Wiens, J. & Shenoy, E. S. Machine learning for healthcare: On the verge of a serious shift in healthcare epidemiology. Clin. Infect. Dis. 66, 149–153. https://doi.org/10.1093/CID/CIX731 (2018).Article
PubMed
Google Scholar
Templeton, J. M., Poellabauer, C. & Schneider, S. Design of a neurocognitive digital well being system (NDHS) for neurodegenerative ailments. in Proceedings of the 2021 Workshop on Future of Digital Biomarkers 26–33, https://doi.org/10.1145/3469266.3471157 (2021).Far, M. S., Eickhoff, S. B., Goni, M. & Dukart, J. Exploring test-retest reliability and longitudinal stability of digital biomarkers for Parkinson disease within the m-power information set: Cohort research. J. Med. Internet Res. 23, e26608. https://doi.org/10.2196/26608 (2021).Article
Google Scholar
Waring, J., Lindvall, C. & Umeton, R. Automated machine learning: Review of the state-of-the-art and alternatives for healthcare. Artif. Intell. Med. 104, 101822. https://doi.org/10.1016/J.ARTMED.2020.101822 (2020).Article
PubMed
Google Scholar
Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A. & Escobar, G. Big information in well being care: Using analytics to determine and handle high-risk and high-cost sufferers. Health 33, 1123–1131. https://doi.org/10.1377/HLTHAFF.2014.0041 (2017).Article
Google Scholar
Marella, W. M., Sparnon, E. & Finley, E. Screening digital well being record-related affected person security experiences using machine learning. J. Patient Saf. 13, 31–36. https://doi.org/10.1097/PTS.0000000000000104 (2017).Article
PubMed
Google Scholar
Deng, Okay. et al. Heterogeneous digital biomarker integration out-performs affected person self-reports in predicting Parkinson’s disease. Commun. Biol.https://doi.org/10.1038/s42003-022-03002-x (2022).Article
PubMed
PubMed Central
Google Scholar
Maetzler, W. & Pilotto, A. Digital evaluation at house: mPower towards Parkinson disease. Nat. Rev. Neurol. 2021(17), 661–662. https://doi.org/10.1038/s41582-021-00567-9 (2021).Article
Google Scholar
Hansen, C., Sanchez-Ferro, A. & Maetzler, W. How cell well being expertise and digital well being data will change care of sufferers with Parkinson’s disease. J. Parkinson Dis.https://doi.org/10.3233/JPD-181498 (2018).Article
Google Scholar
Byrom, B., Wenzel, Okay., Pierce, J., Wenzel, Okay. & Pierce, J. Computerised medical assessments: Derived advanced medical endpoints from affected person self-report information. EPro 1, 179–202. https://doi.org/10.4324/9781315580142-14 (2016).Article
Google Scholar
Templeton, J. M., Poellabauer, C. & Schneider, S. The Case for Symptom-Specific Neurological Digital Biomarkers (Springer, 2021).
Google Scholar
Kumar, S. et al. Mobile well being expertise analysis. Am. J. Prev. Med. 45, 228–236. https://doi.org/10.1016/j.amepre.2013.03.017 (2013).Article
PubMed
PubMed Central
Google Scholar
Templeton, J. M., Poellabauer, C. & Schneider, S. Enhancement of neurocognitive assessments using smartphone capabilities: Systematic assessment. JMIR mHealth and uHealth 8, e15517. https://doi.org/10.2196/15517 (2020).Article
PubMed
PubMed Central
Google Scholar
Löfgren, N., Conradsson, D., Rennie, L., Moe-Nilssen, R. & Franzén, E. The results of built-in single- and dual-task coaching on automaticity and consideration allocation in Parkinson’s disease: A secondary evaluation from a randomized trial. Neuropsychology 33, 147–156. https://doi.org/10.1037/neu0000496 (2019).Article
PubMed
Google Scholar
Nasreddine, Z. S. et al. The montreal cognitive evaluation, MoCA: A short screening device for delicate cognitive impairment. J. Am. Geriatr. Soc. 53, 695–9. https://doi.org/10.1111/j.1532-5415.2005.53221.x (2005).Article
