Exploratory Data Mining Techniques (Decision Tree Models) for Examining the Impact of Internet-Based Cognitive Behavioral Therapy for Tinnitus: Machine Learning Approach (Preprint)
Open accessAbstract
BACKGROUND: There is huge variability in the way that individuals with tinnitus respond to interventions. These experiential variations, together with a range of associated etiologies, contribute to tinnitus being a highly heterogeneous condition. Despite this heterogeneity, a “one size fits all” approach is taken when making management recommendations. Although there are various management approaches, not all are equally effective. Psychological approaches such as cognitive behavioral therapy have the most evidence base. Managing tinnitus is challenging due to the significant variations in tinnitus experiences and treatment successes. Tailored interventions based on individual tinnitus profiles may improve outcomes. Predictive models of treatment success are, however, lacking. OBJECTIVE: This study aimed to use exploratory data mining techniques (ie, decision tree models) to identify the variables associated with the treatment success of internet-based cognitive behavioral therapy (ICBT) for tinnitus. METHODS: Individuals (N=228) who underwent ICBT in 3 separate clinical trials were included in this analysis. The primary outcome variable was a reduction of 13 points in tinnitus severity, which was measured by using the Tinnitus Functional Index following the intervention. The predictor variables included demographic characteristics, tinnitus and hearing-related variables, and clinical factors (ie, anxiety, depression, insomnia, hyperacusis, hearing disability, cognitive function, and life satisfaction). Analyses were undertaken by using various exploratory machine learning algorithms to identify the most influencing variables. In total, 6 decision tree models were implemented, namely the classification and regression tree (CART), C5.0, GB, XGBoost, AdaBoost algorithm and random forest models. The Shapley additive explanations framework was applied to the two optimal decision tree models to determine relative predictor importance. RESULTS: Among the six decision tree models, the CART (accuracy: mean 70.7%, SD 2.4%; sensitivity: mean 74%, SD 5.5%; specificity: mean 64%, SD 3.7%; area under the receiver operating characteristic curve [AUC]: mean 0.69, SD 0.001) and gradient boosting (accuracy: mean 71.8%, SD 1.5%; sensitivity: mean 78.3%, SD 2.8%; specificity: 58.7%, SD 4.2%; AUC: mean 0.68, SD 0.02) models were found to be the best predictive models. Although the other models had acceptable accuracy (range 56.3%-66.7%) and sensitivity (range 68.6%-77.9%), they all had relatively weak specificity (range 31.1%-50%) and AUCs (range 0.52-0.62). A higher education level was the most influencing factor for ICBT outcomes. The CART decision tree model identified 3 participant groups who had at least an 85% success probability following the undertaking of ICBT. CONCLUSIONS: Decision tree models, especially the CART and gradient boosting models, appeared to be promising in predicting ICBT outcomes. Their predictive power may be improved by using larger sample sizes and including a wider range of predictive factors in future studies.
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Studies that ranked high for selective reporting bias tended to report only user satisfaction data and treatment adherence, despite their methods considering collection and assessment of psychiatric scale data and other effectiveness outcomes.
“The studies that ranked high for selective reporting bias tended to report only user satisfaction data and treatment adherence, despite their methods considering the collection and assessment of psychiatric scale data and other effectiveness outcomes (29, 76, 79, 85–88, 92).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [92]
Split into 2 atoms — 1 confirmed, 0 partially supported, 1 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe studies reporting on the use of ML algorithms found significant success in outcome prediction and feature detection to support online therapy delivery.
“Additionally, the studies reporting on the use of ML algorithms found significant success in outcome prediction and feature detection to support online therapy delivery (6, 19, 28, 29, 31, 33, 83, 85, 86, 92–94).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [92]
The Discussion (Predictors of ICBT Outcomes; page unavailable) identifies factors associated with outcomes of internet-based cognitive behavioral therapy for tinnitus. But the Results (Decision Tree Model Performance Evaluations; page unavailable) report AUCs of 0.52–0.69 and say none of the models was robust, so “significant success” in outcome prediction overstates this study’s findings. (LLM, conf=0.89)
Experimental — accuracy not yet measuredThe relevant studies highlighted the importance of analyzing the large datasets produced by online mental healthcare interventions and the efficiency and accuracy of AI algorithms, which in some cases were comparable to or better than human assessors.
