An appropriate and cost-effective hospital recommender system for a patient of rural area using deep reinforcement learning
Insufficient doctors and nurses enable a weak healthcare system in developing and undeveloped countries. This study aims to mitigate the demand-supply gap of doctor patients of an undeveloped or developing county. We observe people in a rural area, unaware of an appropriate hospital and doctors for...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-05-01
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| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305323000431 |
| _version_ | 1797804529287168000 |
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| author | Rajesh K. Jha Sujoy Bag Debbani Koley Giridhar Reddy Bojja Subhas Barman |
| author_facet | Rajesh K. Jha Sujoy Bag Debbani Koley Giridhar Reddy Bojja Subhas Barman |
| author_sort | Rajesh K. Jha |
| collection | DOAJ |
| description | Insufficient doctors and nurses enable a weak healthcare system in developing and undeveloped countries. This study aims to mitigate the demand-supply gap of doctor patients of an undeveloped or developing county. We observe people in a rural area, unaware of an appropriate hospital and doctors for their disease, and randomly go to the nearest hospital to check-up their health. However, each doctor has expertise in a specific disease, and hospitals' treatment performance varies. As a result, the patient engages multiple doctors and hospitals to cure their disease. This study develops as an appropriate and cost-effective hospital recommender system for a specific disease to provide the best hospital to a patient using deep reinforcement learning. Hence, the patient's treatment time, insignificant medicine consumption, the side effect of using inappropriate medicine, and a doctor's load can be minimized using the developed hospital recommender system. |
| first_indexed | 2024-03-13T05:38:33Z |
| format | Article |
| id | doaj.art-5d8efb436f6042ff992d076ef4d656dd |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-03-13T05:38:33Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-5d8efb436f6042ff992d076ef4d656dd2023-06-14T04:34:52ZengElsevierIntelligent Systems with Applications2667-30532023-05-0118200218An appropriate and cost-effective hospital recommender system for a patient of rural area using deep reinforcement learningRajesh K. Jha0Sujoy Bag1Debbani Koley2Giridhar Reddy Bojja3Subhas Barman4BNM Institute of Technology, IndiaIndian Institute of Technology, Kharagpur, India; Corresponding author.College of Medicine & Sagore Dutta Hospital, IndiaDakota State University, Madison, SD, USAJalpaiguri Government Engineering College, IndiaInsufficient doctors and nurses enable a weak healthcare system in developing and undeveloped countries. This study aims to mitigate the demand-supply gap of doctor patients of an undeveloped or developing county. We observe people in a rural area, unaware of an appropriate hospital and doctors for their disease, and randomly go to the nearest hospital to check-up their health. However, each doctor has expertise in a specific disease, and hospitals' treatment performance varies. As a result, the patient engages multiple doctors and hospitals to cure their disease. This study develops as an appropriate and cost-effective hospital recommender system for a specific disease to provide the best hospital to a patient using deep reinforcement learning. Hence, the patient's treatment time, insignificant medicine consumption, the side effect of using inappropriate medicine, and a doctor's load can be minimized using the developed hospital recommender system.http://www.sciencedirect.com/science/article/pii/S2667305323000431HospitalDoctorsPatientsRecommender systemsMonte Carlo learningDeep reinforcement learning |
| spellingShingle | Rajesh K. Jha Sujoy Bag Debbani Koley Giridhar Reddy Bojja Subhas Barman An appropriate and cost-effective hospital recommender system for a patient of rural area using deep reinforcement learning Intelligent Systems with Applications Hospital Doctors Patients Recommender systems Monte Carlo learning Deep reinforcement learning |
| title | An appropriate and cost-effective hospital recommender system for a patient of rural area using deep reinforcement learning |
| title_full | An appropriate and cost-effective hospital recommender system for a patient of rural area using deep reinforcement learning |
| title_fullStr | An appropriate and cost-effective hospital recommender system for a patient of rural area using deep reinforcement learning |
| title_full_unstemmed | An appropriate and cost-effective hospital recommender system for a patient of rural area using deep reinforcement learning |
| title_short | An appropriate and cost-effective hospital recommender system for a patient of rural area using deep reinforcement learning |
| title_sort | appropriate and cost effective hospital recommender system for a patient of rural area using deep reinforcement learning |
| topic | Hospital Doctors Patients Recommender systems Monte Carlo learning Deep reinforcement learning |
| url | http://www.sciencedirect.com/science/article/pii/S2667305323000431 |
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