Ensemble Risk Model of Emergency Admissions (ERMER)

Abstract

Introduction: About half of hospital readmissions can be avoided with preventive interventions.Developing decision support tools for identification of patients' emergency readmission risk is an important area of research. Because, it remains unclear how to design features and develop predictive models that can adjust continuously to a fast-changing healthcare system and population characteristics. The objective of this study was to develop a generic ensemble Bayesian risk model of emergency readmission. Methods:We produced a decision support tool that predicts risk of emergency readmission using 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 approaches, and it was robust and stable with high precision. Moreover, the framework and the Bayesian model allow the model to continuously adjust it to new significant features, different population characteristics and changes in the system.

Analysing repeated hospital readmissions using data mining techniques

“…Defining π i = P(y i = 1) and 1 − π i = P(y i = 0), we have (1) to predict the emergency readmission to NHS hospitals (Mesgarpour, Chaussalet, & Chahed, 2017). They chose BPM, since it is not prone to overfitting, highly efficient in approximating the Bayesian average classifier (Mesgarpour et al, 2017) improving their AUC from their previous work (Mesgarpour et al, 2016) reported above to 77.1%.…”

Analysing repeated hospital readmissions using data mining techniques

“…to predict the emergency readmission to NHS hospitals (Mesgarpour, Chaussalet, & Chahed, 2017). They chose BPM, since it is not prone to overfitting, highly efficient in approximating the Bayesian average classifier (Mesgarpour et al, 2017) improving their AUC from their previous work (Mesgarpour et al, 2016) reported above to 77.1%.…”

Temporal Comorbidity-Adjusted Risk of Emergency Readmission (T-CARER): A tool for comorbidity risk assessment

“…Also, in this research, no condition was imposed on the admission type at the trigger event, and a minimal number of raw features were used. This makes it different from general readmission models, such as the ERMER [28], that use a wide range of raw features and may enforce the emergency admission condition for both trigger events and future events.…”

“…In the machine learning pipeline developed in our previous study [28], the comorbidity index is a significant factor with high potential for further improvement. Moreover, little research has been conducted on temporal comorbidity risk scores [29], and the majority of temporal models [30] in the literature have focused on survival analysis in comorbidity indices.…”