基于机器学习的医疗紧急情况药物推荐系统

C. Silpa, B. Sravani, D. Vinay, C. Mounika, K. Poorvitha
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引用次数: 0

摘要

在线推荐系统越来越多地被用于医院、医疗专业人员和药品。今天,绝大多数消费者在向医生咨询各种健康状况的处方建议之前都会上网查询。当流行病、洪水或飓风来袭时,医疗建议系统可能很有价值。在机器学习(ML)时代,推荐系统在使用更少资源的同时提供更准确、精确和可靠的临床预测。药物推荐系统为患者提供有关药物、剂量和任何可能的不良反应的可靠信息。根据病人的症状、血压、糖尿病、体温和其他参数给药。药物推荐系统在决策过程中随时提供精确的信息,同时提高患者数据的性能、完整性和隐私性。推荐系统中,决策树产生最准确的结果。在医疗紧急情况下,药物推荐系统有助于向患者推荐安全药物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Drug Recommendation System in Medical Emergencies using Machine Learning
Online recommender systems are being used increasingly often for hospitals, medical professionals, and drugs. Today, the great majority of consumers look online before asking their doctors for prescription suggestions for a range of health conditions. The medical suggestion system can be valuable when pandemics, floods, or cyclones hit. In the age of Machine Learning (ML), recommender systems give more accurate, precise, and reliable clinical predictions while using less resources. The medicine recommendation system gives the patient reliable information about the medication, the dosage, and any possible adverse effects. Medication is given based on the patient's symptoms, blood pressure, diabetes, temperature, and other parameters. Drug recommendation systems provide precise information at any time while improving the performance, integrity, and privacy of patient data in the decision-making process. Recommender system, the decision tree produces the most accurate results. In times of medical emergency, a drug recommendation system is helpful for giving patients recommendations for safe medications.
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