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From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models

Authors:

Zachary Englhardt,

Margaret E. Morris,

Chun-Cheng Chang,

Xuhai "Orson" Xu,

Vikram IyerAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 8, Issue 2

Article No.: 56, Pages 1 - 25

https://doi.org/10.1145/3659604

Published: 15 May 2024 Publication History

Abstract

Passively collected behavioral health data from ubiquitous sensors could provide mental health professionals valuable insights into patient's daily lives, but such efforts are impeded by disparate metrics, lack of interoperability, and unclear correlations between the measured signals and an individual's mental health. To address these challenges, we pioneer the exploration of large language models (LLMs) to synthesize clinically relevant insights from multi-sensor data. We develop chain-of-thought prompting methods to generate LLM reasoning on how data pertaining to activity, sleep and social interaction relate to conditions such as depression and anxiety. We then prompt the LLM to perform binary classification, achieving accuracies of 61.1%, exceeding the state of the art. We find models like GPT-4 correctly reference numerical data 75% of the time.

While we began our investigation by developing methods to use LLMs to output binary classifications for conditions like depression, we find instead that their greatest potential value to clinicians lies not in diagnostic classification, but rather in rigorous analysis of diverse self-tracking data to generate natural language summaries that synthesize multiple data streams and identify potential concerns. Clinicians envisioned using these insights in a variety of ways, principally for fostering collaborative investigation with patients to strengthen the therapeutic alliance and guide treatment. We describe this collaborative engagement, additional envisioned uses, and associated concerns that must be addressed before adoption in real-world contexts.

References

[1]

2013. Diagnostic and statistical manual of mental disorders: DSM-5 (fifth edition. ed.). American Psychiatric Association, Arlington, VA.

[2]

Mostafa M. Amin, Erik Cambria, and Björn W. Schuller. 2023. Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT. http://arxiv.org/abs/2303.03186

[3]

Chizobam Ani, Mohsen Bazargan, David Hindman, Douglas Bell, Muhammad A. Farooq, Lutful Akhanjee, Francis Yemofio, Richard Baker, and Michael Rodriguez. 2008. Depression symptomatology and diagnosis: discordance between patients and physicians in primary care settings. BMC Family Practice 9, 1 (Jan. 2008), 1. https://doi.org/10.1186/1471-2296-9-1

[4]

Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, Marco Dos Santos, Stephen McAleer, Albert Q.Jiang, Jia Deng, Stella Biderman, and Sean Welleck. 2023. Llemma: An Open Language Model For Mathematics. https://doi.org/10.48550/arXiv.2310.10631 arXiv:2310.10631 [cs].

[5]

Sangwon Bae, Denzil Ferreira, Brian Suffoletto, Juan C. Puyana, Ryan Kurtz, Tammy Chung, and Anind K. Dey. 2017. Detecting drinking episodes in young adults using smartphone-based sensors. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 2, Article 5 (Jun 2017), 36 pages. https://doi.org/10.1145/3090051

Digital Library

[6]

Rachit Bansal, Bidisha Samanta, Siddharth Dalmia, Nitish Gupta, Shikhar Vashishth, Sriram Ganapathy, Abhishek Bapna, Prateek Jain, and Partha Talukdar. 2024. LLM Augmented LLMs: Expanding Capabilities through Composition. https://doi.org/10.48550/arXiv.2401.02412 arXiv:2401.02412 [cs].

[7]

Dror Ben-Zeev, Emily A. Scherer, Rui Wang, Haiyi Xie, and Andrew T. Campbell. 2015. Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health. Psychiatric Rehabilitation Journal 38, 3 (2015), 218.

[8]

Thorsten Brants, Ashok C Popat, Peng Xu, Franz J Och, and Jeffrey Dean. 2007. Large language models in machine translation. (2007).

[9]

Leo Breiman. 2001. Random forests. Machine learning 45 (2001), 5--32.

[10]

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems, Vol. 33. Curran Associates, Inc., 1877--1901. https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html

[11]

Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang. 2023. Sparks of Artificial General Intelligence: Early experiments with GPT-4. http://arxiv.org/abs/2303.12712

[12]

Sarah Kate Cameron, Jacqui Rodgers, and Dave Dagnan. 2018. The relationship between the therapeutic alliance and clinical outcomes in cognitive behaviour therapy for adults with depression: A meta-analytic review. Clinical psychology & psychotherapy 25, 3 (2018), 446--456.