“As such, the relevant studies highlighted the importance of analyzing the large datasets produced by online mental healthcare interventions and the efficiency and accuracy of AI algorithms which in some cases were comparable to or better than human assessors (6, 19, 28, 29, 31, 33, 83, 85, 86, 92–94).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [92]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredRodrigo et al. (2021 & 2022) identified higher education level (master's degree and above) as a strong predictor of treatment response applying a DL approach, noting that this result may also underly additional factors in the way that the studied treatments were delivered and accessed.
“As an example, the two studies by Rodrigo et al. (2021 & 2022) identified higher education level (master’s degree and above) as a strong predictor of treatment response applying a DL approach, noting that this result may also underly additional factors in the way that the studied treatments were delivered and accessed (92, 93).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [92]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredRodrigo et al. (2021) determined that education level (master's degree or higher, 88% chance of treatment success), baseline tinnitus severity, and depression and anxiety symptoms were predictors of treatment response with around 70% accuracy.
“Rodrigo et al. (2021) applied decision trees models and determined that education level (master’s degree or higher education level 88% chance of treatment success), baseline tinnitus severity, and depression and anxiety symptoms were predictors of treatment response with around 70% accuracy (92).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [92]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486 · see citing works
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works
The satisfaction with life scale
Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985) · Journal of Personality Assessment · doi:10.1207/s15327752jpa4901_13 · Cited by 31,768 · see citing works
A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7
Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006) · Archives of Internal Medicine · doi:10.1001/archinte.166.10.1092 · Cited by 31,765 · see citing works
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,834 · see citing works
(1992) · Cited by 23,431 · see citing works
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325 · see citing works
An Introduction to Statistical Learning
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013) · Springer Texts in Statistics · doi:10.1007/978-1-4614-7138-7 · Cited by 12,106 · see citing works
From local explanations to global understanding with explainable AI for trees
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551 · see citing works
Spitzer, R. L. (1999) · JAMA · doi:10.1001/jama.282.18.1737 · Cited by 9,802 · see citing works
Validation of the Insomnia Severity Index as an outcome measure for insomnia research
Bastien, C. (2001) · Sleep Medicine · doi:10.1016/s1389-9457(00)00065-4 · Cited by 8,067 · see citing works
The random subspace method for constructing decision forests
Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 6,874 · see citing works
The Cognitive Failures Questionnaire (CFQ) and its correlates.
Broadbent, D., Cooper, P. F., Fitzgerald, P. H., & Parkes, K. (1982) · British Journal of Clinical Psychology · doi:10.1111/j.2044-8260.1982.tb01421.x · Cited by 2,967 · see citing works
The Tinnitus Functional Index: Development of a New Clinical Measure for Chronic, Intrusive Tinnitus
Meikle, M. B., Henry, J. A., Griest, S. E., Stewart, B. J., Abrams, H. B., McArdle, R., Myers, P. J., Newman, C. W., Sandridge, S., Turk, D. C., et al. (2012) · Ear & Hearing · doi:10.1097/aud.0b013e31822f67c0 · Cited by 792 · see citing works
The COVID-19 pandemic: the ‘black swan’for mental health care and a turning point for e-health
Wind, T. R., Rijkeboer, M., Andersson, G., & Riper, H. (2020) · Internet Interventions · doi:10.1016/j.invent.2020.100317 · Cited by 782 · see citing works
Self-citationA systematic review of the reporting of tinnitus prevalence and severity
McCormack, A., Edmondson-Jones, M., Somerset, S., & Hall, D. (2016) · Hearing Research · doi:10.1016/j.heares.2016.05.009 · Cited by 715 · see citing works
Psychometric Normalization of a Hyperacusis Questionnaire
Khalfa, S., Dubal, S., Veuillet, E., Perez-Diaz, F., Jouvent, R., & Collet, L. (2002) · ORL · doi:10.1159/000067570 · Cited by 488 · see citing works
Ayyadevara, V. K. (2018) · Apress eBooks · doi:10.1007/978-1-4842-3564-5_6 · Cited by 479 · see citing works
Cognitive behavioural therapy for tinnitus
Martinez-Devesa, P., Perera, R., Theodoulou, M., & Waddell, A. (2010) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd005233.pub3 · Cited by 477 · see citing works
(2012) · Ear and Hearing · doi:10.1097/aud.0b013e3182597b3e · Cited by 434 · see citing works
Analysis of the Prevalence and Associated Risk Factors of Tinnitus in Adults
Kim, H., Lee, H., An, S.-Y., Sim, S., Park, B., Kim, S. W., Lee, J. S., Hong, S. K., & Choi, H. G. (2015) · PLoS ONE · doi:10.1371/journal.pone.0127578 · Cited by 336 · see citing works
Andersson, G. (2002) · Psychosomatic Medicine · doi:10.1097/01.psy.0000031577.42041.f8 · Cited by 312 · cited 1× · see citing works
“To overcome this barrier, internet-based CBT (ICBT) was developed in the late 1990s [12].”
