DMCA
Logistic-Based Patient Grouping for Multi-disciplinary Treatment /DXUD0UXWHU
BibTeX
@MISC{Geerhard_logistic-basedpatient,
author = {Ton Weijters Geerhard and Geerhard De Vries and Antal Van Den Bosch and Walter Daelemans},
title = {Logistic-Based Patient Grouping for Multi-disciplinary Treatment /DXUD0UXWHU},
year = {}
}
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Abstract
this paper we present an alternative logistic-driven grouping approach. The starting point of our approach is a database with medical cases for 3603 patients with peripheral arterial vascular diseases. For these medical cases, six basic logistic variables (such as the number of visits to different specialist) are selected. Using these logistic variables, clustering techniques are used to group the medical cases in logistically homogeneous groups. In our approach, the quality of the resulting grouping is not measured by statistical significance, but by (i) the usefulness of the grouping for the creation of new multidisciplinary units, and (ii) how well patients can be selected for treatment in the new units. Given a-priori knowledge of a patient (e.g. age, diagnosis), machine learning techniques are employed to induce rules that can be used for the selection of the patients eligible for treatment in the new units. In the paper we describe the results of the above-proposed methodology for patients with peripheral arterial vascular diseases. Two groupings and the accompanied classification rule sets are presented. One grouping is based on all logistic variables, and another grouping is based on two variables, found by applying factor analysis. On the basis of the results we can conclude that the search for logistic homogenous groups has advantages over more traditional search for medically homogenous groups. 1.
Keyphrases
multi-disciplinary treatment dxud0uxwhu medical case logistic-based patient grouping new unit peripheral arterial vascular disease logistic variable above-proposed methodology factor analysis basic logistic variable new multidisciplinary unit alternative logistic-driven grouping approach homogenous group machine learning technique accompanied classification rule set statistical significance traditional search logistic homogenous group a-priori knowledge different specialist homogeneous group