[13]

Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al. 2021. Extracting training data from large language models. In 30th USENIX Security Symposium (USENIX Security 21). 2633--2650.

[14]

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 (March 2020), 1--11. https://doi.org/10.1038/s41746-020-0233-7

[15]

Prerna Chikersal, Afsaneh Doryab, Michael Tumminia, Daniella K Villalba, Janine M Dutcher, Xinwen Liu, Sheldon Cohen, Kasey G. Creswell, Jennifer Mankoff, J. David Creswell, Mayank Goel, and Anind K. Dey. 2021. Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing. ACM Transactions on Computer-Human Interaction 28, 1 (Jan. 2021), 1--41. https://doi.org/10.1145/3422821

Digital Library

[16]

Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311 (2022).

[17]

Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. 2022. PaLM: Scaling Language Modeling with Pathways. http://arxiv.org/abs/2204.02311 arXiv:2204.02311 [cs].

[18]

Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022. Scaling Instruction-Finetuned Language Models. http://arxiv.org/abs/2210.11416 arXiv:2210.11416 [cs].

[19]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 [cs] (May 2019). http://arxiv.org/abs/1810.04805

[20]

Xiangjue Dong, Yibo Wang, Philip S. Yu, and James Caverlee. 2023. Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation. https://doi.org/10.48550/arXiv.2311.00306 arXiv:2311.00306 [cs].

[21]

Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023. PAL: Program-aided Language Models. In Proceedings of the 40th International Conference on Machine Learning. PMLR, 10764--10799. https://proceedings.mlr.press/v202/gao23f.html

[22]

Caglar Gulcehre, Orhan Firat, Kelvin Xu, Kyunghyun Cho, and Yoshua Bengio. 2017. On integrating a language model into neural machine translation. Computer Speech & Language 45 (2017), 137--148.

Digital Library

[23]

Aodhán Hickey. 2021. The rise of wearables: From innovation to implementation. In Digital health. Elsevier, 357--365.

[24]

Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. 2023. Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes. arXiv preprint arXiv:2305.02301 (2023).

[25]

Jeremy F Huckins, Alex W DaSilva, Elin L Hedlund, Eilis I Murphy, Courtney Rogers, Weichen Wang, Mikio Obuchi, Paul E Holtzheimer, Dylan D Wagner, and Andrew T Campbell. 2020. Causal Factors of Anxiety and Depression in College Students: Longitudinal Ecological Momentary Assessment and Causal Analysis Using Peter and Clark Momentary Conditional Independence. JMIR Mental Health 7, 6 (June 2020), e16684. https://doi.org/10.2196/16684

[26]

Jeremy F Huckins, Alex W daSilva, Weichen Wang, Elin Hedlund, Courtney Rogers, Subigya K Nepal, Jialing Wu, Mikio Obuchi, Eilis I Murphy, Meghan L Meyer, Dylan D Wagner, Paul E Holtzheimer, and Andrew T Campbell. 2020. Mental Health and Behavior of College Students During the Early Phases of the COVID-19 Pandemic: Longitudinal Smartphone and Ecological Momentary Assessment Study. Journal of Medical Internet Research 22, 6 (June 2020), e20185. https://doi.org/10.2196/20185

[27]

R Indrakumari, T Poongodi, P Suresh, and B Balamurugan. 2020. The growing role of Internet of Things in healthcare wearables. In Emergence of Pharmaceutical Industry Growth with Industrial IoT Approach. Elsevier, 163--194.

[28]

Nino Isakadze and Seth S. Martin. 2020. How useful is the smartwatch ECG? Trends in Cardiovascular Medicine 30, 7 (Oct. 2020), 442--448. https://doi.org/10.1016/j.tcm.2019.10.010

[29]

Nicholas C. Jacobson and Yeon Joo Chung. 2020. Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones. Sensors 20, 12 (June 2020), 3572. https://doi.org/10.3390/s20123572

[30]

Lavender Yao Jiang, Xujin Chris Liu, Nima Pour Nejatian, Mustafa Nasir-Moin, Duo Wang, Anas Abidin, Kevin Eaton, Howard Antony Riina, Ilya Laufer, Paawan Punjabi, Madeline Miceli, Nora C. Kim, Cordelia Orillac, Zane Schnurman, Christopher Livia, Hannah Weiss, David Kurland, Sean Neifert, Yosef Dastagirzada, Douglas Kondziolka, Alexander T. M. Cheung, Grace Yang, Ming Cao, Mona Flores, Anthony B. Costa, Yindalon Aphinyanaphongs, Kyunghyun Cho, and Eric Karl Oermann. 2023. Health system-scale language models are all-purpose prediction engines. Nature (June 2023). https://doi.org/10.1038/s41586-023-06160-y