Self-citationTest-Retest Reliability of the Hearing Handicap Inventory for Adults
Newman, C. W., Weinstein, B. E., Jacobson, G. P., & Hug, G. A. (1991) · Ear and Hearing · doi:10.1097/00003446-199110000-00009 · Cited by 229 · see citing works
Features and functionality of the Iterapi platform for internet-based psychological treatment
Vlaescu, G., Alasjö, A., Miloff, A., Carlbring, P., & Andersson, G. (2016) · Internet Interventions · doi:10.1016/j.invent.2016.09.006 · Cited by 229 · see citing works
Self-citationJames, G. M., Witten, D., Hastie, T. J., & Tibshirani, R. J. (2013) · Springer texts in statistics · doi:10.1007/978-1-4614-7138-7_1 · Cited by 215 · see citing works
Cognitive behavioural therapy for tinnitus
Fuller, T., Cima, R., Langguth, B., Mazurek, B., Vlaeyen, J. W., & Hoare, D. J. (2020) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd012614.pub2 · Cited by 212 · see citing works
Depression in Patients with Tinnitus: A Systematic Review
Salazar, J. W., Meisel, K., Smith, E. R., Quiggle, A., McCoy, D. B., & Amans, M. R. (2019) · Otolaryngology–Head and Neck Surgery · doi:10.1177/0194599819835178 · Cited by 167 · cited 1× · see citing works
“Moreover, distressing tinnitus is often associated with an increased risk of anxiety and depression [7,8].”
A systematic review and meta-analysis of psychological functioning in chronic tinnitus
Trevis, K. J., McLachlan, N. M., & Wilson, S. J. (2017) · Clinical Psychology Review · doi:10.1016/j.cpr.2017.12.006 · Cited by 151 · see citing works
Clinical Practice Guideline: Tinnitus Executive Summary
Tunkel, D. E., Bauer, C. A., Sun, G. H., Rosenfeld, R. M., Chandrasekhar, S. S., Cunningham, E. R., Archer, S. M., Blakley, B. W., Carter, J. M., Granieri, E. C., et al. (2014) · Otolaryngology · doi:10.1177/0194599814547475 · Cited by 107 · see citing works
Beukes, E. W., Baguley, D., Allen, P. M., Manchaiah, V. K. C., & Andersson, G. (2017) · Ear and Hearing · doi:10.1097/aud.0000000000000505 · Cited by 106 · see citing works
Self-citationChanges in Tinnitus Experiences During the COVID-19 Pandemic
Beukes, E. W., Baguley, D., Jacquemin, L., Lourenco, M. P. C. G., Allen, P. M., Onozuka, J., Stockdale, D., Kaldo, V., Andersson, G., & Manchaiah, V. K. C. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.592878 · Cited by 101 · see citing works
Self-citationMeikle, M. B., Stewart, B. J., Griest, S. E., & Henry, J. A. (2008) · Trends in Amplification · doi:10.1177/1084713808319943 · Cited by 99 · see citing works
Beukes, E. W., Andersson, G., Allen, P. M., Manchaiah, V. K. C., & Baguley, D. (2018) · JAMA Otolaryngology–Head & Neck Surgery · doi:10.1001/jamaoto.2018.2238 · Cited by 88 · see citing works
Self-citationLandry, E. C., Sandoval, X. C. R., Simeone, C. N., Tidball, G., Lea, J., & Westerberg, B. D. (2020) · Otology & Neurotology · doi:10.1097/mao.0000000000002472 · Cited by 82 · see citing works
Beukes, E. W., Manchaiah, V., Allen, P. M., Baguley, D. M., & Andersson, G. (2019) · Trends in Hearing · doi:10.1177/2331216519851749 · Cited by 75 · cited 1× · see citing works
“The feasibility and efficacy of such an approach have been demonstrated among several populations in Sweden, Germany, Australia, the United Kingdom [14], and, more recently, the United States [15,16].”
