{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T04:37:17Z","timestamp":1780547837407,"version":"3.54.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T00:00:00Z","timestamp":1635379200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T00:00:00Z","timestamp":1635379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000002","name":"U.S. Department of Health & Human Services | National Institutes of Health","doi-asserted-by":"publisher","award":["5R01DC006859"],"award-info":[{"award-number":["5R01DC006859"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"United States Department of Defense | United States Navy | Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-17-1-2826"],"award-info":[{"award-number":["N00014-17-1-2826"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000055","name":"U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000055","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Digital health data are multimodal and high-dimensional. A patient\u2019s health state can be characterized by a multitude of signals including medical imaging, clinical variables, genome sequencing, conversations between clinicians and patients, and continuous signals from wearables, among others. This high volume, personalized data stream aggregated over patients\u2019 lives has spurred interest in developing new artificial intelligence (AI) models for higher-precision diagnosis, prognosis, and tracking. While the promise of these algorithms is undeniable, their dissemination and adoption have been slow, owing partially to unpredictable AI model performance once deployed in the real world. We posit that one of the rate-limiting factors in developing algorithms that generalize to real-world scenarios is the very attribute that makes the data exciting\u2014their high-dimensional nature. This paper considers how the large number of features in vast digital health data can challenge the development of robust AI models\u2014a phenomenon known as \u201cthe curse of dimensionality\u201d in statistical learning theory. We provide an overview of the curse of dimensionality in the context of digital health, demonstrate how it can negatively impact out-of-sample performance, and highlight important considerations for researchers and algorithm designers.<\/jats:p>","DOI":"10.1038\/s41746-021-00521-5","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T10:02:44Z","timestamp":1635415364000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":297,"title":["Digital medicine and the curse of dimensionality"],"prefix":"10.1038","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8804-8874","authenticated-orcid":false,"given":"Visar","family":"Berisha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5988-4953","authenticated-orcid":false,"given":"Chelsea","family":"Krantsevich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P. Richard","family":"Hahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shira","family":"Hahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gautam","family":"Dasarathy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pavan","family":"Turaga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julie","family":"Liss","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"521_CR1","unstructured":"Food and Drug Administration. Proposed regulatory framework for modifications to artificial intelligence\/machine learning (AI\/ML)-based software as a medical device (SaMD). https:\/\/www.regulations.gov\/document\/FDA-2019-N-1185-0001 (2019)."},{"key":"521_CR2","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44\u201356 (2019).","journal-title":"Nat. Med."},{"key":"521_CR3","unstructured":"Ross, C. & Swetlitz, I. IBM\u2019s Watson supercomputer recommended \u2018unsafe and incorrect\u2019 cancer treatments, internal documents show. Stat News. https:\/\/www.statnews.com\/2018\/07\/25\/ibm-watson-recommended-unsafe-incorrect-treatments\/ (2018)."