[31]

Yubin Kim, Xuhai Xu, Daniel McDuff, Cynthia Breazeal, and Hae Won Park. 2024. Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data. https://arxiv.org/abs/2401.06866v1

[32]

Jan Kocoń, Igor Cichecki, Oliwier Kaszyca, Mateusz Kochanek, Dominika Szydło, Joanna Baran, Julita Bielaniewicz, Marcin Gruza, Arkadiusz Janz, Kamil Kanclerz, et al. 2023. ChatGPT: Jack of all trades, master of none. Information Fusion (2023), 101861.

[33]

K. Kroenke, R. L. Spitzer, J. B.W. Williams, and B. Lowe. 2009. An Ultra-Brief Screening Scale for Anxiety and Depression: The PHQ-4. Psychosomatics 50, 6 (Nov. 2009), 613--621. https://doi.org/10.1176/appi.psy.50.6.613

[34]

Bishal Lamichhane. 2023. Evaluation of ChatGPT for NLP-based Mental Health Applications. http://arxiv.org/abs/2303.15727

[35]

Heon-Jeong Lee, Chul-Hyun Cho, Taek Lee, Jaegwon Jeong, Ji Won Yeom, Sojeong Kim, Sehyun Jeon, Ju Yeon Seo, Eunsoo Moon, Ji Hyun Baek, Dong Yeon Park, Se Joo Kim, Tae Hyon Ha, Boseok Cha, Hee-Ju Kang, Yong-Min Ahn, Yujin Lee, Jung-Been Lee, and Leen Kim. 2023. Prediction of impending mood episode recurrence using real-time digital phenotypes in major depression and bipolar disorders in South Korea: a prospective nationwide cohort study. Psychological Medicine 53, 12 (Sept. 2023), 5636--5644. https://doi.org/10.1017/S0033291722002847

[36]

Yunxiang Li, Zihan Li, Kai Zhang, Ruilong Dan, Steve Jiang, and You Zhang. 2023. ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge. http://arxiv.org/abs/2303.14070 arXiv:2303.14070 [cs].

[37]

Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023. Lost in the Middle: How Language Models Use Long Contexts. https://doi.org/10.48550/arXiv.2307.03172 arXiv:2307.03172 [cs].

[38]

Xin Liu, Daniel McDuff, Geza Kovacs, Isaac Galatzer-Levy, Jacob Sunshine, Jiening Zhan, Ming-Zher Poh, Shun Liao, Paolo Di Achille, and Shwetak Patel. 2023. Large Language Models are Few-Shot Health Learners. In arXiv.

[39]

Xin Liu, Daniel McDuff, Geza Kovacs, Isaac Galatzer-Levy, Jacob Sunshine, Jiening Zhan, Ming-Zher Poh, Shun Liao, Paolo Di Achille, and Shwetak Patel. 2023. Large Language Models are Few-Shot Health Learners. arXiv preprint arXiv:2305.15525 (2023).

[40]

Daniel J Martin, John P Garske, and M Katherine Davis. 2000. Relation of the therapeutic alliance with outcome and other variables: a meta-analytic review. Journal of consulting and clinical psychology 68, 3 (2000), 438.

[41]

Stephen M Mattingly, Julie M Gregg, Pino Audia, Ayse Elvan Bayraktaroglu, Andrew T Campbell, Nitesh V Chawla, Vedant Das Swain, Munmun De Choudhury, Sidney K D'Mello, Anind K Dey, et al. 2019. The Tesserae project: Large-scale, longitudinal, in situ, multimodal sensing of information workers. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. 1--8.