Self-citationBoehmke, B., & Greenwell, B. (2019) · Chapman & Hall/CRC eBooks · doi:10.1201/9780367816377-11 · Cited by 65 · see citing works
Exploring tinnitus heterogeneity
(2021) · Progress in Brain Research · doi:10.1016/bs.pbr.2020.05.022 · Cited by 55 · see citing works
Self-citationManchaiah, V. K. C., Beukes, E. W., Granberg, S., Durisala, N., Baguley, D., Allen, P. M., & Andersson, G. (2017) · Journal of the American Academy of Audiology · doi:10.3766/jaaa.17094 · Cited by 50 · see citing works
Self-citationObjective measurement of tinnitus using functional near-infrared spectroscopy and machine learning
Shoushtarian, M., Alizadehsani, R., Khosravi, A., Acevedo, N., McKay, C. M., Nahavandi, S., & Fallon, J. B. (2020) · PLoS ONE · doi:10.1371/journal.pone.0241695 · Cited by 50 · see citing works
W Beukes, E., Andersson, G., Fagelson, M., & Manchaiah, V. (2022) · Journal of Medical Internet Research · doi:10.2196/27584 · Cited by 50 · see citing works
Self-citationDevelopment and technical functionality of an Internet-based intervention for tinnitus in the UK
Beukes, E. W., Vlaescu, G., Manchaiah, V. K. C., Baguley, D., Allen, P. M., Kaldo, V., & Andersson, G. (2016) · Internet Interventions · doi:10.1016/j.invent.2016.08.002 · Cited by 49 · see citing works
Self-citationSituationally influenced tinnitus coping strategies: a mixed methods approach
Beukes, E. W., Manchaiah, V. K. C., Andersson, G., Allen, P. M., Terlizzi, P. M., & Baguley, D. (2017) · Disability and Rehabilitation · doi:10.1080/09638288.2017.1362708 · Cited by 49 · see citing works
Self-citationLong-Term Efficacy of Audiologist-Guided Internet-Based Cognitive Behavior Therapy for Tinnitus
Beukes, E. W., Allen, P. M., Baguley, D., Manchaiah, V. K. C., & Andersson, G. (2018) · American Journal of Audiology · doi:10.1044/2018_aja-imia3-18-0004 · Cited by 46 · see citing works
Self-citationInternet-Based Intervention for Tinnitus: Outcome of a Single-Group Open Trial
Beukes, E. W., Allen, P. M., Manchaiah, V. K. C., Baguley, D., & Andersson, G. (2017) · Journal of the American Academy of Audiology · doi:10.3766/jaaa.16055 · Cited by 43 · see citing works
Self-citationBeukes, E. W., Andersson, G., Fagelson, M. A., & Manchaiah, V. (2021) · Internet Interventions · doi:10.1016/j.invent.2021.100402 · Cited by 37 · see citing works
Self-citationBeukes, E. W., Andersson, G., Fagelson, M., & Manchaiah, V. (2021) · American Journal of Audiology · doi:10.1044/2021_aja-20-00222 · Cited by 34 · see citing works
Self-citationCognitive Behavioral Therapy for Tinnitus.
