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Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning
Precision medicine is a new approach to understanding health and disease based on patient-specific data such as medical diagnoses; clinical phenotype; biologic investigations such as laboratory studies and imaging; and environmental, demographic, and lifestyle factors. The importance of machine lear...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8781402/ https://www.ncbi.nlm.nih.gov/pubmed/35055402 http://dx.doi.org/10.3390/jpm12010087 |
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author | Oh, Sang Ho Lee, Su Jin Park, Jongyoul |
author_facet | Oh, Sang Ho Lee, Su Jin Park, Jongyoul |
author_sort | Oh, Sang Ho |
collection | PubMed |
description | Precision medicine is a new approach to understanding health and disease based on patient-specific data such as medical diagnoses; clinical phenotype; biologic investigations such as laboratory studies and imaging; and environmental, demographic, and lifestyle factors. The importance of machine learning techniques in healthcare has expanded quickly in the last decade owing to the rising availability of vast multi-modality data and developed computational models and algorithms. Reinforcement learning is an appealing method for developing efficient policies in various healthcare areas where the decision-making process is typically defined by a long period or a sequential process. In our research, we leverage the power of reinforcement learning and electronic health records of South Koreans to dynamically recommend treatment prescriptions, which are personalized based on patient information of hypertension. Our proposed reinforcement learning-based treatment recommendation system decides whether to use mono, dual, or triple therapy according to the state of the hypertension patients. We evaluated the performance of our personalized treatment recommendation model by lowering the occurrence of hypertension-related complications and blood pressure levels of patients who followed our model’s recommendation. With our findings, we believe that our proposed hypertension treatment recommendation model could assist doctors in prescribing appropriate antihypertensive medications. |
format | Online Article Text |
id | pubmed-8781402 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87814022022-01-22 Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning Oh, Sang Ho Lee, Su Jin Park, Jongyoul J Pers Med Article Precision medicine is a new approach to understanding health and disease based on patient-specific data such as medical diagnoses; clinical phenotype; biologic investigations such as laboratory studies and imaging; and environmental, demographic, and lifestyle factors. The importance of machine learning techniques in healthcare has expanded quickly in the last decade owing to the rising availability of vast multi-modality data and developed computational models and algorithms. Reinforcement learning is an appealing method for developing efficient policies in various healthcare areas where the decision-making process is typically defined by a long period or a sequential process. In our research, we leverage the power of reinforcement learning and electronic health records of South Koreans to dynamically recommend treatment prescriptions, which are personalized based on patient information of hypertension. Our proposed reinforcement learning-based treatment recommendation system decides whether to use mono, dual, or triple therapy according to the state of the hypertension patients. We evaluated the performance of our personalized treatment recommendation model by lowering the occurrence of hypertension-related complications and blood pressure levels of patients who followed our model’s recommendation. With our findings, we believe that our proposed hypertension treatment recommendation model could assist doctors in prescribing appropriate antihypertensive medications. MDPI 2022-01-11 /pmc/articles/PMC8781402/ /pubmed/35055402 http://dx.doi.org/10.3390/jpm12010087 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Oh, Sang Ho Lee, Su Jin Park, Jongyoul Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning |
title | Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning |
title_full | Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning |
title_fullStr | Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning |
title_full_unstemmed | Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning |
title_short | Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning |
title_sort | precision medicine for hypertension patients with type 2 diabetes via reinforcement learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8781402/ https://www.ncbi.nlm.nih.gov/pubmed/35055402 http://dx.doi.org/10.3390/jpm12010087 |
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