Formal Approach to Control Design of Complex and Dynamical Systems

Kadri, Hela; Ahmed, Samir Ben; Collart-Dutilleul, Simon

doi:10.1016/j.procs.2017.05.134

Cited by 4 publications

(1 citation statement)

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“…The application of Markov models can be used to calculate the parameter of the failure rate and the probability estimation of the first and second kinds of errors, and the analysis of the causes of system failure remains a topical issue [17]. Thus, data mining techniques are used to solve many problems in the processing and analyzing of medical information.…”

Section: Introductionmentioning

confidence: 99%

Personalized Data Analysis Approach for Assessing Necessary Hospital Bed-Days Built on Condition Space and Hierarchical Predictor

Melnykova

Shakhovska

Melnykov

et al. 2021

BDCC

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The paper describes the medical data personalization problem by determining the individual characteristics needed to predict the number of days a patient spends in a hospital. The mathematical problem of patient information analysis is formalized, which will help identify critical personal characteristics based on conditioned space analysis. The condition space is given in cube form as a reflection of the functional relationship of the general parameters to the studied object. The dataset consists of 51 instances, and ten parameters are processed using different clustering and regression models. Days in hospital is the target variable. A condition space cube is formed based on clustering analysis and features selection. In this manner, a hierarchical predictor based on clustering and an ensemble of weak regressors is built. The quality of the developed hierarchical predictor for Root Mean Squared Error metric is 1.47 times better than the best weak predictor (perceptron with 12 units in a single hidden layer).

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Section: Introductionmentioning

confidence: 99%