Care for the Mind amid Chronic Diseases: An Interpretable AI Approach Using IoT

Published Online:https://doi.org/10.1287/mnsc.2023.04183

References

  • AHQR (2022) Healthcare expenditures for treatment of mental disorders: Estimates for adults ages 18 and older, U.S. civilian noninstitutionalized population, 2019. Accessed August 2, 2025, https://meps.ahrq.gov/data_files/publications/st539/stat539.pdf?utm_source=chatgpt.com.Google Scholar
  • Ansah JP, Chiu CT (2023) Projecting the chronic disease burden among the adult population in the united states using a multi-state population model. Frontiers Public Health 10:1082183.CrossrefGoogle Scholar
  • APA (2021) The economic cost of depression is increasing; direct costs are only a small part. Accessed August 2, 2025, https://www.psychiatry.org/news-room/apa-blogs/the-economic-cost-of-depression-is-increasing?utm_source=chatgpt.com.Google Scholar
  • Bajinka O (2022) Chronic illness and mental health: Recognizing and treating depression. Res. Chronic Diseases 6(4):86–89.Google Scholar
  • Bardhan I, Chen H, Karahanna E (2020) Connecting systems, data, and people: A multidisciplinary research roadmap for chronic disease management. MIS Quart. 44(1):185–200.CrossrefGoogle Scholar
  • Bertens LC, Broekhuizen BD, Naaktgeboren CA, Rutten FH, Hoes AW, van Mourik Y, Moons KG, et al. (2013) Use of expert panels to define the reference standard in diagnostic research: A systematic review of published methods and reporting. PLoS Medicine 10(10):e1001531.CrossrefGoogle Scholar
  • Bockting CL, Hollon SD, Jarrett RB, Kuyken W, Dobson K (2015) A lifetime approach to major depressive disorder: The contributions of psychological interventions in preventing relapse and recurrence. Clin. Psych. Rev. 41:16–26.CrossrefGoogle Scholar
  • Bot BM, Suver C, Neto EC, Kellen M, Klein A, Bare C, Doerr M, et al. (2016) The mPower study, Parkinson disease mobile data collected using ResearchKit. Sci. Data 3(1):1–9.CrossrefGoogle Scholar
  • Britannica (2023) U.S. States ranked by population: Which is largest? Accessed August 16, 2024, https://www.britannica.com/topic/largest-U-S-state-by-population.Google Scholar
  • Canzian L, Musolesi M (2015) Trajectories of depression: Unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. Proc. ACM Internat. Joint Conf. Pervasive Ubiquitous Comput. (ACM, New York).Google Scholar
  • Caruana R, Lou Y, Gehrke J, Koch P, Sturm M, Elhadad N (2015) Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. Proc. KDD (ACM, New York).Google Scholar
  • CDC (2012) Mental health and chronic diseases CDC fact sheet. Technical report, Centers for Disease Control and Prevention, Atlanta, GA.Google Scholar
  • CDC (2021) U.S. healthcare spending attributable to cigarette smoking in 2014. Technical report, Centers for Disease Control and Prevention, Atlanta, GA.Google Scholar
  • CDC (2022) Chronic diseases in America. Technical report, Centers for Disease Control and Prevention, Atlanta, GA.Google Scholar
  • Chau M, Li TM, Wong PW, Xu JJ, Yip PS, Chen H (2020) Finding people with emotional distress in online social media: A design combining machine learning and rule-based classification. MIS Quart. 44(2):933–955.Google Scholar
  • Chen C, Li O, Tao C, Barnett AJ, Su J, Rudin C (2019) This looks like that: Deep learning for interpretable image recognition. Proc. 33rd Internat. Conf. Neural Inform. Processing Systems (ACM, New York).Google Scholar