PubMed
Google Scholar
Tombaugh, T. N. & McIntyre, N. J. The mini-mental state examination: A complete assessment. J. Am. Geriatr. Soc. 40, 922–935. https://doi.org/10.1111/j.1532-5415.1992.tb01992.x (1992).CAS
Article
PubMed
Google Scholar
Neff, C., Wang, M. C. & Martel, H. Using the PDQ-39 in routine take care of Parkinson’s disease. Parkinson. Relat. Disord. 53, 105–107. https://doi.org/10.1016/J.PARKRELDIS.2018.05.019 (2018).Article
Google Scholar
Deshpande, P., Sudeepthi, B., Rajan, S. & Abdul Nazir, C. Patient-reported outcomes: A brand new period in medical analysis. Perspect. Clin. Res. 2, 137. https://doi.org/10.4103/2229-3485.86879 (2011).Article
PubMed
PubMed Central
Google Scholar
Zhan, A. et al. Using smartphones and machine learning to quantify Parkinson disease severity the cell Parkinson disease rating. JAMA Neurol. 75, 876–880. https://doi.org/10.1001/jamaneurol.2018.0809 (2018).ADS
Article
PubMed
PubMed Central
Google Scholar
De Vos, M., Prince, J., Buchanan, T., FitzGerald, J. J. & Antoniades, C. A. Discriminating progressive supranuclear palsy from Parkinson’s disease using wearable expertise and machine learning. Gait Posture 77, 257–263. https://doi.org/10.1016/J.GAITPOST.2020.02.007 (2020).Article
PubMed
Google Scholar
Bhardwaj, R., Nambiar, A. R. & Dutta, D. A research of machine learning in healthcare. Proc. Int. Comput. Softw. Appl. Conf. 2, 236–241. https://doi.org/10.1109/COMPSAC.2017.164 (2017).Article
Google Scholar
Hadirah, N., Anwar, Okay., Saian, R. & Abu Bakar, S. An enhanced ant colony optimization with gini index for predicting kind 2 diabetes. AIP Proc.https://doi.org/10.1063/5.0057315 (2021).Article
Google Scholar
Uddin, S., Khan, A., Hossain, M. E. & Moni, M. A. Comparing totally different supervised machine learning algorithms for disease prediction. BMC Med. Inform. Decis. Mak. 19, 1–16. https://doi.org/10.1186/S12911-019-1004-8 (2019).Article
Google Scholar
Ricciardi, C. et al. Classifying totally different stages of Parkinson’s disease by random forests. IFMBE Proc. 76, 1155–1162. https://doi.org/10.1007/978-3-030-31635-8_140 (2019).Article
Google Scholar
Domingos, P. Tapping into the people information wanted to advance machine learning purposes. Reviewhttps://doi.org/10.1145/2347736.2347755 (2012).Article
Google Scholar
Vega, J. et al. Back to analogue: Self-reporting for Parkinson’s disease. J. Parkinson Dis.https://doi.org/10.1145/3173574.3173648 (2018).Article
Google Scholar
Nicolson, P. J., Hinman, R. S., Wrigley, T. V., Stratford, P. W. & Bennell, Okay. L. Self-reported house train adherence: A validity and reliability research using hid accelerometers. J. Orthop. Sports Phys. Ther. 48, 943–950. https://doi.org/10.2519/jospt.2018.8275 (2018).Article
PubMed
Google Scholar
Reychav, I. et al. How dependable are self-assessments using cell expertise in healthcare? The results of expertise identification and self-efficacy. Comput. Hum. Behav. 91, 52–61. https://doi.org/10.1016/j.chb.2018.09.024 (2019).Article
Google Scholar
Prince, S. A. et al. A comparability of self-reported and machine measured sedentary behaviour in adults: A scientific assessment and meta-analysis. AIP Proc.https://doi.org/10.1186/s12966-020-00938-3 (2020).Article
Google Scholar
Hoehn, M. M. & Yahr, M. D. Parkinsonism. Neurology 17, 427–427. https://doi.org/10.1212/WNL.17.5.427 (1967).CAS
Article
PubMed
Google Scholar
Padman, N., Swarnalatha, R., Venkatesh, V. & Kumar, N. Telediagnosis of Parkinson’s disease symptom severity using H&Y scale. J. Eng. Sci. Technol. 15, 1466–1480 (2020).
