Thirty-day hospital readmission prediction model based on common data model with weather and air quality data

AbstractAlthough several studies have attempted to develop a model for predicting 30-day re-hospitalization, few attempts have been made for sufficient verification and multi-center expansion for clinical use. In this study, we developed a model that predicts unplanned hospital readmission within 30 days of discharge; the model is based on a common data model and considers weather and air quality factors, and can be easily extended to multiple hospitals. We developed and compared four tree-based machine learning methods: decision tree, random forest, AdaBoost, and gradient boosting machine (GBM). Above all, GBM showed the highest AUC performance of 75.1 in the clinical model, while the clinical and W-score model showed the best performance of 73.9 for musculoskeletal diseases. Further, PM10, rainfall, and maximum temperature were the weather and air quality variables that most impacted the model. In addition, external validation has confirmed that the model based on weather and air quality factors has transportability to adapt to other hospital systems.

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Thirty-day Hospital Readmission Prediction Model Based on Common Data Model with Weather and Air Quality Data

10.21203/rs.3.rs-598503/v1 ◽

2021 ◽

Author(s):

Borim Ryu ◽

Sooyoung Yoo ◽

Seok Kim ◽

Jinwook Choi

Keyword(s):

Machine Learning ◽

Air Quality ◽

Prediction Model ◽

Data Model ◽

Hospital Readmissions ◽

External Validation ◽

Quality Data ◽

Common Data Model ◽

Quality Factors ◽

Model Based

Abstract Many epidemiological studies have established an association between environmental exposure and clinical outcome for hospital admissions. However, few studies have explored the impact of environmental factors, such as ambient air pollution and meteorological factors, on hospital readmissions using predictive analysis. In this study, we aimed to develop a model to predict unplanned hospital readmissions within 30 days of discharge based on the common data model considering weather and air quality factors. Moreover, we validated the proposed model externally. We developed and compared the following machine learning methods: decision tree, random forest, AdaBoost, and gradient boosting machine–based models. We performed 10-fold cross-validation for internal validation, and external validation was performed by applying the model to unseen data. The performance of the prediction model was evaluated using the area under the receiver operating characteristic curve. PM10, rainfall, and maximum temperature were the weather and air quality variables that most impacted the model. Among the four machine learning models, the AdaBoost-based model demonstrated the best performance and was the most accurate in predicting the readmission of patients with musculoskeletal diseases. External validation demonstrated that the model based on weather and air quality factors is transportable.

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Development of Prediction Models for Unplanned Hospital Readmission within 30 Days Based on Common Data Model: A Feasibility Study

Methods of Information in Medicine ◽

10.1055/s-0041-1735166 ◽

2021 ◽

Author(s):

Sooyoung Yoo ◽

Jinwook Choi ◽

Borim Ryu ◽

Seok Kim

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Data Model ◽

Hospital Readmission ◽

Prediction Models ◽

External Validation ◽

Gradient Boosting ◽

Common Data Model ◽

Time Interval ◽

Machine Learning Classification

Abstract Background Unplanned hospital readmission after discharge reflects low satisfaction and reliability in care and the possibility of potential medical accidents, and is thus indicative of the quality of patient care and the appropriateness of discharge plans. Objectives The purpose of this study was to develop and validate prediction models for all-cause unplanned hospital readmissions within 30 days of discharge, based on a common data model (CDM), which can be applied to multiple institutions for efficient readmission management. Methods Retrospective patient-level prediction models were developed based on clinical data of two tertiary general university hospitals converted into a CDM developed by Observational Medical Outcomes Partnership. Machine learning classification models based on the LASSO logistic regression model, decision tree, AdaBoost, random forest, and gradient boosting machine (GBM) were developed and tested by manipulating a set of CDM variables. An internal 10-fold cross-validation was performed on the target data of the model. To examine its transportability, the model was externally validated. Verification indicators helped evaluate the model performance based on the values of area under the curve (AUC). Results Based on the time interval for outcome prediction, it was confirmed that the prediction model targeting the variables obtained within 30 days of discharge was the most efficient (AUC of 82.75). The external validation showed that the model is transferable, with the combination of various clinical covariates. Above all, the prediction model based on the GBM showed the highest AUC performance of 84.14 ± 0.015 for the Seoul National University Hospital cohort, yielding in 78.33 in external validation. Conclusions This study showed that readmission prediction models developed using machine-learning techniques and CDM can be a useful tool to compare two hospitals in terms of patient-data features.

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An Urban Infrastructure Common Data Model Based on Loose Coupling Topology and Rule Base

2009 First International Conference on Information Science and Engineering ◽

10.1109/icise.2009.297 ◽

2009 ◽

Author(s):

Lei Zhang ◽

Zhijiang Yang

Keyword(s):

Data Model ◽

Urban Infrastructure ◽

Rule Base ◽

Common Data Model ◽

Loose Coupling ◽

Model Based

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Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocy032 ◽

2018 ◽

Vol 25 (8) ◽

pp. 969-975 ◽

Cited By ~ 34

Author(s):

Jenna M Reps ◽

Martijn J Schuemie ◽

Marc A Suchard ◽

Patrick B Ryan ◽

Peter R Rijnbeek

Keyword(s):

Open Source ◽

Observational Data ◽

Open Source Software ◽

Data Model ◽

Prediction Models ◽

External Validation ◽

Model Performance ◽

Common Data Model ◽

Proof Of Concept ◽

Model Framework

Abstract Objective To develop a conceptual prediction model framework containing standardized steps and describe the corresponding open-source software developed to consistently implement the framework across computational environments and observational healthcare databases to enable model sharing and reproducibility. Methods Based on existing best practices we propose a 5 step standardized framework for: (1) transparently defining the problem; (2) selecting suitable datasets; (3) constructing variables from the observational data; (4) learning the predictive model; and (5) validating the model performance. We implemented this framework as open-source software utilizing the Observational Medical Outcomes Partnership Common Data Model to enable convenient sharing of models and reproduction of model evaluation across multiple observational datasets. The software implementation contains default covariates and classifiers but the framework enables customization and extension. Results As a proof-of-concept, demonstrating the transparency and ease of model dissemination using the software, we developed prediction models for 21 different outcomes within a target population of people suffering from depression across 4 observational databases. All 84 models are available in an accessible online repository to be implemented by anyone with access to an observational database in the Common Data Model format. Conclusions The proof-of-concept study illustrates the framework’s ability to develop reproducible models that can be readily shared and offers the potential to perform extensive external validation of models, and improve their likelihood of clinical uptake. In future work the framework will be applied to perform an “all-by-all” prediction analysis to assess the observational data prediction domain across numerous target populations, outcomes and time, and risk settings.

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