Woods, J. W., & Theodoroff, S. M. (2019) · PubMed · Cited by 28 · see citing works
Coping With Tinnitus During the COVID-19 Pandemic.
Beukes, E., Onozuka, J., Brazell, T. P., & Manchaiah, V. (2021) · American Journal of Audiology · doi:10.1044/2021_aja-20-00188 · Cited by 26 · see citing works
Self-citationNiemann, U., Boecking, B., Brüeggemann, P., Mebus, W., Mazurek, B., & Spiliopoulou, M. (2020) · PLoS ONE · doi:10.1371/journal.pone.0228037 · Cited by 25 · see citing works
Beukes, E., Fagelson, M. A., Aronson, E., Muñoz, M., Andersson, G., & Manchaiah, V. (2020) · American Journal of Audiology · doi:10.1044/2019_aja-19-00014 · Cited by 25 · see citing works
Self-citationManchaiah, V., Vlaescu, G., Varadaraj, S., Aronson, E., Fagelson, M. A., Muñoz, M., Andersson, G., & Beukes, E. (2020) · American Journal of Audiology · doi:10.1044/2020_aja-20-00002 · Cited by 25 · see citing works
Self-citationDisease Profiling for Computerized Peer Support of Ménière's Disease
Rasku, J., Pyykkö, I. V., Levo, H., Kentala, E. L., & Manchaiah, V. K. C. (2015) · JMIR Rehabilitation and Assistive Technologies · doi:10.2196/rehab.4109 · Cited by 23 · see citing works
Self-citationMcCormack, A., Edmondson‐Jones, M., Somerset, S., & Hall, D. A. (2016) · Hearing Research · doi:10.1016/j.heares.2016.08.010 · Cited by 15 · see citing works
Risk Factors for Tinnitus in 37,692 Patients Followed in General Practices in Germany
Kostev, K., Alymova, S., Kössl, M., & Jacob, L. (2019) · Otology & Neurotology · doi:10.1097/mao.0000000000002161 · Cited by 15 · see citing works
Rodrigo, H., Beukes, E. W., Andersson, G., & Manchaiah, V. K. C. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-049384 · Cited by 11 · see citing works
Self-citationWeininger, O., Warnecke, A., Lesinski‐Schiedat, A., Lenarz, T., & Stolle, S. (2019) · Audiology Research · doi:10.4081/audiores.2019.230 · Cited by 8 · see citing works
The Association Between Tinnitus and Mental Illnesses
Adeyi, A., & Taiwo, J. (2013) · InTech eBooks · doi:10.5772/52755 · Cited by 7 · see citing works
Beukes, E. W., Andersson, G., Fagelson, M. A., et al. (2021) · doi:10.2196/preprints.27584 · Cited by 5 · see citing works
Self-citationApplication of artificial intelligence in audiology
Juhola, M., Viikki, K., Laurikkala, J. P. S., Auramo, Y., Kentala, E. L., & Pyykkö, I. V. (2001) · Scandinavian Audiology · doi:10.1080/010503901300007209 · Cited by 3 · see citing works
Fox, J. (1991) · Sage eBooks · doi:10.4135/9781412985604.n3 · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 63 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [46]Lundberg SM Lee SI A unified approach to interpreting model predictions 2017 12 The 31st International Conference on Neural Information Processing Systems December 4-9, 2017 Long Beach, California, USA 4768 4777
Cited by
Sorted by citations
Kasraei, B., Schmidt, M., Zhang, J., Bulmer, C., Filatow, D., Arbor, A., Pennell, T., & Heung, B. (2024) · Geoderma · doi:10.1016/j.geoderma.2024.116873 · Cited by 78 · see citing works
Gutierrez, G., Stephenson, C., Eadie, J., Asadpour, K., & Alavi, N. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1356773 · Cited by 71 · see citing works
Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions
Frosolini, A., Franz, L., Caragli, V., Genovese, E., de Filippis, C., & Marioni, G. (2024) · Sensors · doi:10.3390/s24227126 · Cited by 68 · see citing works
Wu, T., Wei, Y., Wu, J.-B., Yi, B., & Li, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-28421-6 · Cited by 35 · see citing works