Google Scholar
Goetz, C. G. et al. Movement dysfunction society-sponsored revision of the unified Parkinson’s disease ranking scale (MDS-UPDRS): Scale presentation and clinimetric testing outcomes. Mov. Disord. 23, 2129–2170. https://doi.org/10.1002/mds.22340 (2008).Article
PubMed
Google Scholar
Martinez-Martin, P. et al. Validation research of the hoehn and yahr scale included within the MDS-UPDRS. Mov. Disord. 33, 651–652. https://doi.org/10.1002/MDS.27242 (2018).Article
PubMed
Google Scholar
Evers, L. J., Krijthe, J. H., Meinders, M. J., Bloem, B. R. & Heskes, T. M. Measuring Parkinson’s disease over time: The real-world within-subject reliability of the MDS-UPDRS. Mov. Disord. 34, 1480–1487. https://doi.org/10.1002/MDS.27790 (2019).Article
PubMed
PubMed Central
Google Scholar
Post, B. et al. Young onset Parkinson’s disease: A contemporary and tailor-made strategy. J. Parkinson Dis.https://doi.org/10.3233/JPD-202135 (2020).Article
Google Scholar
Qutubuddin, A. A. et al. Validating the Berg Balance Scale for sufferers with Parkinson’s disease: A key to rehabilitation analysis. Arch. Phys. Med. Rehabil. 86, 789–792. https://doi.org/10.1016/J.APMR.2004.11.005 (2005).Article
PubMed
Google Scholar
Bhatt, T., Yang, F., Mak, M. Okay., Hui-Chan, C.W.-Y. & Pai, Y.-C. Effect of externally cued coaching on dynamic stability management in the course of the sit-to-stand job in folks with Parkinson disease. Phys. Ther. 93, 492–503. https://doi.org/10.2522/PTJ.20100423 (2013).Article
PubMed
Google Scholar
Brusse, Okay. J., Zimdars, S., Zalewski, Okay. R. & Steffen, T. M. Testing purposeful efficiency in folks With Parkinson disease. Phys. Ther. 85, 134–141. https://doi.org/10.1093/PTJ/85.2.134 (2005).Article
PubMed
Google Scholar
Duncan, R. P., Leddy, A. L. & Earhart, G. M. Five occasions sit to face check efficiency in Parkinson disease. Arch. Phys. Med. Rehabil. 92, 1431. https://doi.org/10.1016/J.APMR.2011.04.008 (2011).Article
PubMed
PubMed Central
Google Scholar
Templeton, J. M., Poellabauer, C. & Schneider, S. Design of a Mobile-Based Neurological Assessment Tool for Aging Populations. 166–185, (Springer, 2021). https://doi.org/10.1007/978-3-030-70569-5_11.Scarpina, F. & Tagini, S. The stroop coloration and phrase check. Front. Psychol. 8, 557. https://doi.org/10.3389/fpsyg.2017.00557 (2017).Article
PubMed
PubMed Central
Google Scholar
Dangare, C. S., Apte, S. S. & Student, M. E. Improved research of coronary heart disease prediction system using information mining classification strategies. Int. J. Comput. Appl. 47, 975–999 (2012).
