Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions
Technion – Israel Institute of Technology
Abstract
Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they…
Citation impact
- FWCI
- 26.88
- Percentile
- 100%
- References
- 151
Authors
4Topics & keywords
- Point-of-care testing
- Point of care
- Biosensor
- Computer science
- Point (geometry)
- Risk analysis (engineering)
- Intensive care medicine
- Biochemical engineering