Machine Learning in Healthcare Communication
Definition
:1. Introduction
2. Machine Learning Technology
2.1. Natural Language Processing (NLP)
2.2. Deep Neural Network (DNN)
3. Application of Machine Learning in Healthcare Communication
3.1. Overview of Chatbot
3.2. Patient Care
3.3. Radiology and Radiotherapy
3.4. Education and Knowledge Transfer System
3.5. Emergency Response and COVID-19
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Entry Link on the Encyclopedia Platform
References
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System Name | Brief Description |
---|---|
ASLForm | It is an adaptive learning system that has some fundamental rules for finding a target text. As a user selects output, it continuously and simultaneously updates. |
COAT | It is a clinical note processing system that is rule-based and uses machine learning (through WEKA) components with the integration of MetaMap Transfer. |
LEXIMER | It was implemented to render medical imaging and has the ability to find significant recommendations and clinical findings from CT and MRI reports. |
Barrett et al. (unnamed) | It can identify 17 serious sentinel events such as sepsis, dyspnea, and delirium in palliative carte consult letters. |
Martinez et al. (unnamed) | It takes NegEx, Genia Tagger, and MetaMap as input and can classify cancer staging pathology reports. |
Otal et al. 2013 (unnamed) | It can detect T cancer staging classification. It uses WEKA. |
Wieneke et al. 2015 (unnamed) | It can extract results, laterality and procedure from breast pathology reports, and if high NPV and high PPV classifiers do not agree, then it is sent for manual review. |
Machine Learning Algorithm | RMSE | R-Squared | Prediction Speed (Observation/s) | Training Time (s) |
---|---|---|---|---|
Square Exponential GPR | 0.0038 | 0.99 | 4100 | 0.18 |
Matern 5/2 GPR | 0.0038 | 0.99 | 3800 | 0.21 |
Rational Quadratic GPR | 0.0038 | 0.99 | 2700 | 0.23 |
Linear Regression | 0.0045 | 0.98 | 1700 | 0.37 |
Exponential GPR | 0.0125 | 0.87 | 3900 | 0.18 |
Linear SVM | 0.0123 | 0.87 | 4500 | 0.21 |
Quadratic SVM | 0.0151 | 0.81 | 3400 | 0.13 |
Cubic SVM | 0.0193 | 0.68 | 4700 | 0.11 |
Fine Tree | 0.0218 | 0.60 | 4600 | 0.10 |
Medium Tree | 0.0305 | 0.21 | 4600 | 0.42 |
Coarse Tree | 0.0344 | 0.00 | 5600 | 0.09 |
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Siddique, S.; Chow, J.C.L. Machine Learning in Healthcare Communication. Encyclopedia 2021, 1, 220-239. https://doi.org/10.3390/encyclopedia1010021
Siddique S, Chow JCL. Machine Learning in Healthcare Communication. Encyclopedia. 2021; 1(1):220-239. https://doi.org/10.3390/encyclopedia1010021
Chicago/Turabian StyleSiddique, Sarkar, and James C. L. Chow. 2021. "Machine Learning in Healthcare Communication" Encyclopedia 1, no. 1: 220-239. https://doi.org/10.3390/encyclopedia1010021
APA StyleSiddique, S., & Chow, J. C. L. (2021). Machine Learning in Healthcare Communication. Encyclopedia, 1(1), 220-239. https://doi.org/10.3390/encyclopedia1010021