APP Medical Diagnostic Check-up Consultation System Based on Speech Recognition

Page: [408 - 414] Pages: 7

  • * (Excluding Mailing and Handling)

Abstract

Background: Medical test orders can display the physiological functions of patients by using medical means. The medical staff determines the patient's condition through medical test orders and completes the treatment. However, for most patients and their families, there are so many terminologies in the medical test list and they are inconvenient to understand and query, which would affect the patients’ cognition and treatment effect. Therefore, it is especially necessary to develop a consulting system that can provide related analysis after getting medical test data.

Objective: This paper starts with information acquisition and speech recognition. It proposes a natural scene information acquisition and analysis model based on deep learning, focusing on improving the recognition rate of routine test list and achieving targeted smart search to allow users to get more accurate personalized health advice.

Methods: Based on medical characteristics, considering the needs of patients, this paper constructs an APP-based conventional medical test consultation system, using artificial intelligence and voice recognition technology to collect user input; analyzing user needs with the help of conventional medical information knowledge database.

Results: This model combines speech recognition and data mining methods to obtain routine test list data and is suitable for accurate analysis of problems in routine check-up procedure. The app provides effective explanations and guidance for the treatment and rehabilitation of patients.

Conclusion: It organically links the Internet with personalized medicine, which can effectively improve the popularity of medical knowledge and provide a reference for the application of medical services on the Internet. Meanwhile, this app can contribute to the improvement of medical standards and provide new models for modern medical management.

Keywords: Artificial intelligence, speech recognition, machine learning, medical test, rehabilitation, knowledge base.

Graphical Abstract

[1]
Ahadzadeh AS, Pahlevan Sharif S, Ong FS, Khong KW. Integrating health belief model and technology acceptance model: an investigation of health-related internet use. J Med Internet Res 2015; 17(2) e45
[http://dx.doi.org/10.2196/jmir.3564] [PMID: 25700481]
[2]
Liu G, Zhang HB. Voice Assistant-Application of speech recognition technology in the Android system. Appl Mech Mater 2014; 596: 384-7.
[http://dx.doi.org/10.4028/www.scientific.net/AMM.596.384]
[3]
Hu H, Xi X, Wong LLN, Hochmuth S, Warzybok A, Kollmeier B. Construction and evaluation of the Mandarin Chinese matrix (CMNmatrix) sentence test for the assessment of speech recognition in noise. Int J Audiol 2018; 57(11): 838-50.
[http://dx.doi.org/10.1080/14992027.2018.1483083] [PMID: 30178681]
[4]
Saini P, Kaur P. Automatic speech recognition: a review. Int J Eng Trend Technol 2013; 4(2): 132-6.
[5]
Hamill M, Young V, Boger J, Mihailidis A. Development of an automated speech recognition interface for personal emergency response systems. J Neuroeng Rehabil 2009; 6(1): 26.
[http://dx.doi.org/10.1186/1743-0003-6-26] [PMID: 19583876]
[6]
Yu D, Seltzer ML, Li J, Huang J-T, Seide F. Feature learning in deep neural networks-studies on speech recognition tasks arXiv preprint arXiv:13013605 2013.
[7]
Guo G, Zhang N. A survey on deep learning based face recognition. Comput Vis Image Underst 2019; 189102805
[http://dx.doi.org/10.1016/j.cviu.2019.102805]]
[8]
Novotny J, Sovka P, Uhlir J. Study and application of silence model adaptation for use in telephone speech recognition system. Wuxiandian Gongcheng 2004; 13: 1-6.
[9]
Debnath S, Roy P. Study of speech enabled healthcare technology. Int J Med Eng Inform 2019; 11(1): 71-85.
[http://dx.doi.org/10.1504/IJMEI.2019.096893]
[10]
Dewyer NA, Jiradejvong P, Henderson Sabes J, Limb CJ. Automated smartphone audiometry: validation of a word recognition test app. Laryngoscope 2018; 128(3): 707-12.
[http://dx.doi.org/10.1002/lary.26638] [PMID: 28543040]
[11]
Badal VD, Wright D, Katsis Y, et al. Challenges in the construction of knowledge bases for human microbiome-disease associations. Microbiome 2019; 7(1): 129.
[http://dx.doi.org/10.1186/s40168-019-0742-2] [PMID: 31488215]
[12]
Wallis JW. Use of artificial intelligence in cardiac imaging. J Nucl Med 2001; 42(8): 1192-4.
[PMID: 11483679]
[13]
Bakator M, Radosav D. Deep learning and medical diagnosis: a review of literature. Multimodal Technol Interaction 2018; 2(3): 47.
[http://dx.doi.org/10.3390/mti2030047]
[14]
Shi W, Zhang X, Zou X, Han W. Deep neural network and noise classification-based speech enhancement. Mod Phys Lett B 2017; 31(19-21) 1740096
[http://dx.doi.org/10.1142/S0217984917400966]
[15]
Kenny P, Lennig M, Mermelstein P. A linear predictive HMM for vector-valued observations with applications to speech recognition. IEEE Trans Acoust Speech Signal Process 1990; 38(2): 220-5.
[http://dx.doi.org/10.1109/29.103057]
[16]
Yu PP. Knowledge bases, clinical decision support systems, and rapid learning in oncology. J Oncol Pract 2015; 11(2): e206-11.
[http://dx.doi.org/10.1200/JOP.2014.000620] [PMID: 25715002]
[17]
Qi Y, Tong Y, Gao H, et al. Research on Construction Methods of Traditional Chinese Medicine Health Maintenance World Science and Technology-Modernization of Traditional Chinese Medicine 2015; (8): 1612-6.
[18]
Murukannaiah PK, Singh MP. Understanding location-based user experience. IEEE Internet Comput 2014; 18(6): 72-6.
[http://dx.doi.org/10.1109/MIC.2014.127]
[19]
Clarke MA, King JL, Kim MS. Toward successful implementation of speech recognition technology: a survey of srt utilization issues in healthcare settings. South Med J 2015; 108(7): 445-51.
[PMID: 26192944]
[20]
Wiechmann W, Kwan D, Bokarius A, Toohey SL. There’s an app for that? Highlighting the difficulty in finding clinically relevant smartphone applications. West J Emerg Med 2016; 17(2): 191-4.
[http://dx.doi.org/10.5811/westjem.2015.12.28781] [PMID: 26973750]