  • Cho K, Van Merriënboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: Encoder-decoder approaches. Proc. SSST-8, Eighth Workshop Syntax, Semantics Structure Statistical Translation (Association for Computational Linguistics, Doha, Qatar), 103–111.Google Scholar
  • Coelln R, Dawe RJ, Leurgans SE, Curran TA, Truty T, Yu L, Barnes LL, et al. (2019) Quantitative mobility metrics from a wearable sensor predict incident parkinsonism in older adults. Parkinsonism Related Disorders 65:190–196.CrossrefGoogle Scholar
  • Czech MD, Patel S (2019) GaitPy: An open-source Python package for gait analysis using an accelerometer on the lower back. J. Open Source Software 4(43):1778.CrossrefGoogle Scholar
  • Dattani S, Ritchie H, Roser M (2021) Mental health. Our World in Data.Google Scholar
  • Dixon-Woods M, Redwood S, Leslie M, Minion J, Martin GP, Coleman JJ (2013) Improving quality and safety of care using “technovigilance”: An ethnographic case study of secondary use of data from an electronic prescribing and decision support system. Milbank Quart. 91(3):424–454.CrossrefGoogle Scholar
  • Farhan AA, Yue C, Morillo R, Ware S, Lu J, Bi J, Kamath J, et al. (2016) Behavior vs. introspection: Refining prediction of clinical depression via smartphone sensing data. Proc. IEEE Wireless Health.Google Scholar
  • Fawaz H, Lucas B, Forestier G, Pelletier C, Schmidt DF, Weber J, Webb GI, et al. (2020) Inceptiontime: Finding Alexnet for time series classification. Data Mining Knowledge Discovery 34(6):1936–1962.CrossrefGoogle Scholar
  • Forbes (2022) Our nation’s chronic disease epidemic is getting worse so, who’s responsible? Forbes (November 22), https://www.forbes.com/sites/ritanumerof/2022/11/22/our-nations-chronic-disease-epidemic-is-getting-worse-so-whos-responsible/.Google Scholar
  • Gaitpy (2024) gaitpy—Pypi.org. Accessed August 16, 2024. https://pypi.org/project/gaitpy/.Google Scholar
  • Gao R, Huo Y, Bao S, Tang Y, Antic SL, Epstein ES, Balar AB, et al. (2019) Distanced LSTM: Time-distanced gates in long short-term memory models for lung cancer detection. Suk HI, Liu M, Yan P, Lian C, eds. Internat. Workshop Machine Learn. Medical Imaging (Springer, Cham, Switzerland), 310–318.CrossrefGoogle Scholar
  • Gee AH, Garcia-Olano D, Ghosh J, Paydarfar D (2019) Explaining deep classification of time-series data with learned prototypes. CEUR Workshop Proc. (NIH Public Access).Google Scholar
  • Ghosal GR, Abbasi-Asl R (2021) Multi-modal prototype learning for interpretable multivariable time series classification. Preprint, submitted June 17, https://arxiv.org/abs/2106.09636.Google Scholar
  • Hedman J, Srinivasan N, Lindgren R (2013) Digital traces of information systems: Sociomateriality made researchable. Proc. 34th Internat. Conf. Inform. Systems (Association for Information Systems, Atlanta), 1069.Google Scholar
  • Hoffman D (2022) Commentary on Chronic Disease Prevention in the US in 2022. chronicdisease.org. Accessed August 16, 2024, https://chronicdisease.org/wp-content/uploads/2022/04/FS_ChronicDiseaseCommentary2022FINAL.pdf.Google Scholar
  • Hubble RP, Naughton GA, Silburn PA, Cole MH (2015) Wearable sensor use for assessing standing balance and walking stability in people with Parkinson’s disease: A systematic review. PLoS One 10(4):e0123705.CrossrefGoogle Scholar
  • Jacobson NC, Chung YJ (2020) Passive sensing of prediction of moment-to-moment depressed mood among undergraduates with clinical levels of depression sample using smartphones. Sensors 20(12):3572.CrossrefGoogle Scholar
  • Jian JY, Bisantz AM, Drury CG (2000) Foundations for an empirically determined scale of trust in automated systems. Internat. J. Cognitive Ergonomics 4(1):53–71.CrossrefGoogle Scholar