Google Scholar
Aydın, F. & Aslan, Z. Recognizing Parkinson’s disease gait patterns by vibes algorithm and Hilbert–Huang remodel. Eng. Sci. Technol. Int. J. 24, 112–125. https://doi.org/10.1016/J.JESTCH.2020.12.005 (2021).Article
Google Scholar
de Andrade, J. B. C. et al. Oxfordshire neighborhood stroke challenge classification: A proposed automated algorithm. Eur. Stroke J. 6, 160–167. https://doi.org/10.1177/23969873211012136 (2021).Article
PubMed
PubMed Central
Google Scholar
Ghiasi, M. M., Zendehboudi, S. & Mohsenipour, A. A. Decision tree-based prognosis of coronary artery disease: CART mannequin. Comput. Methods Prog. Biomed. 192, 105400. https://doi.org/10.1016/J.CMPB.2020.105400 (2020).Article
Google Scholar
Venkatasubramaniam, A. et al. Decision timber in epidemiological analysis. Emerg. Themes Epidemiol. 14, 1–12. https://doi.org/10.1186/S12982-017-0064-4/FIGURES/6 (2017).Article
Google Scholar
Sharma, A., Scholar, R., Professor, A. & Gupta, M. Theoretical research of resolution tree algorithms to determine pivotal components for efficiency enchancment: A assessment theoretical research of resolution tree algorithms to determine pivotal components for efficiency enchancment: A assessment Pooja Gulati. Int. J. Comput. Appl. 141, 975–8887. https://doi.org/10.5120/ijca2016909926 (2016).Article
Google Scholar
Kino, S. et al. A scoping assessment on the use of machine learning in analysis on social determinants of well being: Trends and analysis prospects. SSM Popul. Health 15, 100836. https://doi.org/10.1016/J.SSMPH.2021.100836 (2021).Article
PubMed
PubMed Central
Google Scholar
Sharma, S. R., Singh, B. & Kaur, M. Classification of Parkinson disease using binary Rao optimization algorithms. Expert Syst. 38, e12674. https://doi.org/10.1111/EXSY.12674 (2021).Article
Google Scholar
Li, Y. et al. Envelope learning view challenge clever algorithm and system view challenge classification of Parkinson’s disease by resolution tree primarily based occasion choice and ensemble learning algorithms. J. Med. Imaging Health Inform. 7, 1–9. https://doi.org/10.1166/jmihi.2017.2033 (2017).Article
Google Scholar
Gordon, L. Using Classification and Regression Trees (CART) in SAS® Enterprise Miner TM For Applications in Public Health. (2013).Albers, E. A. et al. Visualization codecs of patient-reported consequence measures in medical apply: A scientific assessment about preferences and interpretation accuracy. J. Patient-Rep. Outcomes 6, 1–41. https://doi.org/10.1186/S41687-022-00424-3/TABLES/3 (2022).Article
Google Scholar
Blake-Krebs, B. When Parkinson’s Strikes Early: Voices, Choices, Resources and Treatment, 1st ed. (HunterHouse, 2001).Ryu, J., Vero, J., Dobkin, R. D. & Torres, E. B. Dynamic digital biomarkers of motor and cognitive perform in Parkinson’s disease. J. Vis. Exp. 2019, e59827. https://doi.org/10.3791/59827 (2019).Article
Google Scholar
Mazzoni, P., Shabbott, B. & Cortés, J. C. Motor management abnormalities in Parkinson’s disease. Cold Spring Harbor Perspect. Med.https://doi.org/10.1101/cshperspect.a009282 (2012).Article
Google Scholar
Vianello, A., Chittaro, L., Burigat, S. & Budai, R. MotorMind: A cell app for the evaluation of customers’ motor efficiency in neurology. Comput. Methods Prog. Biomed. 143, 35–47. https://doi.org/10.1016/j.cmpb.2017.02.012 (2017).Article
Google Scholar
Maguire, Á., Martin, J., Jarke, H. & Ruggeri, Okay. Getting nearer? Differences stay in neuropsychological assessments transformed to cell gadgets. Psychol. Serv.https://doi.org/10.1037/ser0000307 (2018).Article
PubMed
Google Scholar
Jacobson, N. C., Weingarden, H. & Wilhelm, S. Digital biomarkers of temper issues and symptom change. NPJ Dig. Med. 2, 1–3. https://doi.org/10.1038/s41746-019-0078-0 (2019).Article
Google Scholar
Pahuja, G. & Nagabhushan, T. N. A comparative research of present machine learning approaches for Parkinson’s disease detection. IETE J. Res. 67, 4–14. https://doi.org/10.1080/03772063.2018.1531730 (2021).Article
Google Scholar
Dijkhuis, T. B., Blaauw, F. J., van Ittersum, M. W., Velthuijsen, H. & Aiello, M. Personalized bodily exercise teaching: A machine learning strategy. Sensors. 18, 623. https://doi.org/10.3390/S18020623 (2018).ADS
Article
PubMed Central
Google Scholar
Templeton, J. M., Poellabauer, C. & Schneider, S. Towards symptom-specific intervention suggestion techniques. J. Parkinson’s Dis. 12, 1621–1631. https://doi.org/10.3233/JPD-223214 (2022).Article
Google Scholar
https://www.nature.com/articles/s41598-022-18015-z
