics and to identify patient groups with very good, good, moderate and poor responses. Furthermore, the ability to predict overall outcome from a comprehensive baseline cluster model that uses pulmonary and extra-pulmonary traits as well as general health status, is assessed.

Pulmonary rehabilitation: a learning health care model?

Augustin, I. M. (2022).

In this doctoral thesis, I. Augustin summarizes her work on comprehensive lung function assessments, pulmonary rehabilitation and multidimensional therapeutic response for COPD patients. Viscovery SOMine is used for several different cluster models and statistical analyses.

Clustering based on comorbidities in patients with chronic heart failure: an illustration of clinical diversity

Uszko-Lencer, N.H.M.K., Janssen, D.J.A., Gaffron, S., Vanfleteren, L.E.G.W., Janssen, E., Werter, C., ..., Spruit, M.A. (2021). ESC Heart Failure, 2.13704.

This article analyzes data from 603 patients with chronic heart failure who participated in a specialized heart failure rehabilitation program. Viscovery SOMine is used to find clusters of patients with similar comorbidity profiles, which are then screened for significant differences in extracardiac physical and psychological characteristics.

Clusters of comorbidities in idiopathic pulmonary fibrosis

Prior, T. S., Hoyer, N., Hilberg, O., Shaker, S. B., Davidsen, J. R., Rasmussen, F., & Bendstrup, E. (2021). Respiratory Medicine, 106490.

The focus of this article is to find comorbidity phenotypes for idiopathic pulmonary fibrosis and assess their influence on disease trajectory. To accomplish this, Viscovery SOMine is used to find comorbidity clusters, which are then used within mixed effects models and Cox regressions to find comorbidity phenotype specific disease slopes and hazard ratios.

Comorbidity clusters in patients with moderate-to-severe OSA

Testelmans, D., Spruit, M. A., Vrijsen, B., Sastry, M., Belge, C., Kalkanis, A., ... & Buyse, B. (2021). Sleep and Breathing, 1-10.

A cohort of 710 patients with moderate to severe obstructive sleep apnea is analyzed. Viscovery SOMine is used to find clinically relevant comorbidity clusters.

Incorporating comprehensive assessment parameters to better characterize and plan rehabilitation for persons with chronic obstructive pulmonary disease

Augustin, I. M., Spruit, M. A., Franssen, F. M., Gaffron, S., van Merode, F., & Wouters, E. F. (2020). Journal of the American Medical Directors Association, 21(12), 1986-1991.

This article describes a follow-up clustering model to an earlier article on COPD by Augustin et. al.. In addition to pulmonary traits, the new Viscovery model incorporates extrapulmonary functional traits and general health status to give a more detailed view on persons with chronic obstructive pulmonary disease.

Clustering of 27,525,663 death records from the United States Based on health conditions associated with death: an example of big health data exploration

Janssen, D., Rechberger, S., Wouters, E., Schols, J., Johnson, M., Currow, D., Curtis, J., & Spruit, M. (2019). Journal of Clinical Medicine, 8(7), 922.

Following a population-based approach to palliative care, this article analyzes more than 27 million US death certificates from 2006 to 2016 with respect to common comorbidities, socio-demographic background and circumstances of death. Viscovery SOMine's Big Data capabilities are used to cluster the death records in the 346-dimensional space of health conditions, to identify population-based patterns, and to derive implications for palliative care.

Comprehensive lung function assessment does not allow to infer response to pulmonary rehabilitation in patients with COPD

Augustin, I., Wouters, E., Houben-Wilke, S., Gaffron, S., Janssen, D., Franssen, F., & Spruit, M. (2019). Journal of Clinical Medicine, 8(1), 27.

This article examines whether the extent of improvement in lung function, exercise performance, daily activities, mood, and disease-specific health status of COPD patients undergoing pulmonary rehabilitation can be predicted from the lung function profile prior to rehabilitation. The lung function data is clustered with Viscovery SOMine to obtain and statistically analyze groups of patients with similar profiles.

