skip to main content
10.1145/3613904.3642369acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and Treatment

Published: 11 May 2024 Publication History

Abstract

Early detection and intervention for relapse is important in the treatment of schizophrenia spectrum disorders. Researchers have developed AI models to predict relapse from patient-contributed data like social media. However, these models face challenges, including misalignment with practice and ethical issues related to transparency, accountability, and potential harm. Furthermore, how patients who have recovered from schizophrenia view these AI models has been underexplored. To address this gap, we first conducted semi-structured interviews with 28 patients and reflexive thematic analysis, which revealed a disconnect between AI predictions and patient experience, and the importance of the social aspect of relapse detection. In response, we developed a prototype that used patients’ Facebook data to predict relapse. Feedback from seven patients highlighted the potential for AI to foster collaboration between patients and their support systems, and to encourage self-reflection. Our work provides insights into human-AI interaction and suggests ways to empower people with schizophrenia.

Supplemental Material

MP4 File - Video Presentation
Video Presentation
Transcript for: Video Presentation

References

[1]
Elizabeth A. Ankrah, Arpita Bhattacharya, Lissamarie Donjuan, Franceli L. Cibrian, Lilibeth Torno, Anamara Ritt Olson, Joel Milam, and Gillian Hayes. 2022. When Worlds Collide: Boundary Management of Adolescent and Young Adult Childhood Cancer Survivors and Caregivers. In CHI Conference on Human Factors in Computing Systems. ACM, New Orleans LA USA, 1–16. https://doi.org/10.1145/3491102.3517544
[2]
Xavier F Amador, David H Strauss, Scott A Yale, Michael M Flaum, Jean Endicott, and Jack M Gorman. 1993. Assessment of insight in psychosis. American journal of Psychiatry 150 (1993), 873–873.
[3]
Nancy C Andreasen, Peg Nopoulos, Susan Schultz, Del Miller, Sanjay Gupta, Victor Swayze, and Michael Flaum. 1994. Positive and negative symptoms of schizophrenia: past, present, and future. Acta Psychiatrica Scandinavica 90 (1994), 51–59.
[4]
Haya Ascher-Svanum, Baojin Zhu, Douglas E Faries, David Salkever, Eric P Slade, Xiaomei Peng, and Robert R Conley. 2010. The cost of relapse and the predictors of relapse in the treatment of schizophrenia. BMC Psychiatry 10, 1 (2010), 2. https://doi.org/10.1186/1471-244x-10-2
[5]
American Psychiatric Association. 2013. Diagnostic and statistical manual of mental disorders (5th ed.).
[6]
Amid Ayobi, Jacob Hughes, Christopher J Duckworth, Jakub J Dylag, Sam James, Paul Marshall, Matthew Guy, Anitha Kumaran, Adriane Chapman, Michael Boniface, and Aisling Ann O’Kane. 2023. Computational Notebooks as Co-Design Tools: Engaging Young Adults Living with Diabetes, Family Carers, and Clinicians with Machine Learning Models. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (2023), 1–20. https://doi.org/10.1145/3544548.3581424
[7]
Anne Kathrine Petersen Bach, Trine Munch Nørgaard, Jens Christian Brok, and Niels van Berkel. 2023. “If I Had All the Time in the World”: Ophthalmologists’ Perceptions of Anchoring Bias Mitigation in Clinical AI Support. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (2023), 1–14. https://doi.org/10.1145/3544548.3581513
[8]
Shaowen Bardzell. 2010. Feminist HCI: taking stock and outlining an agenda for design. In Proceedings of the SIGCHI conference on human factors in computing systems. 1301–1310.
[9]
Shaowen Bardzell and Jeffrey Bardzell. 2011. Towards a feminist HCI methodology: social science, feminism, and HCI. In Proceedings of the SIGCHI conference on human factors in computing systems. 675–684.
[10]
Ian Barnett and John Torous. 2019. Ethics, transparency, and public health at the intersection of innovation and Facebook’s suicide prevention efforts. Annals of internal medicine 170, 8 (2019), 565–566.