Hyperacusis: Loudness intolerance, fear, annoyance and pain
Salvi, R., Chen, G., & Manohar, S. (2022) · Hearing Research · doi:10.1016/j.heares.2022.108648 · Cited by 32 · see citing works
Yin, Z., Kuang, Z., Zhang, H., Guo, Y., Li, T., Wu, Z., & Wang, L. (2024) · JMIR Medical Informatics · doi:10.2196/57678 · Cited by 14 · see citing works
Aazh, H. (2025) · Brain Sciences · doi:10.3390/brainsci15050526 · Cited by 13 · see citing works
Cardon, E., Jacquemin, L., Schecklmann, M., Langguth, B., Mertens, G., Vanderveken, O. M., Lammers, M. J. W., de Heyning, P. V., Rompaey, V. V., & Gilles, A. (2022) · Ear and Hearing · doi:10.1097/aud.0000000000001246 · Cited by 11 · see citing works
Rodrigo, H., Beukes, E. W., Andersson, G., & Manchaiah, V. (2022) · American Journal of Audiology · doi:10.1044/2022_aja-21-00270 · Cited by 9 · see citing works
Self-citationZhou, Y., Zhang, Z., Li, Q., Mao, G., & Zhou, Z. (2024) · BMC Psychology · doi:10.1186/s40359-024-01696-8 · Cited by 11 · see citing works
Vlaescu, G., Carlbring, P., & Andersson, G. (2026) · Internet Interventions · doi:10.1016/j.invent.2026.100955 · Cited by 8 · see citing works
Self-citationYang, T., Chen, Y.-F., Cheng, Y., Huang, J.-N., Wu, C.-S., & Chu, Y.-C. (2023) · BioData Mining · doi:10.1186/s13040-023-00351-z · Cited by 7 · see citing works
Machine learning-based gait adaptation dysfunction identification using CMill-based gait data
Yang, H., Liao, Z., Zou, H., Li, K., Zhou, Y., Gao, Z., Mao, Y., & Song, C. (2024) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2024.1421401 · Cited by 6 · see citing works
Towards precision medicine for otology and neurotology: Machine learning applications and challenges
Adcock, K. S., Arulchelvan, E., Shields, N., & Vanneste, S. (2025) · Hearing Research · doi:10.1016/j.heares.2025.109473 · Cited by 6 · see citing works
Balan, J. R., Rodrigo, H., Saxena, U., & Mishra, S. K. (2023) · The Journal of the Acoustical Society of America · doi:10.1121/10.0021303 · Cited by 6 · see citing works
Self-citationUnderstanding digital therapeutics in disease self-management: A systematic literature review
Yang, L., Zhang, M., Jia, L., Yan, Z., & Yin, Q. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.102831 · Cited by 7 · see citing works
Examining the consequences of tinnitus using the multidimensional perspective
Manchaiah, V. K. C., Nisha, K. V., Prabhu, P., Granberg, S., Karlsson, E., Andersson, G., & Beukes, E. W. (2021) · Acta Oto-Laryngologica · doi:10.1080/00016489.2021.2019307 · Cited by 4 · see citing works
Self-citationJuxtaposing Medical Centers Using Different Questionnaires Through Score Predictors
Puga, C., Schleicher, M., Niemann, U., Unnikrishnan, V., Boecking, B., Brüeggemann, P., Simoes, J. P., Langguth, B., Schlee, W., Mazurek, B., et al. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.818686 · Cited by 4 · see citing works
Artificial intelligence-based psychotherapy: focusing on common psychotherapeutic factors
Giotakos, O. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1710715 · Cited by 3 · see citing works
Mental Health Analysis During Pandemic: A Survey of Detection and Treatment
Upama, P. B., Valero, M., Shahriar, H., Syam, M., & Ahamed, S. I. (2023) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac57700.2023.00236 · Cited by 3 · see citing works
Research trends and hotspots of cognitive behavioral therapy for tinnitus: a bibliometric analysis
He, Y., Liu, J., Cheng, H., Ye, H., Li, C., Gao, Y., & Xu, X. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1536224 · Cited by 2 · see citing works