},{"key":"521_CR4","unstructured":"Koutroumbas, K. & Theodoridis, S. Pattern Recognition (4th Ed.). (Elsevier Inc., Burlington, 2009)."},{"key":"521_CR5","doi-asserted-by":"publisher","first-page":"117","DOI":"10.3389\/fnut.2018.00117","volume":"5","author":"M Verma","year":"2018","unstructured":"Verma, M., Hontecillas, R., Tubau-Juni, N., Abedi, V. & Bassaganya-Riera, J. Challenges in personalized nutrition and health. Front. Nutr. 5, 117 (2018).","journal-title":"Front. Nutr."},{"key":"521_CR6","unstructured":"Williams, S. Personalized Nutrition Companies\u2019 Claims Overhyped: Scientists. The Scientist: Exploring Life, Inspiring Innovation. https:\/\/www.the-scientist.com\/news-opinion\/personalized-nutrition-companies-claims-overhyped--scientists-66321 (2019)."},{"key":"521_CR7","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.3233\/JAD-200888","volume":"78","author":"S de la Fuente Garcia","year":"2020","unstructured":"de la Fuente Garcia, S., Ritchie, C. & Luz, S. Artificial intelligence, speech, and language processing approaches to monitoring Alzheimer\u2019s disease: a systematic review. J. Alzheimer\u2019s Dis. 78, 1547\u20131574 (2020).","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"521_CR8","doi-asserted-by":"publisher","first-page":"1784","DOI":"10.1093\/jamia\/ocaa174","volume":"27","author":"U Petti","year":"2020","unstructured":"Petti, U., Baker, S. & Korhonen, A. A systematic literature review of automatic Alzheimer\u2019s disease detection from speech and language. J. Am. Med. Inform. Assoc. 27, 1784\u20131797 (2020).","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"521_CR9","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1145\/1968.1972","volume":"27","author":"LG Valiant","year":"1984","unstructured":"Valiant, L. G. A theory of the learnable. Commun. Acm. 27, 1134\u20131142 (1984).","journal-title":"Commun. Acm."},{"key":"521_CR10","doi-asserted-by":"publisher","first-page":"1212","DOI":"10.1001\/jama.2020.12067","volume":"324","author":"A Kaushal","year":"2020","unstructured":"Kaushal, A., Altman, R. & Langlotz, C. Geographic distribution of US cohorts used to train deep learning algorithms. JAMA 324, 1212\u20131213 (2020).","journal-title":"JAMA"},{"key":"521_CR11","doi-asserted-by":"crossref","unstructured":"Ben-David, S., & Urner, R. On the hardness of domain adaptation and the utility of unlabeled target samples. International Conference on Algorithmic Learning Theory (Springer, 2012).","DOI":"10.1007\/978-3-642-34106-9_14"},{"key":"521_CR12","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1126\/science.1254404","volume":"346","author":"MA Shafto","year":"2014","unstructured":"Shafto, M. A. & Tyler, L. K. Language in the aging brain: the network dynamics of cognitive decline and preservation. Science 346, 583\u2013587 (2014).","journal-title":"Science"},{"key":"521_CR13","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1080\/02643294.2012.710600","volume":"29","author":"D Poeppel","year":"2012","unstructured":"Poeppel, D. The maps problem and the mapping problem: two challenges for a cognitive neuroscience of speech and language. Cogn. Neuropsychol. 29, 34\u201355 (2012).","journal-title":"Cogn. Neuropsychol."},{"key":"521_CR14","doi-asserted-by":"publisher","unstructured":"Flint, C. et al. Systematic misestimation of machine learning performance in neuroimaging studies of depression. Neuropsychopharmacol. https:\/\/doi.org\/10.1038\/s41386-021-01020-7 (2021).","DOI":"10.1038\/s41386-021-01020-7"},{"key":"521_CR15","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.neuroimage.2016.02.079","volume":"145","author":"MR Arbabshirani","year":"2017","unstructured":"Arbabshirani, M. R., Plis, S., Sui, J. & Calhoun, V. D. Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. Neuroimage 145, 137\u2013165 (2017).","journal-title":"Neuroimage"},{"key":"521_CR16","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0224365","volume":"14","author":"A Vabalas","year":"2019","unstructured":"Vabalas, A., Gowen, E., Poliakoff, E. & Casson, A. J. Machine learning algorithm validation with a limited sample size. PLoS ONE 14, e0224365 (2019).","journal-title":"PLoS ONE"},{"key":"521_CR17","doi-asserted-by":"publisher","first-page":"2781","DOI":"10.1002\/sim.6525","volume":"34","author":"M Kicinski","year":"2015","unstructured":"Kicinski, M., Springate, D. A. & Kontopantelis, E. Publication bias in meta-analyses from the Cochrane Database of Systematic Reviews. Stat. Med. 34, 2781\u20132793 (2015).","journal-title":"Stat. Med."},{"key":"521_CR18","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1037\/0033-2909.86.3.638","volume":"86","author":"R Rosenthal","year":"1979","unstructured":"Rosenthal, R. The file drawer problem and tolerance for null results. Psychol. Bull. 86, 638\u2013641 (1979).","journal-title":"Psychol. Bull."},{"key":"521_CR19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.0020124","volume":"2","author":"JPA Ioannidis","year":"2005","unstructured":"Ioannidis, J. P. A. Why most published research findings are false. PLoS Med. 2, e124 (2005).","journal-title":"PLoS Med."},{"key":"521_CR20","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1126\/science.aaa9375","volume":"349","author":"C Dwork","year":"2015","unstructured":"Dwork, C. et al. The reusable holdout: preserving validity in adaptive data analysis. Science 349, 636\u2013638 (2015).","journal-title":"Science"},{"key":"521_CR21","doi-asserted-by":"crossref","unstructured":"Rao, R. B., Fung, G. & Rosales, R. On the dangers of cross-validation. An experimental evaluation. Proceedings of the 2008 SIAM International Conference on Data Mining (Society for Industrial and Applied Mathematics, 2008).","DOI":"10.1137\/1.9781611972788.54"},{"key":"521_CR22","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1044\/jshd.5204.367","volume":"52","author":"RD Kent","year":"1987","unstructured":"Kent, R. D., Kent, J. F. & Rosenbek, J. C. Maximum performance tests of speech production. J. Speech Hear. Disord. 52, 367\u2013387 (1987).","journal-title":"J. Speech Hear. Disord."},{"key":"521_CR23","doi-asserted-by":"publisher","DOI":"10.1186\/s12883-017-0829-y","volume":"17","author":"A Shirani","year":"2017","unstructured":"Shirani, A., Newton, B. D. & Okuda, D. T. Finger tapping impairments are highly sensitive for evaluating upper motor neuron lesions. BMC Neurol. 17, 55 (2017).","journal-title":"BMC Neurol."},{"key":"521_CR24","doi-asserted-by":"publisher","first-page":"494","DOI":"10.3109\/21678421.2013.817585","volume":"14","author":"JR Green","year":"2013","unstructured":"Green, J. R. et al. Bulbar and speech motor assessment in ALS: Challenges and future directions. Amyotroph. Lateral Scler. Frontotemporal. Degener. 14, 494\u2013500 (2013).","journal-title":"Amyotroph. Lateral Scler. Frontotemporal. Degener."},{"key":"521_CR25","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6947-12-8","volume":"12","author":"RL Figueroa","year":"2012","unstructured":"Figueroa, R. L. et al. Predicting sample size required for classification performance. BMC Med. Inform. Decis. Mak. 12, 8 (2012).","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"521_CR26","doi-asserted-by":"publisher","first-page":"e275","DOI":"10.1016\/S2589-7500(21)00057-1","volume":"3","author":"ML Charpignon","year":"2021","unstructured":"Charpignon, M. L., Celi, L. A. & Samuel, M. C. Who does the model learn from? Lancet Digit. Health 3, e275\u2013e276 (2021).","journal-title":"Lancet Digit. Health"},{"key":"521_CR27","doi-asserted-by":"publisher","first-page":"644.e1","DOI":"10.1016\/j.jvoice.2017.08.003","volume":"32","author":"JT Eichhorn","year":"2018","unstructured":"Eichhorn, J. T., Kent, R. D., Austin, D. & Vorperian, H. K. Effects of aging on vocal fundamental frequency and vowel formants in men and women. J. Voice 32, 644.e1\u2013644.e9 (2018).","journal-title":"J. Voice"},{"key":"521_CR28","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1121\/1.419712","volume":"102","author":"R Hagiwara","year":"1997","unstructured":"Hagiwara, R. Dialect variation and formant frequency: The American English vowels revisited. J. Acoust. Soc. Am. 102, 655\u2013658 (1997).","journal-title":"J. Acoust. Soc. Am."