2021 Fiscal Year Research-status Report
Healthcare Risk Prediction on Data Streams Employing Cross Ensemble Deep Learning
| Project/Area Number |
20K11955
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| Research Institution | Iwate Prefectural University |
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Principal Investigator |
藤田 ハミド 岩手県立大学, 公私立大学の部局等, 特命教授 (30244990)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | 知能情報学関連 / Machine Learning / Health Care System / Deep Learning |
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| Outline of Annual Research Achievements |
We have used ensemble deep learning techniques by constructing Deep Neural Networks (DNNs) based on assembled CNN in architecture of two GPUs in
cross layered connection. Also, we have one GUP system, running as backup for training experiments using large scale data for comparison purpose. I
could achieve good research results using zero short learning on multivariate data. Also I have trained the deep-learning architecture on dynamic data, and image data. The results was promising and therefore we publish three journal articles. We also, implemented Multi-task Learning-based Attentional
Feature Fusion Network for Scene Text Image Super-resolution, with good results.
Although we achieved good results using our reported algorithms (as shown in the list of publications), however, the accuracy for some healthcare data analytics need to be enhanced. The year 2021 was hard due to the pandemic as traveling and moving around was tight, also materials become expensive than usual. Nevertheless, the results achieved is good, as evident from the publication enlisted in journals.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
The experimental results provided high accuracy outcome, by testing it on the learning system, however due to the pandemic traveling
restrictions, it was rather not easy to expand the results with European collaborators that I contribute with in terms of the innovative idea in
Deep learning using semi-supervised learning methods. Some researchers that I collaborate are looking to test the machine learning developed
model on Parkinson’s Disease: as in the joint publication: A. Butt, E. Rovini, Hamido Fujita, C. Maremmani, F. Cavallo, “Data-Driven Models
for Objective Grading Improvement of Parkinson’s Disease”, Annals of Biomedical Engineering (2020) 48, p.2976-2987
https://doi.org/10.1007/s10439-020-02628-4. This is currently to be test for more accuracy on our system, also for Alzheimer.
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| Strategy for Future Research Activity |
For the year 2022: testing data to revise the built architecture for learning. Still some multivariate health care data, are not providing
high quality prediction, maybe this is related to data noise (we need to check this issue). it needs extract further features to refine the training mechanism.
Also to test multi view learning by my joint work Consensus Multi-view Clustering Model for Predicting Alzheimer’s Disease Progression"
Computer Methods and Programs in Biomedicine, V.199, Feb. 2021, 105895. These new results are to be expanded and be used to detect other health
issues like my work: "Application of mechanical trigger for unobtrusive detection of respiratory disorders from body recoil micro-movements" Computer Methods and Programs in Biomedicine, Volume 207, August 2021, 106149
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| Causes of Carryover |
(1) 新型コロナウイルスの影響で海外出張が中止になったため
(1) Due to the pandemic the traveling for conference and meeting with other research collaborator was not possible,
(2) Some development was delayed due to traveling restrictions.
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