Application of clustering techniques for the characterization of macroinvertebrate communities to support river restoration management

With a constant increasing in the health expenses and the aggravation of the global economic situation, managing costs and resources in healthcare is nowadays an essential point in the management of hospitals. The goal of this work is to apply clustering techniques to data collected in real-time about readmitted patients in Intensive Care Units in order to know some possible features that affect readmissions in this area. By knowing the common characteristics of readmitted patients it will be possible helping to improve patient outcome, reduce costs and prevent future readmissions. In this study, it was followed the Stability and Workload Index for Transfer (SWIFT) combined with the results of clinical tests for substances like lactic acid, leucocytes, bilirubin, platelets and creatinine. Attributes like sex, age and identification if the patient came from the chirurgical block were also considered in the characterization of potential readmissions. In general, all the models presented very good results being the Davies-Bouldin index lower than 0.82, where the best index was 0.425.

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Development of an in-stream migration model for Gammarus pulex L. (Crustacea, Amphipoda) as a tool in river restoration management

Aquatic Ecology ◽

10.1007/s10452-005-9022-2 ◽

2005 ◽

Vol 40 (2) ◽

pp. 249-261 ◽

Cited By ~ 11

Author(s):

Andy P. Dedecker ◽

Peter L. M. Goethals ◽

Tom D’heygere ◽

Niels De Pauw

Keyword(s):

River Restoration ◽

Gammarus Pulex ◽

Migration Model ◽

Restoration Management ◽

Stream Migration

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Characterization of benthic macroinvertebrate communities in a restored stream by using self-organizing map

Ecological Informatics ◽

10.1016/j.ecoinf.2005.12.001 ◽

2006 ◽

Vol 1 (3) ◽

pp. 295-305 ◽

Cited By ~ 16

Author(s):

Mi-Young Song ◽

Young-Seuk Park ◽

Inn-Sil Kwak ◽

Hyoseop Woo ◽

Tae-Soo Chon

Keyword(s):

Benthic Macroinvertebrate ◽

Self Organizing Map ◽

Macroinvertebrate Communities ◽

Self Organizing

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Characterization of background particulate matter concentrations using the combination of two clustering techniques in zones with heterogeneous emission sources

Atmospheric Environment ◽

10.1016/j.atmosenv.2020.117832 ◽

2020 ◽

Vol 243 ◽

pp. 117832

Author(s):

Yumara Martín-Cruz ◽

Antonio Vera-Castellano ◽

Álvaro Gómez-Losada

Keyword(s):

Particulate Matter ◽

Emission Sources ◽

Clustering Techniques

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Clustering Techniques for Content-Based Feature Extraction From Image

Feature Dimension Reduction for Content-Based Image Identification - Advances in Multimedia and Interactive Technologies ◽

10.4018/978-1-5225-5775-3.ch006 ◽

2018 ◽

pp. 100-121 ◽

Cited By ~ 1

Author(s):

Madan U. Kharat ◽

Ranjana P. Dahake ◽

Kalpana V. Metre

Keyword(s):

Feature Extraction ◽

Image Retrieval ◽

Image Feature ◽

Image Feature Extraction ◽

Satellite Imaging ◽

Clustering Techniques ◽

Active Research ◽

Significant Attention ◽

Search And Retrieval

Image retrieval is gaining significant attention in areas such as surveillance, access control, etc. The content-based feature extraction plays a very crucial role in image retrieval. For the characterization of a specific image, mainly three features (i.e., color, texture, and shape) are used. Multimedia can store text, image, audio, and video which can be processed and retrieved. The various techniques are used for image retrieval such as textual annotations, content-based image retrieval in many application areas like medical imaging, satellite imaging, etc. However, most of these techniques were designed for specific domains and universally accepted method is yet to be designed; hence, CBIR is a field of active research. Similar output images indicate efficiency of search and retrieval process. In this chapter, the authors have discussed various image feature extraction techniques and clustering approaches for content-based feature extraction from image and specifically focused on color based CBIR techniques.

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Categorize Readmitted Patients in Intensive Medicine by Means of Clustering Data Mining

Hospital Management and Emergency Medicine ◽

10.4018/978-1-7998-2451-0.ch005 ◽

2020 ◽

pp. 84-99

Author(s):

Rui Veloso ◽

Filipe Portela ◽

Manuel Filipe Santos ◽

José Machado ◽

António da Silva Abelha ◽

...

Keyword(s):

Patient Outcome ◽

Data Mining ◽

Essential Point ◽

Clustering Techniques ◽

The Common ◽

Clustering Data ◽

The Stability ◽

Improve Patient ◽

Intensive Medicine

With a constant increasing in the health expenses and the aggravation of the global economic situation, managing costs and resources in healthcare is nowadays an essential point in the management of hospitals. The goal of this work is to apply clustering techniques to data collected in real-time about readmitted patients in Intensive Care Units in order to know some possible features that affect readmissions in this area. By knowing the common characteristics of readmitted patients it will be possible helping to improve patient outcome, reduce costs and prevent future readmissions. In this study, it was followed the Stability and Workload Index for Transfer (SWIFT) combined with the results of clinical tests for substances like lactic acid, leucocytes, bilirubin, platelets and creatinine. Attributes like sex, age and identification if the patient came from the chirurgical block were also considered in the characterization of potential readmissions. In general, all the models presented very good results being the Davies-Bouldin index lower than 0.82, where the best index was 0.425.

Download Full-text