Abstract
Many diseases are heterogeneous and dynamics in nature. Therefore, constant feedback of the related physiological signs and blood biomarkers from patients is essential to achieve optimal treatment effects. Moreover, procedural factors are also important to determine treatment outcomes. Both factors can be more easily recorded and streamlined nowadays by wearable sensors. Nevertheless, interpretation of the vast amount of data is becoming difficult. In this regard, advanced machine learning techniques such as deep learning have great potential to digitize the intertwined relationships and to predict the clinical endpoints. This chapter discusses the basic principles of wearable sensors, artificial intelligence technologies for them and their application for the management of acute pancreatitis in precision medicine.
| Original language | English |
|---|---|
| Title of host publication | Encyclopedia of Sensors and Biosensors |
| Subtitle of host publication | Volume 1-4, First Edition |
| Publisher | Elsevier |
| Pages | 1-16 |
| Number of pages | 16 |
| Volume | 3 |
| ISBN (Electronic) | 9780128225486 |
| ISBN (Print) | 9780128225493 |
| DOIs | |
| Publication status | Published - 2023 |
Keywords
- AI-doscopist
- Artificial intelligence
- Big data analytics
- Biliary strictures
- Deep networks
- Endoscopic informatics
- Gallstone pancreatitis
- Health informatics
- Machine learning
- Post-ERCP pancreatitis
- Predictive analytics
- Sensor networks
- Therapeutic endoscopy
- Unobtrusive sensing
- Wearable sensing