[11]
Ian Barnett, John Torous, Patrick Staples, Luis Sandoval, Matcheri Keshavan, and Jukka Pekka Onnela. 2018. Relapse prediction in schizophrenia through digital phenotyping: a pilot study. Neuropsychopharmacology 2018 43:8 43 (2 2018), 1660–1666. Issue 8. https://doi.org/10.1038/s41386-018-0030-z
[12]
Ian Barnett, John Torous, Patrick Staples, Luis Sandoval, Matcheri Keshavan, and Jukka-Pekka Onnela. 2018. Relapse prediction in schizophrenia through digital phenotyping: a pilot study. Neuropsychopharmacology 43, 8 (2018), 1660–1666. https://doi.org/10.1038/s41386-018-0030-z
[13]
Andrew L Beam and Isaac S Kohane. 2016. Translating artificial intelligence into clinical care. Jama 316, 22 (2016), 2368–2369.
[14]
Paul E Bebbington, Orla McBride, Craig Steel, Elizabeth Kuipers, Mirjana Radovanoviĉ, Traolach Brugha, Rachel Jenkins, Howard I Meltzer, and Daniel Freeman. 2013. The structure of paranoia in the general population. The British Journal of Psychiatry 202, 6 (2013), 419–427.
[15]
Ghazaleh Beigi and Huan Liu. 2020. A survey on privacy in social media: Identification, mitigation, and applications. ACM Transactions on Data Science 1, 1 (2020), 1–38.
[16]
Dror Ben-Zeev, Rachel Brian, Rui Wang, Weichen Wang, Andrew T. Campbell, Min S.H. Aung, Michael Merrill, Vincent W.S. Tseng, Tanzeem Choudhury, Marta Hauser, John M. Kane, and Emily A. Scherer. 2017. CrossCheck: Integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse.Psychiatric Rehabilitation Journal 40 (9 2017), 266–275. Issue 3. https://doi.org/10.1037/PRJ0000243
[17]
Abhilash Biradar and SG Totad. 2018. Detecting depression in social media posts using machine learning. In International Conference on Recent Trends in Image Processing and Pattern Recognition. Springer, 716–725.
[18]
Michael Birnbaum, Asra Rizvi, Munmun De Choudhury, Sindhu Ernala, Guillermo Cecchi, and John Kane. 2018. O9.2. IDENTIFYING PSYCHOTIC SYMPTOMS AND PREDICTING RELAPSE THROUGH SOCIAL MEDIA. Schizophrenia Bulletin 44, Suppl 1 (2018), S100—S100. https://doi.org/10.1093/schbul/sby015.246
[19]
Michael L. Birnbaum, Sindhu Kiranmai Ernala, Asra F. Rizvi, Elizabeth Arenare, Anna R. Van Meter, Munmun De Choudhury, and John M. Kane. 2019. Detecting relapse in youth with psychotic disorders utilizing patient-generated and patient-contributed digital data from Facebook. npj Schizophrenia 5 (12 2019), 17. Issue 1. https://doi.org/10.1038/s41537-019-0085-9
[20]
Virginia Braun and Victoria Clarke. 2019. Reflecting on reflexive thematic analysis. Qualitative research in sport, exercise and health 11, 4 (2019), 589–597.
[21]
Antoine Briand, Hayda Almeida, and Marie-Jean Meurs. 2018. Analysis of social media posts for early detection of mental health conditions. In Canadian Conference on Artificial Intelligence. Springer, 133–143.
[22]
Benjamin Buck, Kevin A. Hallgren, Andrew T. Campbell, Tanzeem Choudhury, John M. Kane, and Dror Ben-Zeev. 2021. mHealth-Assisted Detection of Precursors to Relapse in Schizophrenia. Frontiers in Psychiatry 12 (2021), 642200. https://doi.org/10.3389/fpsyt.2021.642200
[23]
Eleanor R. Burgess, Ivana Jankovic, Melissa Austin, Nancy Cai, Adela Kapuścińska, Suzanne Currie, J. Marc Overhage, Erika S Poole, and Jofish Kaye. 2023. Healthcare AI Treatment Decision Support: Design Principles to Enhance Clinician Adoption and Trust. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–19. https://doi.org/10.1145/3544548.3581251
[24]
Danilo Bzdok and Andreas Meyer-Lindenberg. 2018. Machine learning for precision psychiatry: opportunities and challenges. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 3, 3 (2018), 223–230.
[25]