  • Katon WJ, Lin EH, Von Korff M, Ciechanowski P, Ludman EJ, Young B, Peterson D, et al. (2010) Collaborative care for patients with depression and chronic illnesses. New England J. Medicine 363(27):2611–2620.CrossrefGoogle Scholar
  • Kaur H, Nori H, Jenkins S, Caruana R, Wallach H, Wortman Vaughan J (2020) Interpreting interpretability: Understanding data scientists’ use of interpretability tools for machine learning. Proc. CHI Conf. Human Factors Comput. Systems (ACM, New York), 1–14.Google Scholar
  • Kim BR, Srinivasan K, Kong SH, Kim JH, Shin CS, Ram S (2023) ROLEX: A novel method for interpretable machine learning using robust local explanations. MIS Quart. 47(3):1303–1332.CrossrefGoogle Scholar
  • LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc. IEEE 86(11):2278–2324.CrossrefGoogle Scholar
  • Lee DD, Cheng ZZ, Mao C, Manzoor E (2025) Guided diverse concept miner (GDCM): Uncovering relevant constructs for managerial insights from text. Inform. Systems Res. 36(1):370–393.Google Scholar
  • Lemke MR, Wendorff T, Mieth B, Buhl K, Linnemann M (2000) Spatiotemporal gait patterns during over ground locomotion in major depression compared with healthy controls. J. Psychiatric Res. 34(4–5):277–283.CrossrefGoogle Scholar
  • Li W, Zhu W, Dorsey ER, Luo J (2020) Predicting Parkinson’s disease with multimodal irregularly collected longitudinal smartphone data. 2020 IEEE Internat. Conf. Data Mining (ICDM) (IEEE, Piscataway, NJ), 1106–1111.Google Scholar
  • Lin YK, Fang X (2021) First, do no harm: Predictive analytics to reduce in-hospital adverse events. J. Management Inform. Systems 38(4):1122–1149.CrossrefGoogle Scholar
  • Liu CW, Wang W, Gao G, Agarwal R (2024) The value of virtual engagement: Evidence from a running platform. Management Sci. 70(9):6179–6201.AbstractGoogle Scholar
  • Liu J, Zhang T, Han G, Gou Y (2018) TD-LSTM: Temporal dependence-based LSTM networks for marine temperature prediction. Sensors 18(11):3797.CrossrefGoogle Scholar
  • Liu X, Zhang B, Susarla A, Padman R (2020) Go to Youtube and call me in the morning: Use of social media for chronic conditions. MIS Quart. 44(1):257–283.CrossrefGoogle Scholar
  • Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Adv. Neural Inform. Processing Systems, vol. 30 (ACM, New York).Google Scholar
  • Luo W, Li Y, Urtasun R, Zemel R (2016) Understanding the effective receptive field in deep convolutional neural networks. Proc. 30th Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 4905–4913.Google Scholar
  • Ma D, Wang Z, Xie J, Guo B, Yu Z (2020) Interpretable multivariate time series classification based on prototype learning. Proc. 15th Internat. Conf. Green Pervasive Cloud Comput. (Springer, Cham, Switzerland).Google Scholar
  • Marsh L (2013) Depression and Parkinson’s disease: Current knowledge. Current Neurology Neurosci. Rep. 13(12):409–409.CrossrefGoogle Scholar
  • Mayo Clinic (2022) Depression (major depressive disorder): Symptoms and causes. Accessed October 14, https://www.mayoclinic.org/diseases-conditions/depression/symptoms-causes/syc-20356007.Google Scholar
  • Michalak J, Troje NF, Fischer J, Vollmar P, Heidenreich T, Schulte D (2009) Embodiment of sadness and depression-gait patterns associated with dysphoric mood. Psychosomatic Medicine 71(5):580–587.CrossrefGoogle Scholar
  • Miller T (2019) Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence 267:1–38.CrossrefGoogle Scholar
