{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:39:53Z","timestamp":1771004393458,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031460043","type":"print"},{"value":"9783031460050","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-46005-0_18","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T04:01:36Z","timestamp":1696651296000},"page":"206-217","source":"Crossref","is-referenced-by-count":3,"title":["An Ambient Intelligence-Based Approach for\u00a0Longitudinal Monitoring of\u00a0Verbal and\u00a0Vocal Depression Symptoms"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3442-0578","authenticated-orcid":false,"given":"Alice","family":"Othmani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Muzammel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Borges-J\u00fanior, R., Salvini, R., Nierenberg, A.A., Sachs, G.S., Lafer, B., Dias, R.S.: Forecasting depressive relapse in bipolar disorder from clinical data. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 613\u2013616. IEEE (2018)","DOI":"10.1109\/BIBM.2018.8621255"},{"issue":"8","key":"18_CR2","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1016\/j.cpr.2007.02.005","volume":"27","author":"SL Burcusa","year":"2007","unstructured":"Burcusa, S.L., Iacono, W.G.: Risk for recurrence in depression. Clin. Psychol. Rev. 27(8), 959\u2013985 (2007)","journal-title":"Clin. Psychol. Rev."},{"issue":"1","key":"18_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41398-019-0615-2","volume":"9","author":"M Cearns","year":"2019","unstructured":"Cearns, M., et al.: Predicting rehospitalization within 2 years of initial patient admission for a major depressive episode: a multimodal machine learning approach. Transl. Psychiatry 9(1), 1\u20139 (2019)","journal-title":"Transl. Psychiatry"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Chanda, K., Bhattacharjee, P., Roy, S., Biswas, S.: Intelligent data prognosis of recurrent of depression in medical diagnosis. In: 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pp. 840\u2013844. IEEE (2020)","DOI":"10.1109\/ICRITO48877.2020.9197843"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Chicco, D.: Siamese neural networks: an overview, pp. 73\u201394","DOI":"10.1007\/978-1-0716-0826-5_3"},{"key":"18_CR6","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1146\/annurev-clinpsy-032816-045037","volume":"14","author":"DB Dwyer","year":"2018","unstructured":"Dwyer, D.B., Falkai, P., Koutsouleris, N.: Machine learning approaches for clinical psychology and psychiatry. Annu. Rev. Clin. Psychol. 14, 91\u2013118 (2018)","journal-title":"Annu. Rev. Clin. Psychol."},{"issue":"11","key":"18_CR7","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1111\/cns.13048","volume":"24","author":"S Gao","year":"2018","unstructured":"Gao, S., Calhoun, V.D., Sui, J.: Machine learning in major depression: from classification to treatment outcome prediction. CNS Neurosci. Therapeutics 24(11), 1037\u20131052 (2018)","journal-title":"CNS Neurosci. Therapeutics"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Gemmeke, J.F., et al.: Audio set: an ontology and human-labeled dataset for audio events. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776\u2013780. IEEE (2017)","DOI":"10.1109\/ICASSP.2017.7952261"},{"key":"18_CR9","unstructured":"Gratch, J., et al.: The distress analysis interview corpus of human and computer interviews. In: Proceedings of the International Conference on Language Resources and Evaluation, pp. 3123\u20133128 (2014)"},{"issue":"3","key":"18_CR10","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1111\/j.1600-0447.2009.01519.x","volume":"122","author":"F Hardeveld","year":"2010","unstructured":"Hardeveld, F., Spijker, J., De Graaf, R., Nolen, W., Beekman, A.: Prevalence and predictors of recurrence of major depressive disorder in the adult population. Acta Psychiatr. Scand. 122(3), 184\u2013191 (2010)","journal-title":"Acta Psychiatr. Scand."},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Hershey, S., et al.: CNN architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131\u2013135. IEEE (2017)","DOI":"10.1109\/ICASSP.2017.7952132"},{"key":"18_CR12","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.mbs.2016.10.008","volume":"282","author":"Y Lin","year":"2016","unstructured":"Lin, Y., Huang, S., Simon, G.E., Liu, S.: Analysis of depression trajectory patterns using collaborative learning. Math. Biosci. 282, 191\u2013203 (2016)","journal-title":"Math. Biosci."},{"issue":"1","key":"18_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12974-019-1475-7","volume":"16","author":"CH Liu","year":"2019","unstructured":"Liu, C.H., et al.: Role of inflammation in depression relapse. J. Neuroinflammation 16(1), 1\u201311 (2019)","journal-title":"J. Neuroinflammation"},{"key":"18_CR14","doi-asserted-by":"publisher","unstructured":"Marcus, M., Yasamy, M.T., van\u00a0van Ommeren, M., Chisholm, D., Saxena, S.: Depression: a global public health concern (2012). https:\/\/doi.org\/10.1037\/e517532013-004","DOI":"10.1037\/e517532013-004"},{"key":"18_CR15","unstructured":"Mikolov, T., Grave, E., Bojanowski, P., Puhrsch, C., Joulin, A.: Advances in pre-training distributed word representations. In: Proceedings of the International Conference on Language Resources and Evaluation (2018)"},{"issue":"4","key":"18_CR16","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1037\/a0025190","volume":"118","author":"SM Monroe","year":"2011","unstructured":"Monroe, S.M., Harkness, K.L.: Recurrence in major depression: a conceptual analysis. Psychol. Rev. 118(4), 655 (2011)","journal-title":"Psychol. Rev."