Digital Library

[42]

Jun-Ki Min, Afsaneh Doryab, Jason Wiese, Shahriyar Amini, John Zimmerman, and Jason I. Hong. 2014. Toss "n" turn: Smartphone as sleep and sleep quality detector. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Toronto, Ontario, Canada) (CHI '14). Association for Computing Machinery, New York, NY, USA, 477--486. https://doi.org/10.1145/2556288.2557220

Digital Library

[43]

Shayan Mirjafari, Kizito Masaba, Ted Grover, Weichen Wang, Pino G. Audia, Andrew T. Campbell, Nitesh V. Chawla, Vedant Das Swain, Munmun De Choudhury, Anind K. Dey, et al. 2019. Differentiating higher and lower job performers in the workplace using mobile sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3, 2 (2019), 37:1-37:24. https://doi.org/10.1145/3328908

Digital Library

[44]

Stefanie Nickels, Matthew D Edwards, Sarah F Poole, Dale Winter, Jessica Gronsbell, Bella Rozenkrants, David P Miller, Mathias Fleck, Alan McLean, Bret Peterson, et al. 2021. Toward a mobile platform for real-world digital measurement of depression: User-centered design, data quality, and behavioral and clinical modeling. JMIR mental health 8, 8 (2021), e27589.

[45]

Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, and Eric Horvitz. 2023. Capabilities of GPT-4 on Medical Challenge Problems. http://arxiv.org/abs/2303.13375 arXiv:2303.13375 [cs].

[46]

Reham Omar, Omij Mangukiya, Panos Kalnis, and Essam Mansour. 2023. Chatgpt versus traditional question answering for knowledge graphs: Current status and future directions towards knowledge graph chatbots. arXiv preprint arXiv:2302.06466 (2023).

[47]

OpenAI. 2023. GPT-4 Technical Report. arXiv:2303.08774 [cs.CL]

[48]

Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, and Samuel R. Bowman. 2022. BBQ: A Hand-Built Bias Benchmark for Question Answering. https://doi.org/10.48550/arXiv.2110.08193 arXiv:2110.08193 [cs].

[49]

Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 2023. Is ChatGPT a general-purpose natural language processing task solver? arXiv preprint arXiv:2302.06476 (2023).

[50]

Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving Language Understanding by Generative Pre-Training.

[51]

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research (2020).

[52]

Joshua Robinson and David Wingate. 2023. Leveraging Large Language Models for Multiple Choice Question Answering. In The Eleventh International Conference on Learning Representations. https://openreview.net/forum?id=yKbprarjc5B

[53]

Darius A. Rohani, Maria Faurholt-Jepsen, Lars Vedel Kessing, and Jakob E. Bardram. 2018. Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review. JMIR mHealth and uHealth 6, 8 (Aug. 2018), e9691. https://doi.org/10.2196/mhealth.9691

[54]

Sohrab Saeb, Mi Zhang, Christopher J. Karr, Stephen M. Schueller, Marya E. Corden, Konrad P. Kording, and David C. Mohr. 2015. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: An exploratory study. Journal of Medical Internet Research 17, 7 (2015), 1--11. https://doi.org/10.2196/jmir.4273

[55]

Asif Salekin, Jeremy W Eberle, Jeffrey J Glenn, Bethany A Teachman, and John A Stankovic. 2018. A Weakly Supervised Learning Framework For Detecting Social Anxiety And Depression. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 2 (2018), 26.

Digital Library

[56]

Yasaman S. Sefidgar, Woosuk Seo, Kevin S. Kuehn, Tim Althoff, Anne Browning, Eve Riskin, Paula S. Nurius, Anind K. Dey, and Jennifer Mankoff. 2019. Passively-sensed behavioral correlates of discrimination events in college students. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 114 (Nov 2019), 29 pages. https://doi.org/10.1145/3359216

Digital Library

[57]

Omar Shaikh, Hongxin Zhang, William Held, Michael Bernstein, and Diyi Yang. 2023. On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning. https://doi.org/10.48550/arXiv.2212.08061 arXiv:2212.08061 [cs].

[58]

Kumar Shridhar, Alessandro Stolfo, and Mrinmaya Sachan. 2023. Distilling reasoning capabilities into smaller language models. In Findings of the Association for Computational Linguistics: ACL 2023. 7059--7073.

[59]

Karan Singhal, Tao Tu, Juraj Gottweis, Rory Sayres, Ellery Wulczyn, Le Hou, Kevin Clark, Stephen Pfohl, Heather Cole-Lewis, Darlene Neal, Mike Schaekermann, Amy Wang, Mohamed Amin, Sami Lachgar, Philip Mansfield, Sushant Prakash, Bradley Green, Ewa Dominowska, Blaise Aguera y Arcas, Nenad Tomasev, Yun Liu, Renee Wong, Christopher Semturs, S. Sara Mahdavi, Joelle Barral, Dale Webster, Greg S. Corrado, Yossi Matias, Shekoofeh Azizi, Alan Karthikesalingam, and Vivek Natarajan. 2023. Towards Expert-Level Medical Question Answering with Large Language Models. http://arxiv.org/abs/2305.09617 arXiv:2305.09617 [cs].