  • Ming Y, Xu P, Qu H, Ren L (2019) Interpretable and steerable sequence learning via prototypes. Proc. 25th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 903–913.Google Scholar
  • Molnar C (2020) Interpretable Machine Learning (Leanpub, Victoria, BC, Canada).Google Scholar
  • Moss L, Corsar D, Shaw M, Piper I, Hawthorne C (2022) Demystifying the black box: The importance of interpretability of predictive models in neurocritical care. Neurocritical Care 37(2):185–191.CrossrefGoogle Scholar
  • Murphy KP (2022) Probabilistic Machine Learning: An Introduction (MIT Press).Google Scholar
  • Nauta M, Bree R, Seifert C (2021) Neural prototype trees for interpretable fine-grained image recognition. Proc. IEEE Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 14933–14943.Google Scholar
  • NHS (2025) Symptoms - Depression in adults. Accessed October 14, 2025, https://www.nhs.uk/mental-health/conditions/depression-in-adults/symptoms/.Google Scholar
  • Osborne RH, Batterham RW, Elsworth GR, Hawkins M, Buchbinder R (2013) The grounded psychometric development and initial validation of the health literacy questionnaire (HLQ). BMC Public Health 13(1):1–17.CrossrefGoogle Scholar
  • Oung Q, Hariharan M, Lee H, Basah S, Sarillee M, Lee C (2015) Wearable multimodal sensors for evaluation of patients with Parkinson disease. 2015 IEEE Internat. Conf. Control System, Comput. Engrg. (ICCSCE) (IEEE, Piscataway, NJ), 269–274.Google Scholar
  • Padmanabhan B, Fang X, Sahoo N, Burton-Jones A (2022) Machine learning in information systems research. MIS Quart. 46(1):iii–xix.CrossrefGoogle Scholar
  • Rai A (2017) Editor’s comments: Diversity of design science research. MIS Quart. 41(1):iii–xviii.CrossrefGoogle Scholar
  • Reijnders JS, Ehrt U, Weber WE, Aarsland D, Leentjens AF (2008) A systematic review of prevalence studies of depression in Parkinson’s disease. Movement Disorders 23(2):183–189.CrossrefGoogle Scholar
  • Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Natural Machine Intelligence 1(5):206–215.CrossrefGoogle Scholar
  • Ruiz AP, Flynn M, Large J, Middlehurst M, Bagnall A (2021) The great multivariate time series classification bake off: A review and experimental evaluation of recent algorithmic advances. Data Mining Knowledge Discovery 35(2):401–449.CrossrefGoogle Scholar
  • Shim J, van den Dam R, Aiello S, Penttinen J, Sharda R, French A (2022) The transformative effect of 5g on business and society in the age of the fourth industrial revolution. Comm. Assoc. Inform. Systems 50(1):29.Google Scholar
  • Sigcha L, Costa N, Pavón I, Costa S, Arezes P, López JM, De Arcas G (2020) Deep learning approaches for detecting freezing of gait in Parkinson’s Disease patients through on-body acceleration sensors. Sensors 20(7):1895–1895.CrossrefGoogle Scholar
  • Simchi-Levi D (2020) From the editor: Diversity, equity, and inclusion in management science. Management Sci. 66(9):3802–3802.LinkGoogle Scholar
  • Sloman L, Berridge M, Homatidis S, Hunter D, Duck T (1982) Gait patterns of depressed patients and normal subjects. Amer. J. Psychiatry 139(1):94–97.CrossrefGoogle Scholar
  • Statista (2022) Topic: US smartphone market. Accessed October 14, 2025, https://www.statista.com/topics/2711/us-smartphone-market/?srsltid=AfmBOoqaLT214bbLeKFRkZZjoCeAmdQ-XwYiXmL1x8y9rRyBi45lBEhV.Google Scholar
  • Taylor HL, Menachemi N, Gilbert A, Chaudhary J, Blackburn J (2023) Economic burden associated with untreated mental illness in Indiana. JAMA Health Forum 4(10):e233535.Google Scholar
