The SPECTRA Project: Biomedical Data for Supporting the Detection of Treatment Resistant Schizophrenia
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-60615-1_24
Reference22 articles.
1. Amaro, I., Francese, R., Tortora, G., Tucci, C., D’Errico, L., Staffa, M.: Supporting Schizophrenia PatiEnts’ Care wiTh Robotics and Artificial Intelligence. In: Gao, Q., Zhou, J., Duffy, V.G., Antona, M., Stephanidis, C. (eds.) HCI International 2023–Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol. 14055, pp. 482–495. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-48041-6_32
2. Barone, A., et al.: Disorganization domain as a putative predictor of treatment resistant schizophrenia (TRS) diagnosis: a machine learning approach. J. Psychiatr. Res. 155, 572–578 (2022)
3. Becattini-Oliveira, A.C., de Farias Dutra, D., de Oliveira Campos, B.S., de Araujo, V.C., Charchat-Fichman, H.: A systematic review of a functional assessment tool: UCSD performance-based skill assessment (UPSA). Psychiatry Res. 267, 12–18 (2018)
4. Cantone, A.A., Esposito, M., Perillo, F.P., Romano, M., Sebillo, M., Vitiello, G.: Enhancing elderly health monitoring: achieving autonomous and secure living through the integration of artificial intelligence, autonomous robots, and sensors. Electronics 12(18), 3918 (2023). https://doi.org/10.3390/electronics12183918, https://www.mdpi.com/2079-9292/12/18/3918
5. Chen, L., Wu, M., Zhou, M., Liu, Z., She, J., Hirota, K.: Dynamic emotion understanding in human-robot interaction based on two-layer fuzzy SVR-TS model. IEEE Trans. Syst. Man Cybern. Syst. 50(2), 490–501 (2017)
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