[60]

Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford alpaca: An instruction-following llama model.

[61]

Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. LLaMA: Open and Efficient Foundation Language Models. http://arxiv.org/abs/2302.13971 arXiv:2302.13971 [cs].

[62]

Andreas Triantafyllidis, Haridimos Kondylakis, Konstantinos Votis, Dimitrios Tzovaras, Nicos Maglaveras, and Kazem Rahimi. 2019. Features, outcomes, and challenges in mobile health interventions for patients living with chronic diseases: A review of systematic reviews. International Journal of Medical Informatics 132 (Dec. 2019), 103984. https://doi.org/10.1016/j.ijmedinf.2019.103984

[63]

Fabian Wahle, Tobias Kowatsch, Elgar Fleisch, Michael Rufer, and Steffi Weidt. 2016. Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild. JMIR mHealth and uHealth 4, 3 (2016), e111. https://doi.org/10.2196/mhealth.5960

[64]

Rui Wang, Min S. H. Aung, Saeed Abdullah, Rachel Brian, Andrew T. Campbell, Tanzeem Choudhury, Marta Hauser, John Kane, Michael Merrill, Emily A. Scherer, Vincent W. S. Tseng, and Dror Ben-Zeev. 2016. CrossCheck: Toward passive sensing and detection of mental health changes in people with schizophrenia. Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (2016), 886--897. https://doi.org/10.1145/2971648.2971740

Digital Library

[65]

Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T. Campbell. 2014. StudentLife: Assessing mental health, academic performance and behavioral trends of college students using smartphones. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 3--14.

[66]

Rui Wang, Gabriella Harari, Peilin Hao, Xia Zhou, and Andrew T Campbell. 2015. SmartGPA: how smartphones can assess and predict academic performance of college students. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. 295--306.

Digital Library

[67]

Rui Wang, Weichen Wang, Alex daSilva, Jeremy F. Huckins, William M. Kelley, Todd F. Heatherton, and Andrew T. Campbell. 2018. Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 (2018), 1--26. https://doi.org/10.1145/3191775

Digital Library

[68]

Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171 (2022).

[69]

Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2022. Finetuned Language Models Are Zero-Shot Learners. http://arxiv.org/abs/2109.01652 arXiv:2109.01652 [cs].

[70]

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. http://arxiv.org/abs/2201.11903 arXiv:2201.11903 [cs].

[71]

Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie. 2023. PMC-LLaMA: Further Finetuning LLaMA on Medical Papers. http://arxiv.org/abs/2304.14454 arXiv:2304.14454 [cs].

[72]

Xuhai Xu, Prerna Chikersal, Janine M. Dutcher, Yasaman S. Sefidgar, Woosuk Seo, Michael J. Tumminia, Daniella K. Villalba, Sheldon Cohen, Kasey G. Creswell, J. David Creswell, Afsaneh Doryab, Paula S. Nurius, Eve Riskin, Anind K. Dey, and Jennifer Mankoff. 2021. Leveraging Collaborative-Filtering for Personalized Behavior Modeling: A Case Study of Depression Detection among College Students. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 1 (March 2021), 1--27. https://doi.org/10.1145/3448107

Digital Library

[73]

Xuhai Xu, Xin Liu, Han Zhang, Weichen Wang, Subigya Nepal, Yasaman Sefidgar, Woosuk Seo, Kevin S Kuehn, Jeremy F Huckins, Margaret E Morris, et al. 2023. GLOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 4 (2023), 1--34.

Digital Library

[74]

Xuhai Xu, Xin Liu, Han Zhang, Weichen Wang, Subigya Nepal, Yasaman Sefidgar, Woosuk Seo, Kevin S. Kuehn, Jeremy F. Huckins, Margaret E. Morris, Paula S. Nurius, Eve A. Riskin, Shwetak Patel, Tim Althoff, Andrew Campbell, Anind K. Dey, and Jennifer Mankoff. 2023. GLOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 4 (2023), 1--34. https://doi.org/10.1145/3569485

Digital Library

[75]

Xuhai Xu, Ebrahim Nemati, Korosh Vatanparvar, Viswam Nathan, Tousif Ahmed, Md Mahbubur Rahman, Daniel McCaffrey, Jilong Kuang, and Jun Alex Gao. 2021. Listen2Cough: Leveraging End-to-End Deep Learning Cough Detection Model to Enhance Lung Health Assessment Using Passively Sensed Audio. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 1 (March 2021), 1--22. https://doi.org/10.1145/3448124

Digital Library

[76]

Xuhai Xu, Han Zhang, Yasaman Sefidgar, Yiyi Ren, Xin Liu, Woosuk Seo, Jennifer Brown, Kevin Kuehn, Mike Merrill, Paula Nurius, Shwetak Patel, Tim Althoff, Margaret E Morris, Eve Riskin, Jennifer Mankoff, and Anind K Dey. 2022. GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track. 18.

