Inpatient admissions in different wards/clinics between 1998 and 2014 are considered. A Bayesian network (BN) structure is estimated, directly, from data in order to compute the joint probabilities of the different patient profiles as one of the main objective is to identify the most probable configurations of the wards/clinics. Knowing which wards/clinics are more interrelated could be useful for a better organization of the hospital. Once the Bayesian network is estimated, evidence for some nodes (in our case the history of a patient up to a certain stage) can be propagated through the graph, and the BN shows how such evidence changes the marginal distributions of the remaining nodes. Therefore, it is possible to predict in which ward/clinic there will be a next admission, assuming that there is one. Cross-validation are also performed to test the predictive ability of the BN.
Bayesian networks for the analysis of inpatient admissions / Conigliani, Caterina; Petitti, Tommasangelo; Vitale, Vincenzina. - (2016). (Intervento presentato al convegno 9th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (CMStatistics 2016) tenutosi a Higher Technical School of Engineering, University of Seville, Spain).
Bayesian networks for the analysis of inpatient admissions
Conigliani Caterina;Vitale Vincenzina
2016
Abstract
Inpatient admissions in different wards/clinics between 1998 and 2014 are considered. A Bayesian network (BN) structure is estimated, directly, from data in order to compute the joint probabilities of the different patient profiles as one of the main objective is to identify the most probable configurations of the wards/clinics. Knowing which wards/clinics are more interrelated could be useful for a better organization of the hospital. Once the Bayesian network is estimated, evidence for some nodes (in our case the history of a patient up to a certain stage) can be propagated through the graph, and the BN shows how such evidence changes the marginal distributions of the remaining nodes. Therefore, it is possible to predict in which ward/clinic there will be a next admission, assuming that there is one. Cross-validation are also performed to test the predictive ability of the BN.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
