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Tesi etd-04092018-144359


Thesis type
Tesi di laurea magistrale
Author
PANICACCI, SILVIA
URN
etd-04092018-144359
Title
Population Health Management exploiting Machine Learning to identify high-risk patients
Struttura
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
EMBEDDED COMPUTING SYSTEMS
Supervisors
relatore Fanucci, Luca
relatore Francesconi, Paolo
relatore Profili, Francesco
Parole chiave
  • population health management
  • machine learning
  • decision support system
Data inizio appello
07/05/2018;
Consultabilità
Parziale
Data di rilascio
07/05/2021
Riassunto analitico
Population aging and the increase of chronic conditions incidence and prevalence
produce a higher risk of hospitalization or death. This is particularly high for
patients with multimorbidity, who usually receive ineffective and inefficient
treatments, leading to a great consumption of resources. Identifying as soon as
possible high-risk patients becomes an important challenge to improve health care
service provision and to reduce costs. Nowadays, population health management,
intended as the risk assessment process for de ning patients cohorts and stratifying
members by the risk of preventable hospitalizations or death, can be used to identify
these "complex" patients. Thanks to the growing computational power, it can
exploit machine learning algorithms, intelligent models which learn from experience
and have the capability of analysing huge amount of data. The aim of this study is
to validate machine learning algorithms (Naive Bayes, CART, C5.0, Conditional
Inference Tree, Random Forest, Arti cial Neural Network, LASSO and some
combinations of them) to predict the risk of hospitalization or death and to identify
high-risk patients, starting from administrative and/or socio-economic data on the
residents in the Local Health Unit of Central Tuscany. The high sensitivity, always
greater than 70%, points out that a great part of the target patients is correctly
identi ed by the models, leading to the improving of the quality of their lives, and
that not many chronic patients having really need of speci c treatments are
misclassi ed. The models, at last, have a very good discriminatory power,
outperforming the methods currently used in Tuscany for the identi cation of
high-risk patients (6 vs 40 for the Positive Predictive Ratio metric, which underlines
the goodness of the new classi ers). Because of these considerations, the models
could be really employed for the selection of the complex patients, to support the
general practitioners' decisions and to reduce the risk of hospitalization or death.
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