Kokthi, E., & Guri, F. (2025) · Frontiers in Sustainability · doi:10.3389/frsus.2025.1717100 · Cited by 2 · see citing works
Data mining for prediction and interpretation of bacterial population behavior in food
Hosoe, J., Sunagawa, J., Nakaoka, S., Koseki, S., & Koyama, K. (2022) · Frontiers in Food Science and Technology · doi:10.3389/frfst.2022.979028 · Cited by 1 · see citing works
Modelling Tinnitus Functional Index reduction using supervised machine learning algorithms
Agyemang, E. F. (2024) · Statistics in Transition New Series · doi:10.59139/stattrans-2024-003 · Cited by 1 · see citing works
Zhou, Y., Zhang, Z., Li, Q., Mao, G., & Zhou, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26711735
Hasani, A., Zoto, O., Kuliçi, M., Gashi, N., & Salihu, S. (2026) · Foods · doi:10.3390/foods15020334
Sung, J., Zainal, N. H., Greenberg, J. L., Curtiss, J. E., Jacoby, R. J., Weingarden, H. M., & Wilhelm, S. (2026) · Cognitive Therapy and Research · doi:10.1007/s10608-025-10706-x
Demoen, S., Kerchove, E. V., Timmermans, A. A. A., Rompaey, V. V., Michiels, S., & Gilles, A. (2026) · Journal of Medical Internet Research · doi:10.2196/83529
周, 迎. (2026) · Advances in Clinical Medicine · doi:10.12677/acm.2026.1631084
Engelke, M., Langguth, B., Probst, T., Pryss, R., Schoisswohl, S., Simões, J. P., ten Klooster, P. M., Tödtli, B., Vogel, C., & Schlee, W. (2026) · Internet Interventions · doi:10.1016/j.invent.2026.100943
Viola, P., Aragona, T., Luca, P. D., Gioacchini, F. M., Scarpa, A., Carvelli, S., Petrolo, C., Mussari, R., & Chiarella, G. (2026) · Medicina · doi:10.3390/medicina62101895
THE IMPACT OF ARTIFICIAL INTELLIGENCE ON HEARING HEALTHCARE: DIAGNOSTICS AND THERAPEUTIC INNOVATIONS
Balińska, W., Marek, A., Rogowska, J., & Wąsik, J. (2026) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.4(52).2026.6531
- Authors
- Rodrigo, H., Beukes, E. W., Andersson, G., & Manchaiah, V.
- Affiliations
- The University of Texas Rio Grande Valley (US)
- Linköping University (SE)
- Manipal Academy of Higher Education (IN)
- Corresponding author
- Rodrigo, Hansapani
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2021-03-21
- Volume
- 23
- Issue
- 11
- Pages
- e28999-e28999
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC8596228
- OpenAlex
- W3201242635
- Cited by
- 33
- References
- 63
- Citations by year
- 2026: 8 · 2025: 6 · 2024: 9 · 2023: 4 · 2022: 5 · 2021: 1
- FWCI
- 2.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley) (submittedVersion, cc-by)
- Anglia Ruskin Research Online (Anglia Ruskin University) (submittedVersion)
- UpSpace Institutional Repository (University of Pretoria) (submittedVersion, cc-by)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by-nc-nd)
- Funders
- Anglia Ruskin University
- Topics
- Life Sciences › Neuroscience › Sensory Systems › Hearing, Cochlea, Tinnitus, Genetics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › Speech and Hearing › Noise Effects and Management
- Keywords
- tinnitus, internet-based cognitive behavioral therapy, treatment response prediction, tinnitus severity, Tinnitus Functional Index, decision tree, SHAP, predictor importance, hyperacusis, hearing handicap, anxiety, depression, insomnia, life satisfaction, cognitive function, personalized treatment, treatment effect heterogeneity, gradient boosting, random forest, XGBoost, AdaBoost, C5.0
- MeSH terms
- Machine Learning, Decision Trees, Humans, Tinnitus, Cognitive Behavioral Therapy, Internet, Data Mining
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