[77]

Bohao Yang, Chen Tang, Kun Zhao, Chenghao Xiao, and Chenghua Lin. 2023. Effective distillation of table-based reasoning ability from llms. arXiv preprint arXiv:2309.13182 (2023).

[78]

Kailai Yang, Shaoxiong Ji, Tianlin Zhang, Qianqian Xie, and Sophia Ananiadou. 2023. On the Evaluations of ChatGPT and Emotion-enhanced Prompting for Mental Health Analysis. http://arxiv.org/abs/2304.03347

[79]

Kai-Ching Yeh, Jou-An Chi, Da-Chen Lian, and Shu-Kai Hsieh. 2023. Evaluating Interfaced LLM Bias. In Proceedings of the 35th Conference on Computational Linguistics and Speech Processing (ROCLING 2023), Jheng-Long Wu and Ming-Hsiang Su (Eds.). The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), Taipei City, Taiwan, 292--299. https://aclanthology.org/2023.rocling-1.37

[80]

Han Zhang, Margaret E. Morris, Paula S. Nurius, Kelly Mack, Jennifer Brown, Kevin S. Kuehn, Yasaman S. Sefidgar, Xuhai Xu, Eve A. Riskin, Anind K. Dey, and Jennifer Mankoff. 2022. Impact of Online Learning in the Context of COVID-19 on Undergraduates with Disabilities and Mental Health Concerns. ACM Transactions on Accessible Computing (July 2022), 3538514. https://doi.org/10.1145/3538514

Digital Library

[81]

Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, and Dacheng Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 (2023).

[82]

Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, and Ed Chi. 2023. Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. http://arxiv.org/abs/2205.10625 arXiv:2205.10625 [cs].

Cited By

Ferrara E(2024)Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and ChallengesSensors10.3390/s2415504524:15(5045)Online publication date: 4-Aug-2024
https://doi.org/10.3390/s24155045
Nepal SPillai ACampbell WMassachi THeinz MKunwar AChoi EXu XKuc JHuckins JHolden JPreum SDepp CJacobson NCzerwinski MGranholm ECampbell A(2024)MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling ExperiencesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997618:4(1-44)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699761
Hota AChatterjee SChakraborty S(2024)Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing DataACM Transactions on Intelligent Systems and Technology10.1145/3696461Online publication date: 20-Sep-2024
https://doi.org/10.1145/3696461
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From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 8, Issue 2

May 2024

1330 pages

EISSN:2474-9567

DOI:10.1145/3665317

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Copyright © 2024 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

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Published: 15 May 2024

Published in IMWUT Volume 8, Issue 2

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Cited By

Ferrara E(2024)Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and ChallengesSensors10.3390/s2415504524:15(5045)Online publication date: 4-Aug-2024
https://doi.org/10.3390/s24155045
Nepal SPillai ACampbell WMassachi THeinz MKunwar AChoi EXu XKuc JHuckins JHolden JPreum SDepp CJacobson NCzerwinski MGranholm ECampbell A(2024)MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling ExperiencesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997618:4(1-44)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699761
Hota AChatterjee SChakraborty S(2024)Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing DataACM Transactions on Intelligent Systems and Technology10.1145/3696461Online publication date: 20-Sep-2024
https://doi.org/10.1145/3696461
Hille MKühn SKempermann GBonhoeffer TLindenberger U(2024)From animal models to human individuality: Integrative approaches to the study of brain plasticityNeuron10.1016/j.neuron.2024.10.006112:21(3522-3541)Online publication date: Nov-2024
https://doi.org/10.1016/j.neuron.2024.10.006
Jo SLee JLee JOh MLee J(2024)Differential Sensing Approach as a Pattern‐based Discrimination for Biological SamplesChemistry – A European Journal10.1002/chem.20240287130:60Online publication date: 23-Oct-2024
https://doi.org/10.1002/chem.202402871

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