Disease-specific comorbidity clusters in COPD and accelerated aging

Triest, F. J., Franssen, F. M., Reynaert, N., Gaffron, S., Spruit, M. A., Janssen, D. J., ... & Vanfleteren, L. E. (2019). Journal of clinical medicine, 8(4), 511.

This article analyzes the connection between disease-specific comorbidity clusters in COPD patients and accelerated aging. Viscovery SOMine is used to cluster COPD patients and a comparable control group to evaluate the decrease in telomere length in COPD-specific clusters.

The respiratory physiome: clustering based on a comprehensive lung function assessment in patients with COPD

Augustin, I. M., Spruit, M. A., Houben-Wilke, S., Franssen, F. M., Vanfleteren, L. E., Gaffron, S., ... & Wouters, E. F. (2018). PloS one, 13(9), e0201593.

The aim of this paper is to provide a comprehensive description of lung function for patients with chronic obstructive pulmonary disease (COPD) and study its connection to functional performance and health status. The Viscovery SOMine cluster model shows that lung-function impairment is a multidimensional problem, which cannot be characterized by a single measurement. In addition, it is shown that the lung-function profile alone is a poor predictor for functional performance, highlighting the necessity of additional performance tests and questionnaires to provide optimal treatment for patients.

HIV/Human herpesvirus co-infections: impact on tryptophan-kynurenine pathway and immune reconstitution

Yap, S. H., Abdullah, N. K., McStea, M., Takayama, K., Chong, M. L., Crisci, E., ... & Woo, Y. L. (2017). PloS one, 12(10), e0186000.

This article attempts to clarify the impact of human herpesvirus co-infections of patients infected with human immunodeficiency virus (HIV) on the kynurenine/tryptophan (KT) ratio and long-term CD4 T-cell recovery in antiretroviral treatment. Viscovery SOMine clustering shows that different types and combinations of herpes infections have varying effect on K/T ratio.

Differential response to pulmonary rehabilitation in COPD: multidimensional profiling

Spruit, M. A., Augustin, I. M., Vanfleteren, L. E., Janssen, D. J., Gaffron, S., Pennings, H. J., ... & Groenen, M. T. (2015). European Respiratory Journal, 46(6), 1625-1635.

Multidimensional response to pulmonary rehabilitation in patients with chronic obstructive pulmonary disease (COPD) is studied. Viscovery SOMine identified distinct response profiles; moreover, response outcome depends only slightly on the baseline status of patients.

Objectively identified comorbidities in COPD: impact on pulmonary rehabilitation outcomes

Mesquita, R., Vanfleteren, L. E., Franssen, F. M., Sarv, J., Taib, Z., Groenen, M. T., ... & Spruit, M. A. (2015). European Respiratory Journal, 46(2), 545-548.

Based on the Viscovery SOMine cluster model from "Clusters of comorbidities based on validated objective measurements and systemic inflammation in patients with chronic obstructive pulmonary disease" by Vanfleteren et al., rehabilitation outcomes of patients with chronic obstructive pulmonary disease (COPD) are analyzed. This analysis shows that the existence of comorbidities has only a small influence on the achievable outcomes.

Cluster analyses in a sample of COPD patients

Margotto, S., Caram, L., Tanni, S., Ferrari, R., Bertani, A., Garcia, T., ... & Godoy, I. (2014). European Respiratory Journal, 44(Suppl 58), P612.

To optimize pharmacological treatment and recognize mortality risk factors, a segmentation of chronic obstructive pulmonary disease (COPD) patient groups is obtained. Viscovery Profiler is used on metered data (physical examination, laboratory tests, spirometry), medical history (smoking history, Charlson comorbidity index), and survey data (St. George's Respiratory Questionnaire) and identifies six distinct clusters of patients.

Integrative understanding of macular morphologic patterns in diabetic retinopathy based on self-organizing map

Murakami, T., Ueda-Arakawa, N., Nishijima, K., Uji, A., Horii, T., Ogino, K., & Yoshimura, N. (2014). Investigative ophthalmology & visual science, 55(3), 1994-2003.

This article analyzes the morphologic patterns in patients with diabetic retinopathy. Several parameters obtained via OCT scans are clustered via Viscovery SOMine, identifying distinct morphological profiles.

