Healthc Inform Res.  2020 Jan;26(1):20-33. 10.4258/hir.2020.26.1.20.

Prediction of Chronic Disease-Related Inpatient Prolonged Length of Stay Using Machine Learning Algorithms

• Symum H
• Zayas-Castro JL

• KMID: 2469397
• DOI: http://doi.org/10.4258/hir.2020.26.1.20

Abstract

OBJECTIVES
The study aimed to develop and compare predictive models based on supervised machine learning algorithms for predicting the prolonged length of stay (LOS) of hospitalized patients diagnosed with five different chronic conditions.
METHODS
An administrative claim dataset (2008-2012) of a regional network of nine hospitals in the Tampa Bay area, Florida, USA, was used to develop the prediction models. Features were extracted from the dataset using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes. Five learning algorithms, namely, decision tree C5.0, linear support vector machine (LSVM), k-nearest neighbors, random forest, and multi-layered artificial neural networks, were used to build the model with semi-supervised anomaly detection and two feature selection methods. Issues with the unbalanced nature of the dataset were resolved using the Synthetic Minority Over-sampling Technique (SMOTE).
RESULTS
LSVM with wrapper feature selection performed moderately well for all patient cohorts. Using SMOTE to counter data imbalances triggered a tradeoff between the model's sensitivity and specificity, which can be masked under a similar area under the curve. The proposed aggregate rank selection approach resulted in a balanced performing model compared to other criteria. Finally, factors such as comorbidity conditions, source of admission, and payer types were associated with the increased risk of a prolonged LOS.
CONCLUSIONS
Prolonged LOS is mostly associated with pre-intraoperative clinical and patient socioeconomic factors. Accurate patient identification with the risk of prolonged LOS using the selected model can provide hospitals a better tool for planning early discharge and resource allocation, thus reducing avoidable hospitalization costs.

Keyword

Length of Stay; Chronic Disease; Inpatients; Machine Learning; Discharge Planning

MeSH Terms

Bays
Chronic Disease
Cohort Studies
Comorbidity
Dataset
Decision Trees
Florida
Forests
Hospitalization
Humans
Inpatients*
International Classification of Diseases
Learning
Length of Stay*
Machine Learning*
Masks
Patient Discharge
Resource Allocation
Sensitivity and Specificity
Socioeconomic Factors
Supervised Machine Learning
Support Vector Machine

Figure

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  Figure 1 Data preprocessing steps for building predictive model. SVM: support vector machine.

• 

  Figure 2 Flowchart of the predictive model building and best performing model selection. CQ: chi-square feature selection, WR: support vector machinebased wrapper feature selection, AUC: area under the curve, SP: specificity, SN: sensitivity, SMOTE: Synthetic Minority Over-sampling Technique.

• 

  Figure 3 Performance metric changes (%) with and without SMOTE balancing. AMI: acute myocardial infarction, CHF: congestive heart failure, COPD: chronic obstructive pulmonary disease, DB: type 2 diabetes, PN: pneumonia, SP: specificity, SN: sensitivity, KNN: k-nearest neighbor, LSVM: linear support vector machine, RF: random forest, NN: multi-layer neural network, SMOTE: Synthetic Minority Over-sampling Technique.

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