},{"key":"521_CR29","doi-asserted-by":"publisher","DOI":"10.1136\/bmjhci-2020-100220","volume":"27","author":"JH Maley","year":"2020","unstructured":"Maley, J. H., Wanis, K. N., Young, J. G. & Celi, L. A. Mortality prediction models, causal effects, and end-of-life decision making in the intensive care unit. BMJ Health Care Inform. 27, e100220 (2020).","journal-title":"BMJ Health Care Inform."},{"key":"521_CR30","doi-asserted-by":"publisher","first-page":"20160153","DOI":"10.1098\/rsta.2016.0153","volume":"374","author":"PV Coveney","year":"2016","unstructured":"Coveney, P. V., Dougherty, E. R. & Highfield, R. R. Big data need big theory too. Philos. Trans. R. Soc. A. 374, 20160153 (2016).","journal-title":"Philos. Trans. R. Soc. A."},{"key":"521_CR31","doi-asserted-by":"publisher","first-page":"437","DOI":"10.3389\/fnagi.2017.00437","volume":"9","author":"KD Mueller","year":"2018","unstructured":"Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: results from the Wisconsin registry for alzheimer\u2019s prevention. Front. Aging Neurosci. 9, 437 (2018).","journal-title":"Front. Aging Neurosci."},{"key":"521_CR32","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1007\/s11517-006-0119-0","volume":"44","author":"U Rajendra Acharya","year":"2006","unstructured":"Rajendra Acharya, U., Paul, J. K., Kannathal, N., Lim, C. M. & Suri, J. S. Heart rate variability: a review. Med. Biol. Eng. Comput. 44, 1031\u20131051 (2006).","journal-title":"Med. Biol. Eng. Comput."},{"key":"521_CR33","doi-asserted-by":"crossref","unstructured":"Ravanelli, M. et al. Multi-task self-supervised learning for robust speech recognition. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (2020).","DOI":"10.1109\/ICASSP40776.2020.9053569"},{"key":"521_CR34","doi-asserted-by":"crossref","unstructured":"Miao, Y., Hao Z., and Metze, F. Towards speaker adaptive training of deep neural network acoustic models. Fifteenth Annual Conference of the International Speech Communication Association (2014).","DOI":"10.21437\/Interspeech.2014-490"},{"key":"521_CR35","doi-asserted-by":"publisher","unstructured":"Lu, B. et al. A practical alzheimer disease classifier via brain imaging-based deep learning on 85,721 samples. bioRxiv. Preprint at https:\/\/doi.org\/10.1101\/2020.08.18.256594 (2021).","DOI":"10.1101\/2020.08.18.256594"},{"key":"521_CR36","unstructured":"Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: understanding transfer learning for medical imaging. Proceedings of the Thirty-third Conference on Neural Information Processing Systems (2019)."},{"key":"521_CR37","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/S0925-2312(03)00433-8","volume":"55","author":"LJ Cao","year":"2003","unstructured":"Cao, L. J., Chua, K. S., Chong, W. K., Lee, H. P. & Gu, Q. M. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing 55, 321\u2013336 (2003).","journal-title":"Neurocomputing"},{"key":"521_CR38","first-page":"300","volume":"31","author":"IT Jolliffe","year":"1982","unstructured":"Jolliffe, I. T. A note on the use of principal components in regression. J. R. Stat. Soc. Ser. C. Appl. Stat. 31, 300\u2013303 (1982).","journal-title":"J. R. Stat. Soc. Ser. C. Appl. Stat."},{"key":"521_CR39","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1159\/000511671","volume":"4","author":"GM Stegmann","year":"2020","unstructured":"Stegmann, G. M. et al. Repeatability of commonly used speech and language features for clinical applications. Digit. Biomark. 4, 109\u2013122 (2020).","journal-title":"Digit. Biomark."