Carrie J. Cai, Emily Reif, Narayan Hegde, Jason Hipp, Been Kim, Daniel Smilkov, Martin Wattenberg, Fernanda Viegas, Greg S. Corrado, Martin C. Stumpe, and Michael Terry. 2019. Human-centered tools for coping with imperfect algorithms during medical decision-making. In Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3290605.3300234 _eprint: 1902.02960.
[26]
Carrie J. Cai, Samantha Winter, David Steiner, Lauren Wilcox, and Michael Terry. 2019. "Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (Nov. 2019), 1–24. https://doi.org/10.1145/3359206
[27]
Sarah Carr. 2020. ‘AI gone mental’: engagement and ethics in data-driven technology for mental health. Journal of Mental Health 29, 2 (2020), 125–130.
[28]
Yoon Jeong Cha, Arpita Saxena, Alice Wou, Joyce Lee, Mark W Newman, and Sun Young Park. 2022. Transitioning Toward Independence: Enhancing Collaborative Self-Management of Children with Type 1 Diabetes. In CHI Conference on Human Factors in Computing Systems. ACM, New Orleans LA USA, 1–17. https://doi.org/10.1145/3491102.3502055
[29]
Stevie Chancellor, Michael L Birnbaum, Eric D Caine, Vincent MB Silenzio, and Munmun De Choudhury. 2019. A taxonomy of ethical tensions in inferring mental health states from social media. In Proceedings of the conference on fairness, accountability, and transparency. 79–88.
[30]
Stevie Chancellor and Munmun De Choudhury. 2020. Methods in predictive techniques for mental health status on social media: a critical review. NPJ digital medicine 3, 1 (2020), 43.
[31]
Danton S. Char, Nigam H. Shah, and David Magnus. 2018. Implementing Machine Learning in Health Care — Addressing Ethical Challenges. New England Journal of Medicine 378 (March 2018), 981–983. https://doi.org/10.1056/nejmp1714229/suppl_file/nejmp1714229_disclosures.pdf
[32]
Adam M. Chekroud, Julia Bondar, Jaime Delgadillo, Gavin Doherty, Akash Wasil, Marjolein Fokkema, Zachary Cohen, Danielle Belgrave, Robert DeRubeis, Raquel Iniesta, Dominic Dwyer, and Karmel Choi. 2021. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 20 (6 2021), 154–170. Issue 2. https://doi.org/10.1002/WPS.20882
[33]
Munmun De Choudhury. 2013. Role of social media in tackling challenges in mental health. Proceedings of the 2nd international workshop on Socially-aware multimedia (2013), 49–52.
[34]
Simon Coghlan, Jenny Waycott, Amanda Lazar, and Barbara Barbosa Neves. 2021. Dignity, autonomy, and style of company: Dimensions older adults consider for robot companions. Proceedings of the ACM on human-computer interaction 5, CSCW1 (2021), 1–25.
[35]
Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Machine learning 20 (1995), 273–297.
[36]
Shanley Corvite, Kat Roemmich, Tillie Ilana Rosenberg, and Nazanin Andalibi. 2023. Data Subjects’ Perspectives on Emotion Artificial Intelligence Use in the Workplace: A Relational Ethics Lens. Proceedings of the ACM on Human-Computer Interaction 7, CSCW1 (2023), 1–38.
[37]
Simon D’Alfonso. 2020. AI in Mental Health. Current Opinion in Psychology 36 (12 2020), 112–117. https://doi.org/10.1016/J.COPSYC.2020.04.005
[38]
Munmun De Choudhury. 2013. Role of social media in tackling challenges in mental health. In Proceedings of the 2nd international workshop on Socially-aware multimedia. 49–52.
[39]
Lina Dencik, Arne Hintz, and Jonathan Cable. 2016. Towards data justice? The ambiguity of anti-surveillance resistance in political activism. Big Data & Society 3, 2 (2016), 2053951716679678.
[40]
Michael A DeVito, Jeremy Birnholtz, Jeffery T Hancock, Megan French, and Sunny Liu. 2018. How people form folk theories of social media feeds and what it means for how we study self-presentation. In Proceedings of the 2018 CHI conference on human factors in computing systems. 1–12.
[41]
Emma Dixon, Jesse Anderson, Diana Blackwelder, Mary L. Radnofsky, and Amanda Lazar. 2022. Barriers to Online Dementia Information and Mitigation. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1–14.
[42]
