Differential dementia diagnosis on incomplete data with latent trees
Ledig C, Kaltwang S, Tolonen A, Koikkalainen J, Scheltens P, Barkhof F, Rhodius-Meester H, Tijms B, Lemstra AW, van der Flier W, Lötjönen J, Rueckert D (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: Springer Verlag
Book Volume: 9901 LNCS
Pages Range: 44-52
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN: 9783319467221
DOI: 10.1007/978-3-319-46723-8_6
Abstract
Incomplete patient data is a substantial problem that is not sufficiently addressed in current clinical research. Many published methods assume both completeness and validity of study data. However,this assumption is often violated as individual features might be unavailable due to missing patient examination or distorted/wrong due to inaccurate measurements or human error. In this work we propose to use the Latent Tree (LT) generative model to address current limitations due to missing data. We show on 491 subjects of a challenging dementia dataset that LT feature estimation is more robust towards incomplete data as compared to mean or Gaussian Mixture Model imputation and has a synergistic effect when combined with common classifiers (we use SVM as example). We show that LTs allow the inclusion of incomplete samples into classifier training. Using LTs,we obtain a balanced accuracy of 62% for the classification of all patients into five distinct dementia types even though 20% of the features are missing in both training and testing data (68% on complete data). Further,we confirm the potential of LTs to detect outlier samples within the dataset.
Involved external institutions
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
VTT Technical Research Centre of Finland
Finland (FI)
Combinostics Oy
Finland (FI)
Amsterdam University Medical Centers (Amsterdam UMC) / Amsterdam Universitair Medische Centra
Netherlands (NL)
United Kingdom (GB)
VTT Technical Research Centre of Finland
Finland (FI)
Combinostics Oy
Finland (FI)
Amsterdam University Medical Centers (Amsterdam UMC) / Amsterdam Universitair Medische Centra
Netherlands (NL)
How to cite
APA:
Ledig, C., Kaltwang, S., Tolonen, A., Koikkalainen, J., Scheltens, P., Barkhof, F.,... Rueckert, D. (2016). Differential dementia diagnosis on incomplete data with latent trees. In Gozde Unal, Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 44-52). Springer Verlag.
MLA:
Ledig, Christian, et al. "Differential dementia diagnosis on incomplete data with latent trees." Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Ed. Gozde Unal, Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells, Springer Verlag, 2016. 44-52.
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