PRIM versus CART in subgroup discovery: When patience is harmful
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TLDR
Contrary to current conjectures, PRIM's performance was generally inferior to CART's, and its utility in clinical databases would increase when global information about (ordinal) variables is better put to use and when the search algorithm keeps track of alternative solutions.About:
This article is published in Journal of Biomedical Informatics.The article was published on 2010-10-01 and is currently open access. It has received 23 citations till now. The article focuses on the topics: Ordinal regression & Ordinal data.read more
Citations
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Development of a clinical decision support system for antibiotic management in a hospital environment
Bernardo Canovas-Segura,Manuel Campos,Antonio Morales,Jose M. Juarez,Francisco Javier Perales Palacios +4 more
TL;DR: The need for a multi-user perspective, both reactive and proactive behaviours, and the use of many heterogeneous knowledge sources are identified as the main requirements that differentiate this clinical scenario from a decision support point of view.
Journal ArticleDOI
Contrasting temporal trend discovery for large healthcare databases
TL;DR: This study introduces a novel approach for exploring and comparing temporal trends within different in-patient subgroups, which is based on associated rule mining using Apriori algorithm and linear model-based recursive partitioning and demonstrates the existence of opposite trends in relation to age and sex based subgroups that would be impossible to discover using traditional trend-tracking techniques.
Journal ArticleDOI
Personality biomarkers of pathological gambling: A machine learning study.
Antonio Cerasa,Danilo Lofaro,Paolo Cavedini,Iolanda Martino,Antonella Bruni,Alessia Sarica,Domenico Mauro,Giuseppe Merante,Ilaria Rossomanno,Maria Rizzuto,Antonio Palmacci,Benedetta Aquino,Pasquale De Fazio,Giampaolo Perna,Giampaolo Perna,Elena Vanni,Giuseppe Olivadese,Domenico Conforti,Gennarina Arabia,Aldo Quattrone,Aldo Quattrone +20 more
TL;DR: This is the first study that combines behavioral data with machine learning approach useful to extract multidimensional features characterizing GD realm and provides a proof-of-concept demonstrating the potential of the proposed approach for GD diagnosis.
Journal ArticleDOI
Identification of subgroups by risk of graft failure after paediatric renal transplantation: application of survival tree models on the ESPN/ERA-EDTA Registry
Danilo Lofaro,Kitty J. Jager,Ameen Abu-Hanna,Jaap W. Groothoff,Pekka Arikoski,Britta Hoecker,Gwenaëlle Roussey-Kesler,Brankica Spasojevic,Enrico Verrina,Franz Schaefer,Franz Schaefer,Karlijn J. van Stralen +11 more
TL;DR: The tree model was demonstrated to be an accurate and attractive tool to predict graft failure for patients with specific characteristics and may aid the evaluation of individual graft prognosis and thereby the design of measures to improve graft survival in the poor prognosis groups.
References
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A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study
TL;DR: The SAPS II, based on a large international sample of patients, provides an estimate of the risk of death without having to specify a primary diagnosis, and is a starting point for future evaluation of the efficiency of intensive care units.
Journal ArticleDOI
Bump hunting in high-dimensional data
TL;DR: This paper presents a procedure directed towards this goal based on the notion of “patient” rule induction, which is contrasted with the greedy ones used by most rule induction methods, and semi-greedy Ones used by some partitioning tree techniques such as CART.
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Thinking Inside the Box: A Participatory, Computer-Assisted Approach to Scenario Discovery
TL;DR: How scenario discovery appears to address several outstanding challenges faced when applying traditional scenario approaches in contentious public debates is described, and how this approach has already proved successful in several high impact policy studies is demonstrated.
Journal Article
Subgroup Discovery with CN2-SD
TL;DR: A subgroup discovery algorithm, CN2-SD, developed by modifying parts of the CN2 classification rule learner: its covering algorithm, search heuristic, probabilistic classification of instances, and evaluation measures, shows substantial reduction of the number of induced rules, increased rule coverage and rule significance, as well as slight improvements in terms of the area under ROC curve.
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Intensive care unit length of stay: Benchmarking based on Acute Physiology and Chronic Health Evaluation (APACHE) IV.
TL;DR: The APACHE IV model provides clinically useful ICU length of stay predictions for critically ill patient groups, but its accuracy and utility are limited for individual patients.