Emma Dixon, Anne Marie Piper, and Amanda Lazar. 2021. “Taking care of myself as long as I can”: How People with Dementia Configure Self-Management Systems. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. ACM, Yokohama Japan, 1–14. https://doi.org/10.1145/3411764.3445225
[43]
Simon D’Alfonso. 2020. AI in mental health. Current Opinion in Psychology 36 (2020), 112–117.
[44]
Robin Emsley, Bonginkosi Chiliza, Laila Asmal, and Brian H Harvey. 2013. The nature of relapse in schizophrenia. BMC psychiatry 13 (2013), 1–8.
[45]
Andrea Ferrario and Michele Loi. 2022. How Explainability Contributes to Trust in AI. 2022 ACM Conference on Fairness, Accountability, and Transparency (2022), 1457–1466. https://doi.org/10.1145/3531146.3533202
[46]
Geraldine Fitzpatrick and Gunnar Ellingsen. 2013. A review of 25 years of CSCW research in healthcare: contributions, challenges and future agendas. Computer Supported Cooperative Work (CSCW) 22 (2013), 609–665.
[47]
Pin Sym Foong, Charis Anne Lim, Joshua Wong, Chang Siang Lim, Simon Tangi Perrault, and Gerald Ch Koh. 2020. "You Cannot Offer Such a Suggestion": Designing for Family Caregiver Input in Home Care Systems. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, Honolulu HI USA, 1–13. https://doi.org/10.1145/3313831.3376607
[48]
William R Frey, Desmond U Patton, Michael B Gaskell, and Kyle A McGregor. 2020. Artificial intelligence and inclusion: Formerly gang-involved youth as domain experts for analyzing unstructured twitter data. Social Science Computer Review 38, 1 (2020), 42–56.
[49]
Wolfgang Gaebel and Mathias Riesbeck. 2007. Revisiting the relapse predictive validity of prodromal symptoms in schizophrenia. Schizophrenia research 95, 1-3 (2007), 19–29.
[50]
Sarah Graham, Colin Depp, Ellen E. Lee, Camille Nebeker, Xin Tu, Ho Cheol Kim, and Dilip V. Jeste. 2019. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Current Psychiatry Reports 21, 11 (2019). https://doi.org/10.1007/s11920-019-1094-0
[51]
Sarah Graham, Colin Depp, Ellen E Lee, Camille Nebeker, Xin Tu, Ho-Cheol Kim, and Dilip V Jeste. 2019. Artificial intelligence for mental health and mental illnesses: an overview. Current psychiatry reports 21 (2019), 1–18.
[52]
Hongyan Gu, Chunxu Yang, Mohammad Haeri, Jing Wang, Shirley Tang, Wenzhong Yan, Shujin He, Christopher Kazu Williams, Shino Magaki, and Xiang ’Anthony’ Chen. 2023. Augmenting Pathologists with NaviPath: Design and Evaluation of a Human-AI Collaborative Navigation System. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (2023), 1–19. https://doi.org/10.1145/3544548.3580694
[53]
Connie Guan, Anya Bouzida, Ramzy M. Oncy-avila, Sanika Moharana, and Laurel D. Riek. 2021. Taking an (Embodied) Cue From Community Health: Designing Dementia Caregiver Support Technology to Advance Health Equity. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. ACM, Yokohama Japan, 1–16. https://doi.org/10.1145/3411764.3445559
[54]
Jianxing He, Sally L. Baxter, Jie Xu, Jiming Xu, Xingtao Zhou, and Kang Zhang. 2019. The practical implementation of artificial intelligence technologies in medicine. Nature Medicine 2019 25:1 25 (1 2019), 30–36. Issue 1. https://doi.org/10.1038/s41591-018-0307-0
[55]
Joy He-Yueya, Benjamin Buck, Andrew Campbell, Tanzeem Choudhury, John M. Kane, Dror Ben-Zeev, and Tim Althoff. 2020. Assessing the relationship between routine and schizophrenia symptoms with passively sensed measures of behavioral stability. npj Schizophrenia 6, 1 (2020), 35. https://doi.org/10.1038/s41537-020-00123-2
[56]
Philip Henson, Ryan D’Mello, Aditya Vaidyam, Matcheri Keshavan, and John Torous. 2021. Anomaly detection to predict relapse risk in schizophrenia. Translational Psychiatry 2021 11:1 11 (1 2021), 1–6. Issue 1. https://doi.org/10.1038/s41398-020-01123-7
[57]
Philip Henson, Ryan D’Mello, Aditya Vaidyam, Matcheri Keshavan, and John Torous. 2021. Anomaly detection to predict relapse risk in schizophrenia. Translational Psychiatry 11, 1 (2021), 28. https://doi.org/10.1038/s41398-020-01123-7
[58]