},{"key":"521_CR40","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1093\/imamat\/24.1.59","volume":"24","author":"RB Marimont","year":"1979","unstructured":"Marimont, R. B. & Shapiro, M. B. Nearest neighbour searches and the curse of dimensionality. IMA J. Appl. Math. 24, 59\u201370 (1979).","journal-title":"IMA J. Appl. Math."},{"key":"521_CR41","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1111\/j.1467-8640.2010.00366.x","volume":"26","author":"Y Bengio","year":"2010","unstructured":"Bengio, Y., Delalleau, O. & Simard, C. Decision trees do not generalize to new variations. Comput. Intell. 26, 449\u2013467 (2010).","journal-title":"Comput. Intell."},{"key":"521_CR42","doi-asserted-by":"crossref","unstructured":"B\u00fchlmann, P. & Van de Geer, S. Statistics for High-Dimensional Data. (Springer, Berlin, Heidelberg, 2011).","DOI":"10.1007\/978-3-642-20192-9"},{"key":"521_CR43","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1162\/neco.1994.6.6.1289","volume":"6","author":"H Drucker","year":"1994","unstructured":"Drucker, H., Cortes, C., Jackel, L. D., LeCun, Y. & Vapnik, V. Boosting and other ensemble methods. Neural Comput. 6, 1289\u20131301 (1994).","journal-title":"Neural Comput."},{"key":"521_CR44","unstructured":"Pereyra, G., Tucker, G., Chorowski, J., Kaiser, \u0141. & Hinton, G. Regularizing neural networks by penalizing confident output distributions. arXiv. Preprint at https:\/\/arxiv.org\/abs\/1701.06548 (2017)."},{"key":"521_CR45","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016).","DOI":"10.1109\/CVPR.2016.308"},{"key":"521_CR46","unstructured":"Li, W., Dasarathy, G. & Berisha, V. Regularization via structural label smoothing. Proceedings of the International Conference on Artificial Intelligence and Statistics PMLR (2020)."},{"key":"521_CR47","unstructured":"Goodfellow, I., Shlens, J. & Szegedy, C. Explaining and Harnessing Adversarial Examples. Proceedings of the International Conference on Learning Representations (2015)."},{"key":"521_CR48","doi-asserted-by":"crossref","unstructured":"Dwork, C. et al. Preserving statistical validity in adaptive data analysis. Proceedings of the Forty-Seventh Annual ACM Symposium on Theory of Computing (2015).","DOI":"10.1145\/2746539.2746580"},{"key":"521_CR49","unstructured":"Recht, B., Roelofs, R., Schmidt, L. & Shankar, V. Do cifar-10 classifiers generalize to cifar-10? arXiv. Preprint at https:\/\/arxiv.org\/abs\/1806.00451 (2018)."},{"key":"521_CR50","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1016\/j.jeconom.2019.10.014","volume":"221","author":"A D\u2019Amour","year":"2021","unstructured":"D\u2019Amour, A., Ding, P., Feller, A., Lei, L. & Sekhon, J. Overlap in observational studies with high-dimensional covariates. J. Econom. 221, 644\u2013654 (2021).","journal-title":"J. Econom."},{"key":"521_CR51","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1109\/TSP.2015.2477805","volume":"64","author":"V Berisha","year":"2015","unstructured":"Berisha, V., Wisler, A., Hero, A. O. & Spanias, A. Empirically estimable classification bounds based on a nonparametric divergence measure. IEEE Trans. Signal Process. 64, 580\u2013591 (2015).","journal-title":"IEEE Trans. Signal Process."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-021-00521-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-021-00521-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-021-00521-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T19:03:03Z","timestamp":1670094183000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-021-00521-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,28]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["521"],"URL":"https:\/\/doi.org\/10.1038\/s41746-021-00521-5","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,28]]},"assertion":[{"value":"13 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"V.B. and J.L. are co-founders and have equity in Aural Analytics. S.H. and C.K. are employed by Aural Analytics. The remaining authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"153"}}