Classification of age-related changes in lumbar spine with the help of MRI scores

Khan, A., Iliescu, D., Hines, E., Hutchinson, C., Sneath, R. (2013).

The variation of normal age-related changes in the lumbar spine is characterized and distinguished from pathological deformities. Viscovery SOMine is used on geometric measurements obtained by MRI scans of the lumbar spine and shows that vertebral height and fat signals do not significantly change during normal ageing, whereas changes in disc height, para-spinal-muscle signal intensity and psoas muscle are distinctly correlated with ageing.

Clusters of comorbidities based on validated objective measurements and systemic inflammation in patients with chronic obstructive pulmonary disease

Vanfleteren, L. E., Spruit, M. A., Groenen, M., Gaffron, S., van Empel, V. P., Bruijnzeel, P. L., ... & Franssen, F. M. (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 are analyzed. Viscovery Profiler is 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.

HMGA2 expression in white adipose tissue linking cellular senescence with diabetes

Markowski, D. N., Thies, H. W., Gottlieb, A., Wenk, H., Wischnewsky, M., & Bullerdiek, J. (2013). Genes & nutrition, 8(5), 449.

This study analyzes the connection between obesity, gain of white adipose tissue, type 2 diabetes and HMGA2 expression. The Viscovery SOMine model suggests a higher risk for type 2 diabetes in patients with a high HMGA2 expression.

A psycho-cognitive segmentation of organ donors in Egypt using Kohonen’s self-organizing maps

Mostafa, M. M. (2011). Expert Systems with Applications, 38(6), 6906-6915.

The psychographic background of organ donors is the focus of this paper. Viscovery SOMine is used to cluster panel data about Egyptian citizens’ attitudes towards organ donation.

47 glioblastoma gene expression profile diagnostics by the artificial neural networks

A. A. Mekler, I. Knyazeva, D. R. Schwartz, Y. A. Kuperin, V. V. Dmitrenko, V. I. Rymar, and V. M. Kavsan. (2010). Optical Memory and Neural Networks, 19(2), 181-186.

The connection between gene expression profiles and glioblastoma incidences is analyzed. Viscovery SOMine and a feed-forward neural network are used to classify the data and both produce very good results.

Combining data mining and case-based reasoning for intelligent decision support for pathology ordering by general practitioners

Zhuang, Z. Y., Churilov, L., Burstein, F., & Sikaris, K. (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. 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.

Psychographic clustering of blood donors in Egypt using Kohonen's self-organizing maps

Mostafa, M. M. (2010). International Journal of Nonprofit and Voluntary Sector Marketing, 15(2), 157-171.

This paper aims to find the motives behind donating (or not donating) blood. Viscovery SOMine is used on a data set consisting of panel data about Egyptian citizens’ view on blood donation.

Using supervised and unsupervised techniques to determine groups of patients with different doctor–patient stability

Siew, E. G., Churilov, L., Smith-Miles, K. A., & Sturmberg, J. P. (2008, May). In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 715-722). Springer, Berlin, Heidelberg.

Similarities between any groupings found with unsupervised classification using Viscovery SOMine and supervised classification using classification and regression trees are compared and used to identify insights into factors associated with doctor–patient stability. Both methods result in many similar groupings, indicating that self-perceived health and age are important indicators of stability. Profiles of patients that are at risk are identified. 

Using self organising feature maps to unravel process complexity in a hospital emergency department: a decision support perspective

Ceglowski, A., & Churilov, L. (2008). In Intelligent Decision Making: An AI-Based Approach (pp. 365-385). Springer, Berlin, Heidelberg.

This book chapter describes how to use self-organizing maps to create a decision support system for hospital management practice. Viscovery SOMine is used to cluster emergency patients with respect to treatments performed and thus to reveal actual work processes.

Towards process-of-care aware emergency department information systems: a clustering approach to activity views elicitation

Ceglowski, A. S., & Churilov, L. (2008). International Journal of Healthcare Information Systems and Informatics (IJHISI), 3(4), 1-16.

