Smart Health

Proposed in recent years, smart health has gained great attention after its debut. Undoubtedly, wearable devices will bring great convenience to the development of smart health. With the aging trend, increase of empty families and solitary elderly people, and the growth spurt of chronic disease, wearable devices have come to its spring. But due to the lack of standards in the industry, the data collected by wearable devices is unreliable, and is difficult to be accepted by doctors. Besides, there are also some problems concerning with privacy. In this paper, the advantages, disadvantages, opportunities and obstacles of wearable devices are analyzed, and some development suggestions are provided.

[1]  W. Baxt Application of artificial neural networks to clinical medicine , 1995, The Lancet.

[2]  Athanasios V. Vasilakos,et al.  Neural networks for computer-aided diagnosis in medicine: A review , 2016, Neurocomputing.

[3]  S K Inouye,et al.  A Predictive Model for Delirium in Hospitalized Elderly Medical Patients Based on Admission Characteristics , 1993, Annals of Internal Medicine.

[4]  H. A. Nugroho,et al.  Gaussian Bayes classifier for medical diagnosis and grading: Application to diabetic retinopathy , 2010, 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[5]  M. Tech,et al.  Decision Support in Heart Disease Prediction System using Naive Bayes , 2011 .

[6]  Dhanashree S. Medhekar,et al.  Heart Disease Prediction System using Naive Bayes , 2013 .

[7]  R. Habib,et al.  Biofeedback treatment for asthma. , 2004, Chest.

[8]  Li Li,et al.  Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records , 2016, Scientific Reports.

[9]  Hsinchun Chen,et al.  Deep Learning Based Topic Identification and Categorization: Mining Diabetes-Related Topics on Chinese Health Websites , 2016, DASFAA.

[10]  Yan Zhang,et al.  Chronic Disease Related Entity Extraction in Online Chinese Question and Answer Services , 2015, ICSH.

[11]  Yi-Ping Phoebe Chen,et al.  Computational intelligence for heart disease diagnosis: A medical knowledge driven approach , 2013, Expert Syst. Appl..

[12]  J. Fleiss,et al.  The ability of several short-term measures of RR variability to predict mortality after myocardial infarction. , 1993, Circulation.

[13]  Zhi-Hua Zhou,et al.  Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble , 2003, IEEE Transactions on Information Technology in Biomedicine.

[14]  Yi-Ping Phoebe Chen,et al.  Association rule mining to detect factors which contribute to heart disease in males and females , 2013, Expert Syst. Appl..

[15]  E. Azmitia,et al.  Awakening the sleeping giant: anatomy and plasticity of the brain serotonergic system. , 1991, The Journal of clinical psychiatry.

[16]  E A Mayer,et al.  Emerging disease model for functional gastrointestinal disorders. , 1999, The American journal of medicine.

[17]  M. Prince,et al.  Predicting the onset of Alzheimer's disease using Bayes' theorem. , 1996, American journal of epidemiology.

[18]  E. Shortliffe Mycin: computer-based medical consultations , 1976 .

[19]  Charles Elkan,et al.  Learning to Diagnose with LSTM Recurrent Neural Networks , 2015, ICLR.

[20]  P. Picton,et al.  Heart rate variability biofeedback as a behavioral neurocardiac intervention to enhance vagal heart rate control. , 2005, American heart journal.

[21]  Shadab Pattekari Intelligent Heart Disease Prediction System Using Naive Bayes , 2014 .

[22]  P. Umamaheswari,et al.  Multitude Classifier Using Rough Set Jelinek Mercer Naïve Bayes for Disease Diagnosis , 2016 .

[23]  Sulabha S. Apte,et al.  Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques , 2012 .

[24]  Qeethara Al-Shayea Artificial Neural Networks in Medical Diagnosis , 2024, International Journal of Research Publication and Reviews.