Marvin I Herz, J Steven Lamberti, Jim Mintz, Ruth Scott, Susan P O’Dell, Lisa McCartan, and Glen Nix. 2000. A program for relapse prevention in schizophrenia: a controlled study. Archives of general psychiatry 57, 3 (2000), 277–283.
[59]
Marvin I Herz and Charles Melville. 1980. Relapse in schizophrenia.The American journal of psychiatry 137, 7 (1980), 801–805.
[60]
Sen H Hirano, Michael T Yeganyan, Gabriela Marcu, David H Nguyen, Lou Anne Boyd, and Gillian R Hayes. 2010. vSked: evaluation of a system to support classroom activities for children with autism. In Proceedings of the SIGCHI conference on human factors in computing systems. 1633–1642.
[61]
Tad Hirsch, Kritzia Merced, Shrikanth Narayanan, Zac E Imel, and David C Atkins. 2017. Designing Contestability: Interaction Design, Machine Learning, and Mental Health. ACM, New York, NY, USA, 95–99. https://doi.org/10.1145/3064663.3064703
[62]
Wayne Holmes and Kaśka Porayska-Pomsta. 2022. The Ethics of Artificial Intelligence in education: Practices, challenges, and debates. Taylor & Francis.
[63]
Hwajung Hong, Jennifer G Kim, Gregory D Abowd, and Rosa I Arriaga. 2012. Designing a social network to support the independence of young adults with autism. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work. 627–636.
[64]
Matthew K. Hong, Udaya Lakshmi, Kimberly Do, Sampath Prahalad, Thomas Olson, Rosa I. Arriaga, and Lauren Wilcox. 2020. Using Diaries to Probe the Illness Experiences of Adolescent Patients and Parental Caregivers. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, Honolulu HI USA, 1–16. https://doi.org/10.1145/3313831.3376426
[65]
Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H Schwartz, and Hugo JWL Aerts. 2018. Artificial intelligence in radiology. Nature Reviews Cancer 18, 8 (2018), 500–510.
[66]
Yan Huang, Xiaoqian Liu, and Tingshao Zhu. 2019. Suicidal ideation detection via social media analytics. In International Conference on Human Centered Computing. Springer, 166–174.
[67]
Jim Isaak and Mina J Hanna. 2018. User data privacy: Facebook, Cambridge Analytica, and privacy protection. Computer 51, 8 (2018), 56–59.
[68]
Alon Jacovi, Ana Marasović, Tim Miller, and Yoav Goldberg. 2021. Formalizing Trust in Artificial Intelligence. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (2021), 624–635. https://doi.org/10.1145/3442188.3445923
[69]
Jazette Johnson, Vitica Arnold, Anne Marie Piper, and Gillian R Hayes. 2022. " It’s a lonely disease": Cultivating Online Spaces for Social Support among People Living with Dementia and Dementia Caregivers. Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (2022), 1–27.
[70]
Sophie Kalckreuth, Friederike Trefflich, and Christine Rummel-Kluge. 2014. Mental health related Internet use among psychiatric patients: a cross-sectional analysis. BMC psychiatry 14, 1 (2014), 1–11.
[71]
Stanley R. Kay, Abraham Fiszbein, and Lewis A. Opler. 1987. The Positive and Negative Syndrome Scale (PANSS) for Schizophrenia. Schizophrenia Bulletin 13, 2 (Jan. 1987), 261–276. https://doi.org/10.1093/schbul/13.2.261
[72]
Rachel Kornfield, Renwen Zhang, Jennifer Nicholas, Stephen M Schueller, Scott A Cambo, David C Mohr, and Madhu Reddy. 2020. " Energy is a Finite Resource": Designing Technology to Support Individuals across Fluctuating Symptoms of Depression. In Proceedings of the 2020 CHI Conference on Human factors in Computing systems. 1–17.
[73]
Nikolaos Koutsouleris, Tobias U. Hauser, Vasilisa Skvortsova, and Munmun De Choudhury. 2022. From promise to practice: towards the realisation of AI-informed mental health care. The Lancet Digital Health 4 (11 2022), e829–e840. https://doi.org/10.1016/s2589-7500(22)00153-4
[74]
Kaylee Payne Kruzan, Ada Ng, Colleen Stiles-Shields, Emily G Lattie, David C Mohr, and Madhu Reddy. 2023. The Perceived Utility of Smartphone and Wearable Sensor Data in Digital Self-tracking Technologies for Mental Health. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–16.
