Qiuling Suo
ABSTRACT
Accurate rendering of diagnosis and prognosis for a disease with respect to a patient requires analysis of complicated, diverse, yet correlated risk factors (RFs). Most of the existing methods for this purpose are based on handcraft RFs by calculating their statistical significance to the disease. However, such methods not only incur intensive labor but also lack capability to discover or infer previously unknown complex relationships and combined effects among correlated RFs.
Nowadays, deep learning models have emerged as a hot topic, due to its ability to automatically extract useful and complex features from raw data. In this paper, we explore the effectiveness of deep learning on medical data by building a deep learning based framework to analyze risk factors and study its prediction performance in disease diagnosis. Specifically, we investigate the application of deep learning with a special focus on interpreting the latent features extracted or created from raw data by the model. Experimental results demonstrate that deep learning based methods are able to aggregate features sharing same characteristics, and reduce effects from unimportant and uncorrelated RFs. The abstract features obtained by deep learning methods can represent the essentials of raw inputs, and give a good prediction performance in disease diagnosis.
- http://www.sof.ucsf.edu/interface/.Google Scholar
- http://www.shef.ac.uk/FRAX/tool.jsp/.Google Scholar
- Y. Anzai. Pattern Recognition and Machine Learning. 2012. Google ScholarDigital Library
- R. Bender. Cancer Epidemiology. 2009.Google Scholar
- Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, et al. Greedy layer-wise training of deep networks. Advances in neural information processing systems, 2007. Google ScholarDigital Library
- J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, and Y. Bengio. Theano: a cpu and gpu math expression compiler. In Proceedings of the Python for scientific computing conference (SciPy), 2010.Google ScholarCross Ref
- Z. Che, D. Kale, W. Li, M. T. Bahadori, and Y. Liu. Deep computational phenotyping. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015. Google ScholarDigital Library
- C. Cooper, E. J. Atkinson, H. W. Wahner, W. M. O'Fallon, B. L. Riggs, H. L. Judd, and L. J. Melton. Is caffeine consumption a risk factor for osteoporosis? Journal of Bone and Mineral Research, 1992.Google Scholar
- S. R. Cummings, M. C. Nevitt, W. S. Browner, K. Stone, K. M. Fox, K. E. Ensrud, J. Cauley, D. Black, and T. M. Vogt. Risk factors for hip fracture in white women. New England journal of medicine, 1995.Google Scholar
- D. Erhan, Y. Bengio, A. Courville, and P. Vincent. Visualizing higher-layer features of a deep network. University of Montreal, 2009.Google Scholar
- G. E. Hinton. Deep belief networks. Scholarpedia, 2009.Google ScholarCross Ref
- G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 2006.Google Scholar
- T. A. Lasko, J. C. Denny, and M. A. Levy. Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. PloS one, 2013.Google Scholar
- Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 2015.Google Scholar
- H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th Annual International Conference on Machine Learning, 2009. Google ScholarDigital Library
- H. Li, X. Li, M. Ramanathan, and A. Zhang. Identifying informative risk factors and predicting bone disease progression via deep belief networks. Methods, 2014.Google Scholar
- H. Z. Lo and W. Ding. Understanding deep networks with gradients. neural networks, page 5.Google Scholar
- L. Lusa and R. Blagus. The class-imbalance problem for high-dimensional class prediction. In Machine Learning and Applications (ICMLA), 2012 11th International Conference on, 2012. Google ScholarDigital Library
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al. Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 2011. Google ScholarDigital Library
- J. Robbins, A. Schott, P. Garnero, P. Delmas, D. Hans, and P. Meunier. Risk factors for hip fracture in women with high bmd: Epidos study. Osteoporosis international, 2005.Google ScholarCross Ref
- K. Simonyan, A. Vedaldi, and A. Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034, 2013.Google Scholar
- P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, 2008. Google ScholarDigital Library
- M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In Computer vision--ECCV 2014. 2014.Google ScholarCross Ref
- J. Zhou, J. Sun, Y. Liu, J. Hu, and J. Ye. Patient risk prediction model via top-k stability selection. In In Proceedings of the 13th SIAM International Conference on Data Mining, 2013.Google ScholarCross Ref
Index Terms
- Risk Factor Analysis Based on Deep Learning Models
Recommendations
Prediction and informative risk factor selection of bone diseases
With the booming of healthcare industry and the overwhelming amount of electronic health records (EHRs) shared by healthcare institutions and practitioners, we take advantage of EHR data to develop an effective disease risk management model that not ...
A Semi-Supervised Learning Approach to Integrated Salient Risk Features for Bone Diseases
BCB'13: Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical InformaticsThe study of the risk factor analysis and prediction for diseases requires the understanding of the complicated and highly correlated relationships behind numerous potential risk factors (RFs). The existing models for this purpose usually fix a small ...
Comparative analysis of deep learning models for dysarthric speech detection
AbstractDysarthria is a speech communication disorder that is associated with neurological impairments. To detect this disorder from speech, we present an experimental comparison of deep models developed based on frequency domain features. A comparative ...
Comments