Predicting the probability of finding missing older adults based on machine learning
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2432-2725
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Predicting the probability of finding missing older adults based on machine learning ; day:3 ; month:6 ; year:2022 ; pages:1-19
Journal of computational social science ; (3.6.2022), 1-19
- Creator
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1007/s42001-022-00171-x
- URN
-
urn:nbn:de:101:1-2022080222373460101621
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:27 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Ruiz-Rizzo, Adriana L.
- Archila-Meléndez, Mario E.
- González Veloza, José John Fredy
- SpringerLink (Online service)
Other Objects (12)
Predicting the probability of finding missing older adults based on machine learning
Predicting Poverty with Missing Incomes
Finding invisible quantitative trait loci with missing data
Inverse probability weighted estimation for general missing data problems
CareSearch: finding and evaluating Australia's missing palliative care literature
Finding Substructures of Molecules for Predicting Biological Activity
Finding the missing many: improving Tuberculosis care in Kajiado county through active case finding and community approaches
Finding Nemo: Predicting movie performances by machine learning methods
Finding the missing honey bee genes: lessons learned from a genome upgrade
Probability-based collaborative filtering model for predicting gene–disease associations
A hydroclimatological approach to predicting regional landslide probability using Landlab
Conditional co-occurrence probability acts like frequency in predicting fixation durations
Predicting the probability of finding missing older adults based on machine learning
Predicting Poverty with Missing Incomes
Finding invisible quantitative trait loci with missing data
Inverse probability weighted estimation for general missing data problems
CareSearch: finding and evaluating Australia's missing palliative care literature
Finding Substructures of Molecules for Predicting Biological Activity
Finding the missing many: improving Tuberculosis care in Kajiado county through active case finding and community approaches
Finding Nemo: Predicting movie performances by machine learning methods
Finding the missing honey bee genes: lessons learned from a genome upgrade
Probability-based collaborative filtering model for predicting gene–disease associations
A hydroclimatological approach to predicting regional landslide probability using Landlab
Conditional co-occurrence probability acts like frequency in predicting fixation durations
Predicting the probability of finding missing older adults based on machine learning
Predicting Poverty with Missing Incomes
Finding invisible quantitative trait loci with missing data
Inverse probability weighted estimation for general missing data problems
CareSearch: finding and evaluating Australia's missing palliative care literature
Finding Substructures of Molecules for Predicting Biological Activity
Finding the missing many: improving Tuberculosis care in Kajiado county through active case finding and community approaches
Finding Nemo: Predicting movie performances by machine learning methods
Finding the missing honey bee genes: lessons learned from a genome upgrade
Probability-based collaborative filtering model for predicting gene–disease associations
A hydroclimatological approach to predicting regional landslide probability using Landlab