Disease Inference from Health-Related Questions via Sparse Deep Learning

IEEE Transactions on Knowledge and Data Engineering

Volumn 27, Issue 8, 2015, Pages 2107-2119

  Nie, Liqiang ^a   Wang, Meng ^b   Zhang, Luming ^a   Yan, Shuicheng ^a   Zhang, Bo ^c   Chua, Tat Seng ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b HEFEI UNIVERSITY OF TECHNOLOGY   (China)

^c TSINGHUA UNIVERSITY   (China)

Author keywords

Community based Health Services; Deep Learning; Disease Inference; Question Answering

Indexed keywords

SOCIAL NETWORKING (ONLINE);

COMMUNITY-BASED; DEEP ARCHITECTURES; DEEP LEARNING; HEALTH SERVICES; INCOMPLETE INFORMATION; MEDICAL CONCEPTS; PERFORMANCE GAIN; QUESTION ANSWERING;

HEALTH;

EID: 84936934560     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2015.2399298     Document Type: Article

References (51)

1. S. Fox and M. Duggan, "Health online 2013," Pew Research Center, Survey, 2013.
2. "Online health research eclipsing patient-doctor conversations," Makovsky Health and Kelton, Survey, 2013.
3. T. C. Zhou, M. R. Lyu, and I. King, "A classification-based approach to question routing in community question answering," in Proc. 21st Int. World Wide Web Conf., 2012, pp. 783-790.
4. D. A. Davis, N. V. Chawla, N. Blumm, N. Christakis, and A.-L. Barabasi, "Predicting individual disease risk based on medical history," in Proc. 13th Int. Conf. Inf. Knowl. Manage., 2008, pp. 769-778.
5. L. Nie, M. Wang, Z. Zha, G. Li, and T.-S. Chua, "Multimedia answering: Enriching text qa with media information," in Proc. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2011, 695-704.
6. L. Nie, M. Wang, Y. Gao, Z.-J. Zha, and T.-S. Chua, "Beyond text qa: Multimedia answer generation by harvesting web information," IEEE Trans. Multimedia, vol. 15, no. 2, pp. 426-441, Feb. 2013.
7. L. Nie, Y.-L. Zhao, X. Wang, J. Shen, and T.-S. Chua, "Learning to recommend descriptive tags for questions in social forums," ACM Trans. Inf. Syst., vol. 32, no. 1, p. 5, 2014.
8. D. Zhang and W. S. Lee, "Extracting key-substring-group features for text classification," in Proc. 12th ACM SIGKDD Conf. Knowl. Discovery Data Mining, 2006, pp. 474-483.
9. M. Gallé, "The bag-of-repeats representation of documents," in Proc. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2013, pp. 1053-1056.
10. F. Wang, N. Lee, J. Hu, J. Sun, S. Ebadollahi, and A. Laine, "A framework for mining signatures from event sequences and its applications in healthcare data," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 2, pp. 272-285, Feb. 2013.
11. F. Wang, N. Lee, J. Hu, J. Sun, and S. Ebadollahi, "Towards heterogeneous temporal clinical event pattern discovery: A convolutional approach," in Proc. 18th ACM SIGKDD Conf. Knowl. Discovery Data Mining, 2012, pp. 453-461.
12. S. Doan and H. Xu, "Recognizing medication related entities in hospital discharge summaries using support vector machine," in Proc. Int. Conf. Comput. Linguistics, 2010, pp. 259-266.
13. T. D. Wang, C. Plaisant, A. J. Quinn, R. Stanchak, S. Murphy, and B. Shneiderman, "Aligning temporal data by sentinel events: Discovering patterns in electronic health records," in Proc. SIGCHI Conf. Human Factors Comput. Syst., 2008, pp. 457-466.
14. I. Batal, L. Sacchi, R. Bellazzi, and M. Hauskrecht, "A temporal abstraction framework for classifying clinical temporal data," in Proc. Amer. Med. Informat. Assoc., 2008, pp. 29-33.
15. R. Fakoor, F. Ladhak, A. Nazi, and M. Huber, "Using deep learning to enhance cancer diagnosis and classification," presented at the Int. Conf. Mach. Learn., Atlanta, GA, USA, 2013.
16. C. B. Akgül, D. Ünay, and A. Ekin, "Automated diagnosis of Alzheimer's disease using image similarity and user feedback," in Proc. ACM Int. Conf. Image Video Retrieval, 2009, p. 34.
17. M. Shouman, T. Turner, and R. Stocker, "Using decision tree for diagnosing heart disease patients," in Proc. 9th Australasian Data Mining Conf., 2011, pp. 23-30.
18. Y. Zhang and B. Liu, "Semantic text classification of disease reporting," in Proc. 30th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2007, pp. 747-748.
19. J. Zhou, L. Yuan, J. Liu, and J. Ye, "A multi-task learning formulation for predicting disease progression," in Proc. 17th ACM SIGKDD Conf. Knowl. Discovery Data Mining, 2011, pp. 814-822.
20. A. Khosla, Y. Cao, C. C.-Y. Lin, H.-K. Chiu, J. Hu, and H. Lee, "An integrated machine learning approach to stroke prediction," in Proc. 16th ACM SIGKDD Conf. Knowl. Discovery Data Mining, 2010, pp. 183-192.
21. J. Zhou, J. Liu, V. A. Narayan, and J. Ye, "Modeling disease progression via fused sparse group lasso," in Proc. ACM SIGKDD Conf. Knowl. Discovery Data Mining, 2012, pp. 1095-1103.
22. D. Zhu and B. Carterette, "An adaptive evidence weighting method for medical record search," in Proc. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2013, pp. 1025-1028.
