Gerald C Gannod1, Katherine M Abbott2, Kimberly Van Haitsma3,4, Nathan Martindale1, Alexandra Heppner5. 1. Department of Computer Science, Tennessee Technological University, Cookeville. 2. Department of Sociology and Gerontology, Miami University, Oxford, Ohio. 3. College of Nursing, Penn State University, University Park, Pennsylvania. 4. Polisher Research Institute, the Abramson Center for Jewish Life, North Wales, Pennsylvania. 5. Scripps Gerontology Center, Miami University, Oxford, Ohio.
Abstract
Background and Objectives: Nursing homes (NHs) using the Preferences for Everyday Living Inventory (PELI-NH) to assess important preferences and provide person-centered care find the number of items (72) to be a barrier to using the assessment. Research Design and Methods: Using a sample of n = 255 NH resident responses to the PELI-NH, we used the 16 preference items from the MDS 3.0 Section F to develop a machine learning recommender system to identify additional PELI-NH items that may be important to specific residents. Much like the Netflix recommender system, our system is based on the concept of collaborative filtering whereby insights and predictions (e.g., filters) are created using the interests and preferences of many users. The algorithm identifies multiple sets of "you might also like" patterns called association rules, based upon responses to the 16 MDS preferences that recommends an additional set of preferences with a high likelihood of being important to a specific resident. Results: In the evaluation of the combined apriori and logistic regression approach, we obtained a high recall performance (i.e., the ratio of correctly predicted preferences compared with all predicted preferences and nonpreferences) and high precision (i.e., the ratio of correctly predicted rules with respect to the rules predicted to be true) of 80.2% and 79.2%, respectively. Discussion and Implications: The recommender system successfully provides guidance on how to best tailor the preference items asked of residents and can support preference capture in busy clinical environments, contributing to the feasibility of delivering person-centered care.
Background and Objectives: Nursing homes (NHs) using the Preferences for Everyday Living Inventory (PELI-NH) to assess important preferences and provide person-centered care find the number of items (72) to be a barrier to using the assessment. Research Design and Methods: Using a sample of n = 255 NH resident responses to the PELI-NH, we used the 16 preference items from the MDS 3.0 Section F to develop a machine learning recommender system to identify additional PELI-NH items that may be important to specific residents. Much like the Netflix recommender system, our system is based on the concept of collaborative filtering whereby insights and predictions (e.g., filters) are created using the interests and preferences of many users. The algorithm identifies multiple sets of "you might also like" patterns called association rules, based upon responses to the 16 MDS preferences that recommends an additional set of preferences with a high likelihood of being important to a specific resident. Results: In the evaluation of the combined apriori and logistic regression approach, we obtained a high recall performance (i.e., the ratio of correctly predicted preferences compared with all predicted preferences and nonpreferences) and high precision (i.e., the ratio of correctly predicted rules with respect to the rules predicted to be true) of 80.2% and 79.2%, respectively. Discussion and Implications: The recommender system successfully provides guidance on how to best tailor the preference items asked of residents and can support preference capture in busy clinical environments, contributing to the feasibility of delivering person-centered care.
Authors: Richard Schulz; Hans-Werner Wahl; Judith T Matthews; Annette De Vito Dabbs; Scott R Beach; Sara J Czaja Journal: Gerontologist Date: 2014-08-27
Authors: Edelen Maria Orlando; Gage Barbara J; Rose Adam J; Ahluwalia Sangeeta; DeSantis Amy Soo Jin; Dunbar Michael Stephen; Fischer Shira H; Huang Wenjing; Klein David J; Martino Steven; Pillemer Francesca; Piquado Tepring; Shier Victoria; Shih Regina A; Sherbourne Cathy D; Stucky Brian D Journal: Rand Health Q Date: 2018-01-29
Authors: Jaime Lynn Speiser; Kathryn E Callahan; Denise K Houston; Jason Fanning; Thomas M Gill; Jack M Guralnik; Anne B Newman; Marco Pahor; W Jack Rejeski; Michael E Miller Journal: J Gerontol A Biol Sci Med Sci Date: 2021-03-31 Impact factor: 6.053