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Development and validation of classifiers and variable subsets for predicting nursing home admission

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2017
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2 tweeters

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6 Dimensions

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38 Mendeley
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Title
Development and validation of classifiers and variable subsets for predicting nursing home admission
Published in
BMC Medical Informatics and Decision Making, April 2017
DOI 10.1186/s12911-017-0442-4
Pubmed ID
Authors

Mikko Nuutinen, Riikka-Leena Leskelä, Ella Suojalehto, Anniina Tirronen, Vesa Komssi

Abstract

In previous years a substantial number of studies have identified statistically important predictors of nursing home admission (NHA). However, as far as we know, the analyses have been done at the population-level. No prior research has analysed the prediction accuracy of a NHA model for individuals. This study is an analysis of 3056 longer-term home care customers in the city of Tampere, Finland. Data were collected from the records of social and health service usage and RAI-HC (Resident Assessment Instrument - Home Care) assessment system during January 2011 and September 2015. The aim was to find out the most efficient variable subsets to predict NHA for individuals and validate the accuracy. The variable subsets of predicting NHA were searched by sequential forward selection (SFS) method, a variable ranking metric and the classifiers of logistic regression (LR), support vector machine (SVM) and Gaussian naive Bayes (GNB). The validation of the results was guaranteed using randomly balanced data sets and cross-validation. The primary performance metrics for the classifiers were the prediction accuracy and AUC (average area under the curve). The LR and GNB classifiers achieved 78% accuracy for predicting NHA. The most important variables were RAI MAPLE (Method for Assigning Priority Levels), functional impairment (RAI IADL, Activities of Daily Living), cognitive impairment (RAI CPS, Cognitive Performance Scale), memory disorders (diagnoses G30-G32 and F00-F03) and the use of community-based health-service and prior hospital use (emergency visits and periods of care). The accuracy of the classifier for individuals was high enough to convince the officials of the city of Tampere to integrate the predictive model based on the findings of this study as a part of home care information system. Further work need to be done to evaluate variables that are modifiable and responsive to interventions.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 7 18%
Student > Master 6 16%
Researcher 6 16%
Student > Ph. D. Student 4 11%
Student > Postgraduate 3 8%
Other 5 13%
Unknown 7 18%
Readers by discipline Count As %
Medicine and Dentistry 13 34%
Nursing and Health Professions 7 18%
Social Sciences 2 5%
Computer Science 2 5%
Arts and Humanities 1 3%
Other 6 16%
Unknown 7 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 May 2017.
All research outputs
#5,078,547
of 10,152,716 outputs
Outputs from BMC Medical Informatics and Decision Making
#579
of 1,044 outputs
Outputs of similar age
#115,437
of 264,187 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#15
of 26 outputs
Altmetric has tracked 10,152,716 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,044 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 264,187 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.