The focus of this article is on building an emergency department IT system and an analytical model to predict the patient workflow. The system is based on the treatment cluster model introduced by Ceglowski et. al. (2017) in their article "Combining data mining and discrete event simulation for a value-added view of a hospital emergency department" to predict likely treatment paths and resources needed for new patients.

Combining data mining and discrete event simulation for a value-added view of a hospital emergency department

Ceglowski, R., Churilov, L., & Wasserthiel, J. (2007). Journal of the Operational Research Society, 58(2), 246-254.

Data from treatments given to emergency patients are clustered using Viscovery SOMine. Analysis reveals clusters of treatments related to injury (e.g., tetanus injections, dressings, sutures) and clusters of treatments related to illness (e.g., arterial blood gases, echocardiograms, and intravenous drug infusion). The results provide insight into the complex relationship between urgency, patient treatment and discharge, and the occurrences of queues for treatment.

An investigation of emergency department overcrowding using data mining and simulation: a patient treatment type perspective

Ceglowski, A. S. (2006).

To analyze the problem of overcrowding in emergency departments, homogenous clusters of patient treatment with similar activities are identified. Techniques from the dissociated methods of data mining and management science are combined within the hypothesis and experimentation framework of the scientific method. Viscovery SOMine is used for discovery of patient treatment patterns. The clusters are combined with patient urgency and disposition to create “patient treatment types” that are tracked through the emergency department.

Histological heterogeneity of human glioblastomas investigated with an unsupervised neural network (SOM)

Iglesias-Rozas, J. R., & Hopf, N. (2005). Histology and histopathology, 20(2), 351-356.

This paper aims at validating the World Health Organization classification of human glioblastomas and identifying new sub-groups. Viscovery SOMine is used to cluster 1489 glioblastomas with respect to 50 histological features, age and sex of the patients, providing new interesting insights into this form of brain tumor.

On visual exploration of breast cancer data using the self-organizing map

Eklund, T., Collan, M., Jalava, P., Kuopio, T., & Collan, Y. (2005). WSOM 2005 - 5th Workshop on Self-Organizing Maps.

This study describes a showcase analysis of 497 incidences of breast cancer to confirm well-known facts and demonstrate the use of self-organizing maps in cancer research. Viscovery SOMine is used for exploratory data analysis with respect to patient age, estrogen-receptor status, lymph-node status, size and histological grade of the tumor. A positive correlation between tumor size and histological grade and tumor size and likelihood of metastases as well as an inverse correlation between estrogen level and histological grade are found.

Analysis of hippocampal atrophy in alcoholic patients by a Kohonen feature map

Kurth, C., Wegerer, V., Reulbach, U., Lewczuk, P., Kornhuber, J., Steinhoff, B. J., & Bleich, S. (2004). Neuroreport, 15(2), 367-371.

The correlation of hippocampal volume with homocysteine, folate, vitamin B12 and B6 content in alcoholic patients and healthy controls is examined by applying Viscovery SOMine and conventional statistics. Viscovery SOMine proves to be a sensitive tool for visualization of statistical correlations in data sets, even when no further statistical information is available.

A neural clustering approach to iso-resource grouping for acute healthcare in Australia

Siew, E. G., Smith, K. A., Churilov, L., & Ibrahim, M. (2002, January). In System Sciences, 2002. HICSS. Proceedings of the 35th Annual Hawaii International Conference on (pp. 11-pp). IEEE.

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 patients based on a neural clustering approach is proposed, generating homogeneous groups of patients with similar resource-utilization characteristics. Features of the data and the dependencies between the variables are identified and evaluated from the Viscovery SOMine map.

Clinical–pathological classification of glioblastomas investigated by a non-supervised neural network

Iglesias-Rozas, J. R., Camara, M., & Schwemmle, C. (2000). Electronic Journal of Pathology and Histology, 6(2), 06-06.

Using a variant of unsupervised neural networks, the ability to reproduce a clinical–pathological classification of patients with glioblastomas is 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, J. R., & Grieshaber, F. (2000). Electronic Journal of Pathology and Histology, 6(4), 02-02.

As an alternative to statistical evaluation of histological variability of glioblastomas, 1266 human glioblastomas are 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 are 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, is achieved. The data can be used to improve patient management.

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