Abstract
Background
Frail elderly are at high risk of hospitalizations and have a complex pattern of risk factors that makes it hard to foresee potential needs for additional treatment and care. Machine learning algorithms are potentially well-suited to discover hidden patterns in registrations that are routinely made across sectors.
Objective
To investigate predictors and performance of machine learning algorithms designed to predict acute hospitalizations in elderly recipients of home care services.
Materials and methods
A development study based on a retrospective social sector cohort with 1,282 participants was designed. Included subjects were aged 65 or older and received home care services in Aalborg Municipality at least once a week from 1/1–2016 to 31/12–2017. Data were collected from a newly developed triage tool in combination with administrative and clinical data routinely collected in the Danish healthcare and social care sector. 857 predictors were tested and evaluated based on the area under the precision-recall curve (PR-AUC). The data was divided into a 70/30 training and test split with 5-fold cross-validation. A sliding window approach combining random under-sampling with a boosting algorithm (RUSBoost) was applied with a standard logistic regression included for comparison.
Results
The logistic regression achieved a PR-AUC of 0.01 (CI 0.00; 0.01) while the PR-AUC was 0.71 (CI 0.56; 0.76) for the RUSBoost algorithm. Four of the five most important citizen-level features used to accurately predict an acute hospitalization was the total number of services provided by the municipality to the citizen, the number of personal care registrations as well as number of medication handlings and nutritional registrations. A final important predictor was the number of physical complaints derived from the triage tool.
Conclusions
Municipalities routinely collect valuable administrative and clinical data that can be used for early prediction of acute hospitalizations. However, future studies are needed to validate the results.
Frail elderly are at high risk of hospitalizations and have a complex pattern of risk factors that makes it hard to foresee potential needs for additional treatment and care. Machine learning algorithms are potentially well-suited to discover hidden patterns in registrations that are routinely made across sectors.
Objective
To investigate predictors and performance of machine learning algorithms designed to predict acute hospitalizations in elderly recipients of home care services.
Materials and methods
A development study based on a retrospective social sector cohort with 1,282 participants was designed. Included subjects were aged 65 or older and received home care services in Aalborg Municipality at least once a week from 1/1–2016 to 31/12–2017. Data were collected from a newly developed triage tool in combination with administrative and clinical data routinely collected in the Danish healthcare and social care sector. 857 predictors were tested and evaluated based on the area under the precision-recall curve (PR-AUC). The data was divided into a 70/30 training and test split with 5-fold cross-validation. A sliding window approach combining random under-sampling with a boosting algorithm (RUSBoost) was applied with a standard logistic regression included for comparison.
Results
The logistic regression achieved a PR-AUC of 0.01 (CI 0.00; 0.01) while the PR-AUC was 0.71 (CI 0.56; 0.76) for the RUSBoost algorithm. Four of the five most important citizen-level features used to accurately predict an acute hospitalization was the total number of services provided by the municipality to the citizen, the number of personal care registrations as well as number of medication handlings and nutritional registrations. A final important predictor was the number of physical complaints derived from the triage tool.
Conclusions
Municipalities routinely collect valuable administrative and clinical data that can be used for early prediction of acute hospitalizations. However, future studies are needed to validate the results.
| Originalsprog | Engelsk |
|---|---|
| Artikelnummer | 104715 |
| Tidsskrift | International Journal of Medical Informatics |
| Vol/bind | 160 |
| ISSN | 1386-5056 |
| DOI | |
| Status | Udgivet - apr. 2022 |