Generative AI-Based Data Completeness Augmentation Algorithm for Data-Driven Smart Healthcare

In the decade, artificial intelligence has achieved great popularity and applications in medicine and healthcare. Various AI-based algorithms have shown astonishing performance. However, in various data-driven smart healthcare algorithms, the problem of incomplete dataset remains a huge challenge. In this paper, we propose a data completeness enhancement algorithm based on generative AI (i. e. , GenAI-DAA) to solve the problems of the in-sufficient data for model training, the data imbalance, and the biases of the training samples. We first construct the cognitive field of the generative models and effectively understand the state of incomplete cognition in generative models. Secondly, on this basis, we propose a quest algorithm for abnormal samples in the cognitive field based on local outlier factor. By fine-grained value evaluation, abnormal samples are given more refined attention. Finally, integrating the above process through multiple cognitive adjustments, GenAI-DAA gradually improves the cognitive ability. GenAI-DAA can be summarized as “Quest $ \longrightarrow$ Estimate $ \longrightarrow$ Tune-up”. We have conducted extensive experiments to demonstrate the effectiveness of our proposed algorithm, and shown widely applications to some typical data-driven smart healthcare algorithms.

Authors

• Guipeng Lan Tianjin Unversity
• Shuai Xiao Tianjin Univesity
• Jiachen Yang Lawrence Livermore National Laboratory
• Jiabao Wen Tianjin University
• Meng Xi

Keywords

• Data models
• Medical services
• Image enhancement
• Bioinformatics
• Training
• Cognition
• Training data
• Complete Data
• Smart Healthcare
• AI-based Algorithms
• Artificial Intelligence
• Local Factors
• Field Samples
• Field Model
• Imbalanced Data
• Data-driven Algorithms
• Abnormal Samples
• Enhancement Algorithm
• Smart Algorithm
• Model Performance
• Deep Learning
• Data Distribution
• Medical Imaging
• Training Dataset
• Medical Systems
• Medical Data
• Style Transfer
• Chest X-ray Images
• Well-trained Model
• Pseudo Data
• T1-weighted Images
• Development Of Deep Learning
• Degree Of Samples
• Data Manifold
• Eye Disease
• Generative AI
• Data completeness
• data-driven
• Algorithms
• Humans
• Medical Informatics
• Delivery of Health Care