The presence of patients affected by different diseases at the same time is becoming a major health and societal issue. In clinical literature, this phenomenon is known as comorbidity, and it can be studied from the administrative databases of general practitioners’ prescriptions based on diagnoses. In this contribution, we propose a two-step strategy for analyzing comorbidity patterns. In the first step, we investigate the prescription data with association rules extracted by a twomode network (or bipartite graph) to find frequent itemsets that can be used to assist physicians in making diagnoses. In the second step, we derive a one-mode network of the diseases codes with association rules, and we perform the k-core partitioning algorithm to identify the most relevant and connected parts in the network corresponding to the most related pathologies.

Association Rules and Network Analysis for Exploring Comorbidity Patterns in Health Systems

Giuseppe Giordano
Methodology
;
Sergio Pagano
Methodology
;
Maria Prosperina Vitale
Methodology
;
Pierpaolo Cavallo
Methodology
2020

Abstract

The presence of patients affected by different diseases at the same time is becoming a major health and societal issue. In clinical literature, this phenomenon is known as comorbidity, and it can be studied from the administrative databases of general practitioners’ prescriptions based on diagnoses. In this contribution, we propose a two-step strategy for analyzing comorbidity patterns. In the first step, we investigate the prescription data with association rules extracted by a twomode network (or bipartite graph) to find frequent itemsets that can be used to assist physicians in making diagnoses. In the second step, we derive a one-mode network of the diseases codes with association rules, and we perform the k-core partitioning algorithm to identify the most relevant and connected parts in the network corresponding to the most related pathologies.
978-3-030-31462-0
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4732951
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 3
social impact