4,587 research outputs found
Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification
Network biology has been successfully used to help reveal complex mechanisms
of disease, especially cancer. On the other hand, network biology requires
in-depth knowledge to construct disease-specific networks, but our current
knowledge is very limited even with the recent advances in human cancer
biology. Deep learning has shown a great potential to address the difficult
situation like this. However, deep learning technologies conventionally use
grid-like structured data, thus application of deep learning technologies to
the classification of human disease subtypes is yet to be explored. Recently,
graph based deep learning techniques have emerged, which becomes an opportunity
to leverage analyses in network biology. In this paper, we proposed a hybrid
model, which integrates two key components 1) graph convolution neural network
(graph CNN) and 2) relation network (RN). We utilize graph CNN as a component
to learn expression patterns of cooperative gene community, and RN as a
component to learn associations between learned patterns. The proposed model is
applied to the PAM50 breast cancer subtype classification task, the standard
breast cancer subtype classification of clinical utility. In experiments of
both subtype classification and patient survival analysis, our proposed method
achieved significantly better performances than existing methods. We believe
that this work is an important starting point to realize the upcoming
personalized medicine.Comment: 8 pages, To be published in proceeding of IJCAI 201
PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data
Parkinson's disease is a neurodegenerative disease that can affect a person's
movement, speech, dexterity, and cognition. Clinicians primarily diagnose
Parkinson's disease by performing a clinical assessment of symptoms. However,
misdiagnoses are common. One factor that contributes to misdiagnoses is that
the symptoms of Parkinson's disease may not be prominent at the time the
clinical assessment is performed. Here, we present a machine-learning approach
towards distinguishing between people with and without Parkinson's disease
using long-term data from smartphone-based walking, voice, tapping and memory
tests. We demonstrate that our attentive deep-learning models achieve
significant improvements in predictive performance over strong baselines (area
under the receiver operating characteristic curve = 0.85) in data from a cohort
of 1853 participants. We also show that our models identify meaningful features
in the input data. Our results confirm that smartphone data collected over
extended periods of time could in the future potentially be used as a digital
biomarker for the diagnosis of Parkinson's disease.Comment: AAAI Conference on Artificial Intelligence 201
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU
Machine learning approaches have been effective in predicting adverse
outcomes in different clinical settings. These models are often developed and
evaluated on datasets with heterogeneous patient populations. However, good
predictive performance on the aggregate population does not imply good
performance for specific groups.
In this work, we present a two-step framework to 1) learn relevant patient
subgroups, and 2) predict an outcome for separate patient populations in a
multi-task framework, where each population is a separate task. We demonstrate
how to discover relevant groups in an unsupervised way with a
sequence-to-sequence autoencoder. We show that using these groups in a
multi-task framework leads to better predictive performance of in-hospital
mortality both across groups and overall. We also highlight the need for more
granular evaluation of performance when dealing with heterogeneous populations.Comment: KDD 201
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