Abstract
Computer-aided diagnosis (CAD) plays an important role in medicine. But most of the previous methods only focus on the diagnosis process information like the image data for medical patterns learning, ignoring the pre-diagnosis, which is necessary and important for a doctor’s decision. Besides, traditional CAD methods treat the patients as independent samples in data. To make up this gap, in this paper, we propose to connect patients with pre-diagnosis and propose a novel Multiple Graph REgularized Diagnosis (MuGRED) method for mental disorder diagnosis, which contains two main components: multi-modal representation learning and a multiple graph feature fusion module. We validated our MuGRED method on a practical dataset of children’s attention deficit and hyperactivity disorder and a well-recognized ASD benchmark. Extensive experiments demonstrate that our MuGRED method can achieve a better performance than the state-of-the-art methods for mental disorder diagnosis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
Giger, M.L.: Computer-aided diagnosis of breast lesions in medical images. Comput. Sci. Eng. 2(5), 39–45 (2000)
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)
Gratch, J., et al.: The distress analysis interview corpus of human and computer interviews. In: LREC, pp. 3123–3128 (2014)
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)
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)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
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)
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)
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)
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
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)
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
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Acknowledgement
This work was supported in part by Program of Zhejiang Province Science and Technology (2022C01044), National Natural Science Foundation of China (U20A20387), the Fundamental Research Funds for the Central Universities (226-2022-00142, 226-2022-00051), Project by Shanghai AI Laboratory (P22KS00111), and the Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study (SN-ZJU-SIAS-0010).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13605. Springer, Cham. https://doi.org/10.1007/978-3-031-20500-2_30
Download citation
DOI: https://doi.org/10.1007/978-3-031-20500-2_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20499-9
Online ISBN: 978-3-031-20500-2
eBook Packages: Computer ScienceComputer Science (R0)