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AI and deep learning for processing the huge amount of patient-centric data that assist in clinical decisions
Resource type
Authors/contributors
- Lôbo Marques, João Alexandre (Author)
- Bernardo Gois, Francisco Nauber (Author)
- Nunes da Silveira, Jarbas Aryel (Author)
- Li, Tengyue (Author)
- Fong, Simon James (Author)
- Bhoi, Akash Kumar (Editor)
- de Albuquerque, Victor Hugo C. (Editor)
- Srinivasu, Parvathaneni Naga (Editor)
- Marques, Gonçalo (Editor)
Title
AI and deep learning for processing the huge amount of patient-centric data that assist in clinical decisions
Abstract
The area of clinical decision support systems (CDSS) is facing a boost in research and development with the increasing amount of data in clinical analysis together with new tools to support patient care. This creates a vibrant and challenging environment for the medical and technical staff. This chapter presents a discussion about the challenges and trends of CDSS considering big data and patient-centered constraints. Two case studies are presented in detail. The first presents the development of a big data and AI classification system for maternal and fetal ambulatory monitoring, composed by different solutions such as the implementation of an Internet of Things sensors and devices network, a fuzzy inference system for emergency alarms, a feature extraction model based on signal processing of the fetal and maternal data, and finally a deep learning classifier with six convolutional layers achieving an F1-score of 0.89 for the case of both maternal and fetal as harmful. The system was designed to support maternal–fetal ambulatory premises in developing countries, where the demand is extremely high and the number of medical specialists is very low. The second case study considered two artificial intelligence approaches to providing efficient prediction of infections for clinical decision support during the COVID-19 pandemic in Brazil. First, LSTM recurrent neural networks were considered with the model achieving R2=0.93 and MAE=40,604.4 in average, while the best, R2=0.9939, was achieved for the time series 3. Second, an open-source framework called H2O AutoML was considered with the “stacked ensemble” approach and presented the best performance followed by XGBoost. Brazil has been one of the most challenging environments during the pandemic and where efficient predictions may be the difference in saving lives. The presentation of such different approaches (ambulatory monitoring and epidemiology data) is important to illustrate the large spectrum of AI tools to support clinical decision-making.
Book Title
Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data
Series
Intelligent Data-Centric Systems
Publisher
Academic Press
Date
2022-01-01
Pages
101-121
Language
en
ISBN
978-0-323-85751-2
Accessed
9/21/22, 4:34 AM
Library Catalog
ScienceDirect
Extra
Citation
Lôbo Marques, J. A., Bernardo Gois, F. N., Nunes da Silveira, J. A., Li, T., & Fong, S. J. (2022). AI and deep learning for processing the huge amount of patient-centric data that assist in clinical decisions. In A. K. Bhoi, V. H. C. de Albuquerque, P. N. Srinivasu, & G. Marques (Eds.), Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data (pp. 101–121). Academic Press. https://doi.org/10.1016/B978-0-323-85751-2.00001-3
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