  • Trinh L, Tsang M, Rambhatla S, Liu Y (2021) Interpretable and trustworthy deepfake detection via dynamic prototypes. Proc. IEEE/CVF Winter Conf. Appl. Computer Vision (IEEE, Piscataway, NJ), 1973–1983.Google Scholar
  • Vancampfort D, Basangwa D, Kimbowa S, Firth J, Schuch F, Van Damme T, Mugisha J (2020) Test–retest reliability, validity, and correlates of the 2-min walk test in outpatients with depression. Physiotherapy Res. Internat. 25(2):e1821.CrossrefGoogle Scholar
  • Vicert (2021) Health apps usage statistics. Accessed August 16, 2024, https://www.vicert.com/blog/health-apps-usage-statistics/.Google Scholar
  • Wang B, Di Buccio E, Melucci M (2021) Word2Fun: Modelling words as functions for diachronic word representation. Adv. Neural Inform. Processing Systems 34:2861–2872.Google Scholar
  • Wang T, Yang J, Li Y, Wang B (2025) Partially interpretable estimators (pie): Black-box-refined interpretable machine learning. INFORMS J. Comput., ePub ahead of print July 7, https://doi.org/10.1287/ijoc.2022.0098.Google Scholar
  • Xie J, Chai Y, Liu X (2023) Unbox the blackbox: Predict and interpret YouTube viewership using deep learning. J. Management Inform. Systems 40(2):541–579.CrossrefGoogle Scholar
  • Xie J, Liu X, Zeng D, Fang X (2022) Understanding medication nonadherence from social media: A Sentiment-enriched deep learning approach. MIS Quart. 46(1):341–372.CrossrefGoogle Scholar
  • Xie J, Zhang Z, Liu X, Zeng D (2021a) Unveiling the hidden truth of drug addiction: A social media approach using similarity network-based deep learning. J. Management Inform. Systems 38(1):166–195.CrossrefGoogle Scholar
  • Xie J, Zhang B, Ma J, Zeng D, Lo-Ciganic J (2021b) Readmission prediction for patients with heterogeneous medical history: A trajectory-based deep learning approach. ACM Trans. Management Inform. Systems 13(2):1–27.CrossrefGoogle Scholar
  • Xu D, Ruan C, Körpeoglu E, Kumar S, Achan K (2020) Inductive representation learning on temporal graphs. Proc. 8th Internat. Conf. Learn. Representations (ICLR, Appleton, WI), 1–19.Google Scholar
  • Yu S, Chai Y, Chen H, Sherman SJ, Brown RA (2022) Wearable sensor-based chronic condition severity assessment: An adversarial attention-based deep multisource multitask learning approach. MIS Quart. 46(3), 1355–1394.Google Scholar
  • Zhang W, Ram S (2020) A comprehensive analysis of triggers and risk factors for asthma based on machine learning and large heterogeneous data sources. MIS Quart. 44(1):305–350.CrossrefGoogle Scholar
  • Zhang X, Gao Y, Lin J, Lu CT (2020a) Tapnet: Multivariate time series classification with attentional prototypical network. Proc. AAAI Conf. Artificial Intelligence. vol. 34, no. 4 (AAAI Press, Palo Alto, CA), 6845–6852.Google Scholar
  • Zhang H, Deng K, Li H, Albin RL, Guan Y (2020b) Deep learning identifies digital biomarkers for self-reported Parkinson’s disease. Patterns 1(3):100042.CrossrefGoogle Scholar
  • Zhang D, Zhou L, Tao J, Zhu T, Gao G (2025) Ketch: A knowledge-enhanced transformer-based approach to suicidal ideation detection from social media content. Inform. Systems Res. 36(1):572–599.LinkGoogle Scholar
  • Zhu H, Samtani S, Brown RA, Chen H (2021) A deep learning approach for recognizing activity of daily living (ADL) for senior care: Exploiting interaction dependency and temporal patterns. MIS Quart. 45(2):859–896.CrossrefGoogle Scholar
  • Zhu H, Samtani S, Chen H, Nunamaker JF Jr (2020) Human identification for activities of daily living: A deep transfer learning approach. J. Management Inform. Systems 37(2):457–483.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.