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Publication details

Document type
Conference papers

Document subtype
Full paper

Title
Towards a reliable prediction of conversion from Mild Cognitive Impairment to Alzheimer’s Disease: stepwise learning using time windows

Participants in the publication
T Pereira (Author)
LASIGE
F Ferreira (Author)
M Guerreiro (Author)
A Mendonca (Author)
Sara C. Madeira (Author)
Dep. Informática
LASIGE

Date of Publication
2017-10-18

Event
Proceedings of The First Workshop Medical Informatics and Healthcare held with the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining

Publication Identifiers

Publisher
Proceedings of Machine Learning Research (PMLR)

Edition
.
Number
69

Starting page
19
Last page
26

Document Identifiers
URL - http://proceedings.mlr.press/v69/pereira17a.html


Export

APA
T Pereira, F Ferreira, M Guerreiro, A Mendonca, Sara C. Madeira, (2017). Towards a reliable prediction of conversion from Mild Cognitive Impairment to Alzheimer’s Disease: stepwise learning using time windows. Proceedings of The First Workshop Medical Informatics and Healthcare held with the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining, 19-26

IEEE
T Pereira, F Ferreira, M Guerreiro, A Mendonca, Sara C. Madeira, "Towards a reliable prediction of conversion from Mild Cognitive Impairment to Alzheimer’s Disease: stepwise learning using time windows" in Proceedings of The First Workshop Medical Informatics and Healthcare held with the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining, , 2017, pp. 19-26, doi:

BIBTEX
@InProceedings{39859, author = {T Pereira and F Ferreira and M Guerreiro and A Mendonca and Sara C. Madeira}, title = {Towards a reliable prediction of conversion from Mild Cognitive Impairment to Alzheimer’s Disease: stepwise learning using time windows}, booktitle = {Proceedings of The First Workshop Medical Informatics and Healthcare held with the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining}, year = 2017, pages = {19-26}, address = {}, publisher = {Proceedings of Machine Learning Research (PMLR)} }