Abstract
Data mining models for high-cost patient encounter prediction at the point-of-admission (HPEPP) in inpatient wards are scarce in literature. This is due to lack of availability of relevant features at such an early stage of treatment. In this study, we create a disease co-occurrence network (DCN) using a subset of the State Inpatient database of Arizona. We explore this network for community formation and structural properties to create new input features for HPEPP models. Tree-based data mining models are trained using input feature sets including these new network features and distinct disease communities in the DCN are identified. We propose community membership and high-cost propensity scores as two network based features for HPEPP modeling. We compare the performance of models with different input feature sets and find that the new features significantly improve the accuracy sensitivity and specificity of prediction models. This model has the potential to improve targeted care management and reduce health care expenditure.
| Original language | English (US) |
|---|---|
| Journal | IEEE Journal of Biomedical and Health Informatics |
| DOIs | |
| State | Accepted/In press - Dec 12 2017 |
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Keywords
- Data models
- Diabetes
- disease co-occurrence networks
- Diseases
- healthcare data analysis
- high cost patients
- Hypertension
- Informatics
- point of admission predictive modeling
- Predictive models
ASJC Scopus subject areas
- Biotechnology
- Computer Science Applications
- Electrical and Electronic Engineering
- Health Information Management
Cite this
Predicting High Cost Patients at Point of Admission using Network Science. / Srinivasan, Karthik; Currim, Faiz; Ram, Sudha.
In: IEEE Journal of Biomedical and Health Informatics, 12.12.2017.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Predicting High Cost Patients at Point of Admission using Network Science
AU - Srinivasan, Karthik
AU - Currim, Faiz
AU - Ram, Sudha
PY - 2017/12/12
Y1 - 2017/12/12
N2 - Data mining models for high-cost patient encounter prediction at the point-of-admission (HPEPP) in inpatient wards are scarce in literature. This is due to lack of availability of relevant features at such an early stage of treatment. In this study, we create a disease co-occurrence network (DCN) using a subset of the State Inpatient database of Arizona. We explore this network for community formation and structural properties to create new input features for HPEPP models. Tree-based data mining models are trained using input feature sets including these new network features and distinct disease communities in the DCN are identified. We propose community membership and high-cost propensity scores as two network based features for HPEPP modeling. We compare the performance of models with different input feature sets and find that the new features significantly improve the accuracy sensitivity and specificity of prediction models. This model has the potential to improve targeted care management and reduce health care expenditure.
AB - Data mining models for high-cost patient encounter prediction at the point-of-admission (HPEPP) in inpatient wards are scarce in literature. This is due to lack of availability of relevant features at such an early stage of treatment. In this study, we create a disease co-occurrence network (DCN) using a subset of the State Inpatient database of Arizona. We explore this network for community formation and structural properties to create new input features for HPEPP models. Tree-based data mining models are trained using input feature sets including these new network features and distinct disease communities in the DCN are identified. We propose community membership and high-cost propensity scores as two network based features for HPEPP modeling. We compare the performance of models with different input feature sets and find that the new features significantly improve the accuracy sensitivity and specificity of prediction models. This model has the potential to improve targeted care management and reduce health care expenditure.
KW - Data models
KW - Diabetes
KW - disease co-occurrence networks
KW - Diseases
KW - healthcare data analysis
KW - high cost patients
KW - Hypertension
KW - Informatics
KW - point of admission predictive modeling
KW - Predictive models
UR - http://www.scopus.com/inward/record.url?scp=85038874422&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85038874422&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2017.2783049
DO - 10.1109/JBHI.2017.2783049
M3 - Article
C2 - 29990022
AN - SCOPUS:85038874422
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
SN - 2168-2194
ER -