[75]
Tzu-Sheng Kuo, Hong Shen, Jisoo Geum, Nev Jones, Jason I Hong, Haiyi Zhu, and Kenneth Holstein. 2023. Understanding Frontline Workers’ and Unhoused Individuals’ Perspectives on AI Used in Homeless Services. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–17.
[76]
Simon Meyer Lauritsen, Mads Kristensen, Mathias Vassard Olsen, Morten Skaarup Larsen, Katrine Meyer Lauritsen, Marianne Johansson Jørgensen, Jeppe Lange, and Bo Thiesson. 2020. Explainable artificial intelligence model to predict acute critical illness from electronic health records. Nature communications 11, 1 (2020), 3852.
[77]
Q.Vera Liao and S. Shyam Sundar. 2022. Designing for Responsible Trust in AI Systems: A Communication Perspective. 2022 ACM Conference on Fairness, Accountability, and Transparency (2022), 1257–1268. https://doi.org/10.1145/3531146.3533182
[78]
Jeffrey A Lieberman, Diana Perkins, Aysenil Belger, Miranda Chakos, Fred Jarskog, Kalina Boteva, and John Gilmore. 2001. The early stages of schizophrenia: speculations on pathogenesis, pathophysiology, and therapeutic approaches. Biological psychiatry 50, 11 (2001), 884–897.
[79]
Duri Long and Brian Magerko. 2020. What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI conference on human factors in computing systems. 1–16.
[80]
David Lyon. 2014. Surveillance, Snowden, and big data: Capacities, consequences, critique. Big data & society 1, 2 (2014), 2053951714541861.
[81]
Henrietta Lyons, Eduardo Velloso, and Tim Miller. 2021. Conceptualising Contestability. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (2021), 1–25. https://doi.org/10.1145/3449180 arXiv:2103.01774
[82]
Monique Mann and Tobias Matzner. 2019. Challenging algorithmic profiling: The limits of data protection and anti-discrimination in responding to emergent discrimination. Big Data & Society 6, 2 (2019), 2053951719895805.
[83]
Christine Manta, Bray Patrick-Lake, and Jennifer C Goldsack. 2020. Digital measures that matter to patients: a framework to guide the selection and development of digital measures of health. Digital Biomarkers 4, 3 (2020), 69–77.
[84]
Gabriela Marcu, Karina Caro, Juan Fernando Maestre, Kay H. Connelly, Robin Brewer, and Christina N. Harrington. 2019. Strategies for Inclusion in the Design of Pervasive Computing for Health and Wellbeing. IEEE Pervasive Computing 18, 1 (Jan. 2019), 89–93. https://doi.org/10.1109/MPRV.2019.2898485
[85]
Catherine C Marshall and Frank M Shipman. 2011. Social media ownership: using twitter as a window onto current attitudes and beliefs. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 1081–1090.
[86]
Oscar Mayora, Bert Arnrich, Jakob Bardram, Carsten Drager, Andrea Finke, Mads Frost, Silvia Giordano, Franz Gravenhorst, Agnes Grunerbl, Christian Haring, Reinhold Haux, Paul Lukowicz, Amir Muaremi, Steven Mudda, Stefan Ohler, Alessandro Puiatti, Nina Reichwaldt, Corinna Scharnweber, Gerhard Troester, Lars Vedel Kessing, and Gabriel Wurzer. 2013. Personal Health Systems for Bipolar Disorder Anecdotes, Challenges and Lessons Learnt from MONARCA Project. In Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques. IEEE. https://doi.org/10.4108/icst.pervasivehealth.2013.252123
[87]
David C Mohr, Mi Zhang, and Stephen M Schueller. 2017. Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annual review of clinical psychology 13 (2017), 23–47.
[88]
Diego Muñoz, Stu Favilla, Sonja Pedell, Andrew Murphy, Jeanie Beh, and Tanya Petrovich. 2021. Evaluating an App to Promote a Better Visit Through Shared Activities for People Living with Dementia and their Families. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. ACM, Yokohama Japan, 1–13. https://doi.org/10.1145/3411764.3445764
[89]
Janet R Nelson. 2003. Bioethics and the marginalization of mental illness. Journal of the Society of Christian Ethics 23, 2 (2003), 179–197.
[90]