23. N. Limsopatham, C. Macdonald, and I. Ounis, "Learning to combine representations for medical records search," in Proc. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2013, pp. 833-836.
24. R. W. White and E. Horvitz, "Studies of the onset and persistence of medical concerns in search logs," in Proc. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2012, pp. 265-274.
25. M.-A. Cartright, R. W. White, and E. Horvitz, "Intentions and attention in exploratory health search," in Proc. 34th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2011, pp. 65-74.
26. B. Koopman, P. Bruza, L. Sitbon, and M. Lawley, "Evaluating medical information retrieval," in Proc. 34th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2011, pp. 1139-1140.
27. X. Huang and Q. Hu, "A Bayesian learning approach to promoting diversity in ranking for biomedical information retrieval," in Proc. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2009, pp. 307-314.
28. G. Luo and C. Tang, "On iterative intelligent medical search," in Proc. 31st Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2008, pp. 3-10.
29. L. Nie, T. Li, M. Akbari, J. Shen, and T.-S. Chua, "Wenzher: Comprehensive vertical search for healthcare domain," in Proc. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2014, pp. 1245-1246.
30. P. Sondhi, J. Sun, H. Tong, and C. Zhai, "Sympgraph: A framework for mining clinical notes through symptom relation graphs," in Proc. 18th ACM SIGKDD Conf. Knowl. Discovery Data Mining, 2012, pp. 1167-1175.
31. S. Ghumbre, C. Patil, and A. Ghatol, "Heart disease diagnosis using support vector machine," in Proc. Int. Conf. Comput. Sci. Inf. Technol., 2011, pp. 84-88.
32. E. M. Voorhees and W. Hersh, "Overview of the the text retrieval conference 2012 medical records track," in Proc. Text REtrieval Conf., 2012, pp. 1-6.
33. S.-h. Yang, S. P. Crain, and H. Zha, "Bridging the language gap: Topic adaptation for documents with different technicality," in Proc. 14th Int. Conf. Artif. Intell. Statist., 2011, pp. 823-831.
34. R. Cline and K. Haynes, "Consumer health information seeking on the internet: The state of the art," Health Educ. Res., vol. 16, no. 6, pp. 671-692, 2001.
35. B. King, L. Wang, I. Provalov, and J. Zhou, "Cengage learning at trec 2011 medical track," in Proc. TREC, 2011, pp. 1-10.
36. L. Nie, M. Akbari, T. Li, and T.-S. Chua, "A joint local-global approach for medical terminology assignment," in Proc. Int. ACM SIGIR Workshop, 2014, pp. 24-27.
37. A. R. Aronson and F.-M. Lang, "An overview of MetaMap: Historical perspective and recent advances," J. Amer. Med. Informat. Assoc., vol. 17, no. 3, pp. 229-236, 2010.
38. L. Nie, Y.-L. Zhao, M. Akbari, J. Shen, and T.-S. Chua, "Bridging the vocabulary gap between health seekers and healthcare knowledge," IEEE Trans. Knowl. Data Eng., vol. 27, no. 2, pp. 396-409, Jun. 2014.
39. L. Nie, S. Yan, M. Wang, R. Hong, and T.-S. Chua, "Harvesting visual concepts for image search with complex queries," in Proc. Int. ACM Multimedia Conf., 2012, pp. 59-68.
40. H. Liu, L. Latecki, and S. Yan, "Fast detection of dense subgraphs with iterative shrinking and expansion," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 9, pp. 2131-2142, Sep. 2013.
41. H. Liu and S. Yan, "Robust graph mode seeking by graph shift," in Proc. 27th Int. Conf. Mach. Learn., 2010, pp. 671-678.
42. K. I. Penny and I. Atkinson, "Approaches for dealing with missing data in health care studies," J. Clinical Nursing, vol. 21, no. 2, pp. 2722-2729, 2012.
43. Y.-L. Zhao, L. Nie, X. Wang, and T.-S. Chua, "Personalized recommendations of locally interesting venues to tourists via cross region community matching," ACM Trans. Intell. Syst. Technol., vol. 5, no. 3, p. 50, 2013.
44. Y. Chen, Z. Li, L. Nie, X. Wang, T.-S. Chua, and X. Zhang, "A semi-supervised Bayesian network model for microblog topic classification," in Proc. 24th Int. Conf. Comput. Linguistics, 2012, pp. 561-576.
45. Y. Bengio, A. Courville, and P. Vincent, "Representation learning: A review and new perspectives," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798-1828, Aug. 2013.
46. H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng, "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations," in Proc. 26th Annual Int. Conf. Machine Learning, 2009, pp. 609-616.
47. A. Krogh and J. A. Hertz, "A simple weight decay can improve generalization," in Proc. Adv. Neural Inf. Process. Syst., 1992, pp. 950-957.
48. D. Erhan, Y. Bengio, A. Courville, P.-A. Manzagol, P. Vincent, and S. Bengio, "Why does unsupervised pre-training help deep learning?" J. Mach. Learn. Res., vol. 11, pp. 625-660, 2010.
49. M. Warrens, "Inequalities between multi-rater kappas," Adv. Data Anal. Classification, vol. 4, no. 4, pp. 271-286, 2010.
50. V. Sindhwani and S. S. Keerthi, "Large scale semi-supervised linear SVMs," in Proc. 29th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2006, pp. 477-484.
51. Y. Bengio, "Learning deep architectures for ai," Found. Trends Mach. Learn., vol. 2, no. 2, pp. 1-127, 2009.