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Connecting Patients with Pre-diagnosis: A Multiple Graph Regularized Method for Mental Disorder Diagnosis
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
The distress analysis interview corpus of human and computer interviews
Gratch, J., Artstein, R., Lucas, G., Stratou, G., Scherer, S., Nazarian, A., Wood, R., Boberg, J., DeVault, D., Marsella, S., et al. (2014) · Proceedings of the Language Resources and Evaluation Conference · doi:10.63317/3o7bccg9xequ · Cited by 599
Research works printed but not linked: 24
- Bandini, A., Green, J.R., Taati, B., Orlandi, S., Zinman, L., Yunusova, Y.: Automatic detection of amyotrophic lateral sclerosis (ALS) from video-based analysis of facial movements: speech and non-speech tasks. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 150–157. IEEE (2018) · DOI
- Dinkel, H., Wu, M., Yu, K.: Text-based depression detection on sparse data. arXiv e-prints pp. arXiv-1904 (2019)
- Du, Z., Li, W., Huang, D., Wang, Y.: Encoding visual behaviors with attentive temporal convolution for depression prediction. In: 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), pp. 1–7. IEEE (2019) · DOI
- Feng, F., Wu, Y., Wu, Y., Nie, G., Ni, R.: The effect of artificial neural network model combined with six tumor markers in auxiliary diagnosis of lung cancer. J. Med. Syst. 36(5), 2973–2980 (2012) · DOI
- Giger, M.L.: Computer-aided diagnosis of breast lesions in medical images. Comput. Sci. Eng. 2(5), 39–45 (2000) · DOI
- Goyette, C.H., Conners, C.K., Ulrich, R.F.: Normative data on revised conners parent and teacher rating scales. J. Abnorm. Child Psychol. 6(2), 221–236 (1978) · DOI
- Hamm, J., Kohler, C.G., Gur, R.C., Verma, R.: Automated facial action coding system for dynamic analysis of facial expressions in neuropsychiatric disorders. J. Neurosci. Methods 200(2), 237–256 (2011) · DOI
- Haque, A., Guo, M., Miner, A.S., Fei-Fei, L.: Measuring depression symptom severity from spoken language and 3d facial expressions. arXiv preprint arXiv:1811.08592 (2018) · arXiv
- Jaiswal, S., Valstar, M.F., Gillott, A., Daley, D.: Automatic detection of ADHD and ASD from expressive behaviour in RGBD data. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 762–769. IEEE (2017) · DOI
- Kaushal, C., Bhat, S., Koundal, D., Singla, A.: Recent trends in computer assisted diagnosis (cad) system for breast cancer diagnosis using histopathological images. IRBM 40(4), 211–227 (2019) · DOI
- Kroenke, K., Strine, T.W., Spitzer, R.L., Williams, J.B., Berry, J.T., Mokdad, A.H.: The PHQ-8 as a measure of current depression in the general population. J. Affect. Disord. 114(1–3), 163–173 (2009) · DOI · Paper page
- Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: a unified approach to action segmentation. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 47–54. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49409-8_7 · DOI
- Liang, X., Zhang, Y., Wang, J., Ye, Q., Liu, Y., Tong, J.: Diagnosis of Covid-19 pneumonia based on graph convolutional network. Front. Med. 7 (2020) · DOI
- Liu, P., et al.: HKDP: a hybrid knowledge graph based pediatric disease prediction system. In: Xing, C., Zhang, Y., Liang, Y. (eds.) ICSH 2016. LNCS, vol. 10219, pp. 78–90. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59858-1_8 · DOI
- Mumtaz, W., Xia, L., Ali, S.S.A., Yasin, M.A.M., Hussain, M., Malik, A.S.: Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD). Biomed. Signal Process. Control 31, 108–115 (2017) · DOI
- Muñoz-Organero, M., Powell, L., Heller, B., Harpin, V., Parker, J.: Using recurrent neural networks to compare movement patterns in ADHD and normally developing children based on acceleration signals from the wrist and ankle. Sensors 19(13), 2935 (2019) · DOI
- Stoitsis, J., Valavanis, I., Mougiakakou, S.G., Golemati, S., Nikita, A., Nikita, K.S.: Computer aided diagnosis based on medical image processing and artificial intelligence methods. Nucl. Instrum. Methods Phys. Res., Sect. A 569(2), 591–595 (2006) · DOI
- Sun, R., et al.: Multi-modal knowledge graphs for recommender systems. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1405–1414 (2020) · DOI
- Swanson, J.M., et al.: Clinical relevance of the primary findings of the MTA: success rates based on severity of ADHD and odd symptoms at the end of treatment. J. Am. Acad. Child Adoles. Psychiatry 40(2), 168–179 (2001) · DOI · Paper page
- Vyas, K., et al.: Recognition of atypical behavior in autism diagnosis from video using pose estimation over time. In: 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6. IEEE (2019) · DOI
- Zhang, C., Song, D., Huang, C., Swami, A., Chawla, N.V.: Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 793–803 (2019) · DOI · Paper page
- Zhang, Y., Kong, M., Zhao, T., Hong, W., Zhu, Q., Wu, F.: ADHD intelligent auxiliary diagnosis system based on multimodal information fusion. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 4494–4496 (2020) · DOI
- Zhao, M., Chan, R.H., Chow, T.W., Tang, P.: Compact graph based semi-supervised learning for medical diagnosis in Alzheimer’s disease. IEEE Signal Process. Lett. 21(10), 1192–1196 (2014) · DOI
- Zhao, Z., Bao, Z., Zhang, Z., Cummins, N., Wang, H., Schuller, B.: Hierarchical attention transfer networks for depression assessment from speech. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7159–7163. IEEE (2020) · DOI
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Sorted by citations
Schouten, D., Nicoletti, G., Dille, B., Chia, C., Vendittelli, P., Schuurmans, M., Litjens, G., & Khalili, N. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103621 · Cited by 162
Metadata
- Authors
- Zhao, T., Kong, M., Kuang, K., Huang, Z., Zhu, Q., & Wu, F.
- Affiliations
- Zhejiang University (CN)
- Shanghai Artificial Intelligence Laboratory (CN)
- Corresponding author
- Kuang, Kun
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2022
- Pages
- 362-374
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4313350104
- Cited by
- 1
- References
- 2
- Citations by year
- 2025: 1
- FWCI
- 0.46
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Health Sciences › Medicine › Psychiatry and Mental health › Attention Deficit Hyperactivity Disorder
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- computer-aided diagnosis, psychiatric diagnosis, multimodal representation learning, attention deficit hyperactivity disorder, autism spectrum disorder, children
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- Zhao, T., Kong, M., Kuang, K., Huang, Z., Zhu, Q., & Wu, F. (2022). Connecting Patients with Pre-diagnosis: A Multiple Graph Regularized Method for Mental Disorder Diagnosis. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-031-20500-2_30