Viet Cuong Nguyen, Nathaniel Lu, John M Kane, Michael L Birnbaum, and Munmun De Choudhury. 2022. Cross-Platform Detection of Psychiatric Hospitalization via Social Media Data: Comparison Study. JMIR Mental Health 9, 12 (2022), e39747.
[91]
Hyanghee Park, Daehwan Ahn, Kartik Hosanagar, and Joonhwan Lee. 2021. Human-AI interaction in human resource management: Understanding why employees resist algorithmic evaluation at workplaces and how to mitigate burdens. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–15.
[92]
Krishna R Patel, Jessica Cherian, Kunj Gohil, and Dylan Atkinson. 2014. Schizophrenia: overview and treatment options. Pharmacy and Therapeutics 39, 9 (2014), 638.
[93]
Sachin R Pendse, Amit Sharma, Aditya Vashistha, Munmun De Choudhury, and Neha Kumar. 2021. “Can I not be suicidal on a Sunday?”: understanding technology-mediated pathways to mental health support. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–16.
[94]
Alisha Pradhan, Ben Jelen, Katie A Siek, Joel Chan, and Amanda Lazar. 2020. Understanding Older Adults’ Participation in Design Workshops. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–15.
[95]
Alisha Pradhan, Amanda Lazar, and Leah Findlater. 2020. Use of intelligent voice assistants by older adults with low technology use. ACM Transactions on Computer-Human Interaction (TOCHI) 27, 4 (2020), 1–27.
[96]
Neguine Rezaii, Elaine Walker, and Phillip Wolff. 2019. A machine learning approach to predicting psychosis using semantic density and latent content analysis. npj Schizophrenia 2019 5:1 5 (6 2019), 1–12. Issue 1. https://doi.org/10.1038/s41537-019-0077-9
[97]
Jordana K Schmier and Michael T Halpern. 2004. Patient recall and recall bias of health state and health status. Expert review of pharmacoeconomics & outcomes research 4, 2 (2004), 159–163.
[98]
Partho P Sengupta and Donald A Adjeroh. 2018. Will artificial intelligence replace the human echocardiographer? Clinical considerations., 1639–1642 pages.
[99]
Adrian BR Shatte, Delyse M Hutchinson, and Samantha J Teague. 2019. Machine learning in mental health: a scoping review of methods and applications. Psychological medicine 49, 9 (2019), 1426–1448.
[100]
Donghoon Shin, Jaeyoon Song, Seokwoo Song, Jisoo Park, Joonhwan Lee, and Soojin Jun. 2020. TalkingBoogie: Collaborative Mobile AAC System for Non-verbal Children with Developmental Disabilities and Their Caregivers. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, Honolulu HI USA, 1–13. https://doi.org/10.1145/3313831.3376154
[101]
Farheen Siddiqui, Delvin Varghese, Pushpendra Singh, Sunita Bapuji Bayyavarapu, Stephen Lindsay, Dharshani Chandrasekara, Pranav Kulkarni, Ling Wu, Taghreed Alshehri, and Patrick Olivier. 2023. Exploring the digital support needs of caregivers of people with serious mental illness. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. ACM, Hamburg Germany, 1–16. https://doi.org/10.1145/3544548.3580674
[102]
Venkatesh Sivaraman, Leigh A Bukowski, Joel Levin, Jeremy M. Kahn, and Adam Perer. 2023. Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (2023), 1–18. https://doi.org/10.1145/3544548.3581075
[103]
David Spiegelhalter. 2020. Should we trust algorithms. Harvard Data Science Review 2, 1 (2020), 1.
[104]
Sarah Sullivan, Kate Northstone, Caroline Gadd, Julian Walker, Ruta Margelyte, Alison Richards, and Penny Whiting. 2017. Models to predict relapse in psychosis: A systematic review. PLOS ONE 12 (9 2017), e0183998. https://doi.org/10.1371/journal.pone.0183998
[105]
Yla R Tausczik and James W Pennebaker. 2010. The psychological meaning of words: LIWC and computerized text analysis methods. Journal of language and social psychology 29, 1 (2010), 24–54.
[106]
Anja Thieme, Danielle Belgrave, and Gavin Doherty. 2020. Machine learning in mental health: A systematic review of the HCI literature to support the development of effective and implementable ML systems. ACM Transactions on Computer-Human Interaction (TOCHI) 27, 5 (2020), 1–53.
