Patient-centered deep learning model and diagnosis service for persons with Alzheimer's disease

Guanghua Qiu, Jason L. Qiu

Research output: Contribution to journalConference article

Abstract

Because pharmaceutical companies have failed to develop Alzheimer's disease (AD) cure and treatment as of today, AD early detection and intervention becomes increasingly clear to be the best choice of improving quality of life for persons with AD at least in the near future. Thus, developing patient-centric predictive models and enabling self-diagnosis services are of great potential. This paper presents how recurrent neuron neatwork (RNN) models can be adopted in the AD early diagnosis modeling (ADEDM). In particular, we show that the improved prediction accuracy of RNN AD-EDM can contribute to the delivery of self-diagnosis services for preclinical/early AD patients. By leveraging the fast development of big data technologies and machine learning methods, our AD-EDM tools will make a difference in discovering non-pharmacologic therapy solutions to slow AD progression.

Original languageEnglish (US)
Pages (from-to)1841-1847
Number of pages7
JournalProceedings of the International Conference on Industrial Engineering and Operations Management
Volume2018
Issue numberJUL
StatePublished - Jan 1 2018
Event2nd European International Conference on Industrial Engineering and Operations Management.IEOM 2018 -
Duration: Jul 26 2018Jul 27 2018

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Neurons
Deep learning
Learning model
Alzheimer's disease
Drug products
Learning systems
Industry
Neuron
Big data
Machine learning
Modeling
Prediction accuracy
Quality of life
Progression
Therapy
Pharmaceuticals
Learning methods

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

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abstract = "Because pharmaceutical companies have failed to develop Alzheimer's disease (AD) cure and treatment as of today, AD early detection and intervention becomes increasingly clear to be the best choice of improving quality of life for persons with AD at least in the near future. Thus, developing patient-centric predictive models and enabling self-diagnosis services are of great potential. This paper presents how recurrent neuron neatwork (RNN) models can be adopted in the AD early diagnosis modeling (ADEDM). In particular, we show that the improved prediction accuracy of RNN AD-EDM can contribute to the delivery of self-diagnosis services for preclinical/early AD patients. By leveraging the fast development of big data technologies and machine learning methods, our AD-EDM tools will make a difference in discovering non-pharmacologic therapy solutions to slow AD progression.",
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