Projects
Artificial intelligence enabled electrocardiography system
Participants
Shih-Hua Lin
Adviser
Chin Lin
Technical PI
Chin-Sheng Lin
Clinical PI
Wen-Hui Fang
Community PI
Chia-Jung Hsu
Engineer
Yu-Juei Hsu
Clinical PI
Shi-Hung Tsai
Emergency PI
Chia-Cheng Lee
IT manager
Sy-Jou Chen
Emergency physician
Wen-Yu Lin
Cardiologist
Ban-Yen Liu
Cardiologist
Wen-Cheng Liu
Cardiologist
Da-Wei Chang
Cardiologist
Yu-Lan Liu
Cardiologist
Ping-Huang Tsai
Nephrologist
Chiao-Hsiang Chang
Cardiologist
Hung-Yi Chen
Cardiologist
Chiao-Chin Lee
Cardiologist
Wei-Ting Liu
Cardiologist
Yu-Cheng Chen
Cardiologist
Summary
1npj Digital Medicine, 5(1):8.
2Clin Chim Acta, 536:126-134.
3JMIR Med Inform, 8(3):e15931.
4EuroIntervention, 17(9):765-773.
5J Pers Med, 11(11):1149.
6Can J Cardiol, 38(2):160-168.
7J Pers Med, 12(7):1150.
8Eur J Trauma Emerg Surg, 48(4):3317-3326.
9Int J Environ Res Public Health, 18(7), 3839.
10J Endocr Soc, 5(9):bvab120.
11J Med Syst, 47(1):81.
12J Pers Med, 12(3):455.
13Front Med, 9:870523.
14J Pers Med, 11(8):725.
15Front Cardiovasc Med, 9:895201.
16J Pers Med, 12(2):315.
17Digital Health, 9:20552076231187247.
18Front Cardiovasc Med, 9:754909.
19Diagnostics, 13:2723.
20Acta Cardiol Sin, 39(6):913–928.
21We shared our works to the Discovery.
Algorithm development
Participants
Chin Lin
Adviser
Yu-Sheng Luo
Post doct
Chun-Ho Lee
Master student
Summary
Deep learning is a data-driven algorithm, so it is necessary to understand the limitations of medical data and design suitable algorithms to build an accurate medical artificial intelligence system. We consider that main differences between medical field and other fields and solutions are as follows:
1. Unsupervised learning aided supervised learning to solve the rare data problem: Because the medical data involves manual operations and expensive instruments, the amount of labeled data is scarce compared with other industries. Moreover, the incidence and prevalence of more fatel diseases is lower compared with other common diseases. We need to face this important limitation rather than selecting sufficient data for research. Transfer learning is a common solution in deep learning training that can effectively reduce the number of samples required. Our main work is to use a large amount of unlabeled data for unsupervised algorithms, and apply them to supervised learning.1
2. Combination of multi-level statistical models and deep learning to create personalized artificial intelligence: The response of each indivial to the same treatment is different, so it is impossible to use one artificial intelligence models for accurately appling in everyone. Therefore, it is very important to establish a personalized artificial intelligence model, but it is difficult to train each person's data separately in medical field. The multi-level data analysis in statistics shows how to use the distribution assumptions to construct personalized predictions. Even if only one record also can be used to construct a personalized model, our research attempts to apply this concept to deep learning training. The new age medical artificial intelligence can dimamicly revise the prediction results.2
3. To design special prediction functions and loss functions: When using artificial intelligence models in the medical field, interpretability and confidence issues often arise. For example, if the data quality is poor, it may be advisable for frontline operators to perform the check again immediately. This is particularly important in prospective trials.3
4. Special accuracy evaluation metric: Medical artificial intelligence research often ignores the problem of variable dependence in medical data, such as the relationship between electrocardiograms and age and gender, and the relationship between gender and age and bone density, leading to confusion about whether the relationship between electrocardiograms and bone density is based on a real causal relationship. Because of the powerful feature extraction ability of deep learning models, AI currently often learns false correlations to establish predictive logic, which will reduce the accuracy of external validation in the future, so it is necessary to design appropriate evaluation indicators for this problem.4
1Comput Methods Programs Biomed, 231:107359.
2Eur Heart J-DH, ztac072.
3Digital Health, 8:20552076221143249.
4Unpublished data.
Medical image analysis
Participants
Chin Lin
Adviser
Yi-Ying Wu
Hematologist
Dung-Jang Tsai
Research fellow
Yu-Sheng Luo
PhD student
Ying-Chu Chen
MS student
Xin-An Lin
Engineer
Yu-Cheng Chen
Cardiologist
Hao-Chun Liao
BS student
Summary
Reference:
1JMIR Med Inform, 8(4):e15963.
2Can J Cardiol, 38(6):763-773.
3Digital Health, 9:20552076231191055.
4Can J Cardiol, 38(2):160-168.
5J Med Syst, 47(1):81.
6J Med Syst, 48(1):12.
Medical Natural Language Processing
Participants
Chin Lin
Adviser
Wen-Hui Fang
Clinical expert
Dung-Jang Tsai
Research fellow
Yu-Sheng Luo
PhD student
Xin-An Lin
Engineer
Summary
Reference:
1J Med Internet Res, 19(11):e380.
2JMIR Med Inform, 7(3):e14499.
3Healthcare, 9(10):1298.