[107]
Eric J. Topol. 2019. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine 2019 25:1 25 (1 2019), 44–56. Issue 1. https://doi.org/10.1038/s41591-018-0300-7
[108]
Himanshu Verma, Jakub Mlynar, Roger Schaer, Julien Reichenbach, Mario Jreige, John Prior, Florian Evéquoz, and Adrien Depeursinge. 2023. Rethinking the Role of AI with Physicians in Oncology: Revealing Perspectives from Clinical and Research Workflows. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (2023), 1–19. https://doi.org/10.1145/3544548.3581506
[109]
Simone N Vigod, Paul A Kurdyak, Dallas Seitz, Nathan Herrmann, Kinwah Fung, Elizabeth Lin, Christopher Perlman, Valerie H Taylor, Paula A Rochon, and Andrea Gruneir. 2015. READMIT: a clinical risk index to predict 30-day readmission after discharge from acute psychiatric units. Journal of psychiatric research 61 (2015), 205–213.
[110]
Rui Wang, Weichen Wang, Min Hane Aung, Dror Ben-Zeev, Rachel Brian, Andrew T Campbell, Tanzeem Choudhury, Marta Hauser, John Kane, Emily A Scherer, and Megan Walsh. 2018. Predicting Symptom Trajectories of Schizophrenia Using Mobile Sensing. GetMobile: Mobile Comp. and Comm. 22, 2 (2018), 32—37.
[111]
Peter J Weiden and Mark Olfson. 1995. Cost of relapse in schizophrenia. Schizophrenia bulletin 21, 3 (1995), 419–429.
[112]
Durk Wiersma, Fokko J Nienhuis, Cees J Slooff, and Robert Giel. 1998. Natural course of schizophrenic disorders: a 15-year followup of a Dutch incidence cohort. Schizophrenia bulletin 24, 1 (1998), 75–85.
[113]
World Health Organization. 2022. Schizophrenia. https://www.who.int/news-room/fact-sheets/detail/schizophrenia
[114]
Albert C Yang and Shih-Jen Tsai. 2013. Is mental illness complex? From behavior to brain. Progress in Neuro-Psychopharmacology and Biological Psychiatry 45 (2013), 253–257.
[115]
Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023. Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems 14 (2023), 1–14. https://doi.org/10.1145/3544548.3581393
[116]
Dong Whi Yoo, Aditi Bhatnagar, Sindhu Kiranmai Ernala, Asra Ali, Michael L Birnbaum, Gregory D Abowd, and Munmun De Choudhury. 2023. Discussing Social Media During Psychotherapy Consultations: Patient Narratives and Privacy Implications. Proceedings of the ACM on Human-Computer Interaction 7, CSCW1 (2023), 1–24.
[117]
Dong Whi Yoo, Sindhu Ernala, Bahador Saket, Domino Weir, Elizabeth Arenare, Asra Ali, Anna Van Meter, Michael Birnbaum, Gregory Abowd, and Munmun De Choudhury. 2021. Clinician Perspectives on Utilizing Computational Mental Health Insights from Patient Social Media: Design and Qualitative Evaluation of a Prototype. JMIR Mental Health (2021).
[118]
Joanne Zhou, Bishal Lamichhane, Dror Ben-Zeev, Andrew Campbell, and Akane Sano. 2022. Predicting psychotic relapse in schizophrenia with mobile sensor data: routine cluster analysis. JMIR mHealth and uHealth 10, 4 (2022), e31006.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems
May 2024
18961 pages
ISBN:9798400703300
DOI:10.1145/3613904
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 May 2024

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. artificial intelligence
  2. mental health
  3. patient perspectives
  4. schizophrenia relapse

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • NIH

Conference

CHI '24

Acceptance Rates

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Upcoming Conference

CHI 2025
ACM CHI Conference on Human Factors in Computing Systems
April 26 - May 1, 2025
Yokohama , Japan

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 889
    Total Downloads
  • Downloads (Last 12 months)889
  • Downloads (Last 6 weeks)99
Reflects downloads up to 01 Jan 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Full Text

View this article in Full Text.

Full Text

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media