},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Muzammel, M., Othmani, A., Mukherjee, H., Salam, H.: Identification of signs of depression relapse using audio-visual cues: a preliminary study. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), pp. 62\u201367. IEEE (2021)","DOI":"10.1109\/CBMS52027.2021.00018"},{"key":"18_CR18","volume":"2","author":"M Muzammel","year":"2020","unstructured":"Muzammel, M., Salam, H., Hoffmann, Y., Chetouani, M., Othmani, A.: Audvowelconsnet: a phoneme-level based deep CNN architecture for clinical depression diagnosis. Mach. Learn. Appl. 2, 100005 (2020)","journal-title":"Mach. Learn. Appl."},{"key":"18_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106433","volume":"211","author":"M Muzammel","year":"2021","unstructured":"Muzammel, M., Salam, H., Othmani, A.: End-to-end multimodal clinical depression recognition using deep neural networks: a comparative analysis. Comput. Methods Programs Biomed. 211, 106433 (2021)","journal-title":"Comput. Methods Programs Biomed."},{"key":"18_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/978-3-030-68790-8_1","volume-title":"Pattern Recognition. ICPR International Workshops and Challenges","author":"A Othmani","year":"2021","unstructured":"Othmani, A., Kadoch, D., Bentounes, K., Rejaibi, E., Alfred, R., Hadid, A.: Towards robust deep neural networks for affect and depression recognition from speech. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12662, pp. 5\u201319. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-68790-8_1"},{"key":"18_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2022.100090","volume":"2","author":"A Othmani","year":"2022","unstructured":"Othmani, A., Zeghina, A.O.: A multimodal computer-aided diagnostic system for depression relapse prediction using audiovisual cues: a proof of concept. Healthc. Anal. 2, 100090 (2022)","journal-title":"Healthc. Anal."},{"key":"18_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107132","volume":"226","author":"A Othmani","year":"2022","unstructured":"Othmani, A., Zeghina, A.O., Muzammel, M.: A model of normality inspired deep learning framework for depression relapse prediction using audiovisual data. Comput. Methods Programs Biomed. 226, 107132 (2022)","journal-title":"Comput. Methods Programs Biomed."},{"key":"18_CR23","unstructured":"Rejaibi, E., Komaty, A., Meriaudeau, F., Agrebi, S., Othmani, A.: MFCC-based recurrent neural network for automatic clinical depression recognition and assessment from speech. arXiv preprint arXiv:1909.07208 (2019)"},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Ringeval, F., et al.: AVEC 2017: real-life depression, and affect recognition workshop and challenge. In: Proceedings of the 7th Annual Workshop on Audio\/Visual Emotion Challenge, pp. 3\u20139. ACM (2017)","DOI":"10.1145\/3133944.3133953"},{"key":"18_CR25","doi-asserted-by":"crossref","unstructured":"Ruhe, H.G., et al.: Emotional biases and recurrence in major depressive disorder. Results of 2.5 years follow-up of drug-free cohort vulnerable for recurrence. Front. Psychiatry 10, 145 (2019)","DOI":"10.3389\/fpsyt.2019.00145"},{"issue":"2","key":"18_CR26","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.pscychresns.2015.07.001","volume":"233","author":"JR Sato","year":"2015","unstructured":"Sato, J.R., Moll, J., Green, S., Deakin, J.F., Thomaz, C.E., Zahn, R.: Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression. Psychiatry Res. Neuroimaging 233(2), 289\u2013291 (2015)","journal-title":"Psychiatry Res. Neuroimaging"},{"issue":"1","key":"18_CR27","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/s10597-020-00728-y","volume":"57","author":"SMA Shah","year":"2021","unstructured":"Shah, S.M.A., Mohammad, D., Qureshi, M.F.H., Abbas, M.Z., Aleem, S.: Prevalence, psychological responses and associated correlates of depression, anxiety and stress in a global population, during the coronavirus disease (COVID-19) pandemic. Community Ment. Health J. 57(1), 101\u2013110 (2021)","journal-title":"Community Ment. Health J."},{"issue":"1","key":"18_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41398-020-0780-3","volume":"10","author":"C Su","year":"2020","unstructured":"Su, C., Xu, Z., Pathak, J., Wang, F.: Deep learning in mental health outcome research: a scoping review. Transl. Psychiatry 10(1), 1\u201326 (2020)","journal-title":"Transl. Psychiatry"},{"key":"18_CR29","doi-asserted-by":"crossref","unstructured":"Thelisson, E., Sharma, K., Salam, H., Dignum, V.: The general data protection regulation: an opportunity for the HCI community? In: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1\u20138 (2018)","DOI":"10.1145\/3170427.3170632"},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Williams, L.S., et al.: Performance of the PHQ-9 as a screening tool for depression after stroke. Stroke 36(3), 635\u2013638 (2005)","DOI":"10.1161\/01.STR.0000155688.18207.33"}],"container-title":["Lecture Notes in Computer Science","Predictive Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46005-0_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T04:03:31Z","timestamp":1696651411000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46005-0_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031460043","9783031460050"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46005-0_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}