An Artificial Intelligent-Based Chatbot for Dosage Prediction of Medicine Using Noval Deep Reinforcement Learning with Natural Language Processing
The aim is to create an artificial conversation entity (chatbot) using Python to predict dosage of medicine for healthcare treatments. Materials and Methods: Two algorithms, deep reinforcement algorithms, are compared with deep learning algorithm sample size taken 28. G power of 80% and sample size...
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| Published in | ECS transactions Vol. 107; no. 1; pp. 14841 - 14853 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
The Electrochemical Society, Inc
24.04.2022
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| Online Access | Get full text |
| ISSN | 1938-5862 1938-6737 |
| DOI | 10.1149/10701.14841ecst |
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| Abstract | The aim is to create an artificial conversation entity (chatbot) using Python to predict dosage of medicine for healthcare treatments. Materials and Methods: Two algorithms, deep reinforcement algorithms, are compared with deep learning algorithm sample size taken 28. G power of 80% and sample size is calculated using G power tool. Results and Discussion: Performances of the score model validated test set accuracy with 95% confidence interval for deep reinforcement algorithm with different sub-samples having different number of intents comparing with deep learning, which has 85% accuracy. Conclusion: From the results, it is concluded that proposed algorithm deep reinforcement will produce better results than the existing algorithm. |
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| AbstractList | The aim is to create an artificial conversation entity (chatbot) using Python to predict dosage of medicine for healthcare treatments. Materials and Methods: Two algorithms, deep reinforcement algorithms, are compared with deep learning algorithm sample size taken 28. G power of 80% and sample size is calculated using G power tool. Results and Discussion: Performances of the score model validated test set accuracy with 95% confidence interval for deep reinforcement algorithm with different sub-samples having different number of intents comparing with deep learning, which has 85% accuracy. Conclusion: From the results, it is concluded that proposed algorithm deep reinforcement will produce better results than the existing algorithm. |
| Author | B, Phani Raghava Ashok Kumar, S. |
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| Title | An Artificial Intelligent-Based Chatbot for Dosage Prediction of Medicine Using Noval Deep Reinforcement Learning with Natural Language Processing |
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