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本文引用的文献

1
Prediction model for demands of the health meteorological information using a decision tree method.使用决策树方法预测健康气象信息需求模型。
Asian Nurs Res (Korean Soc Nurs Sci). 2010 Sep;4(3):151-62. doi: 10.1016/S1976-1317(10)60015-1.
2
Evolving forecasting classifications and applications in health forecasting.健康预测中不断发展的预测分类和应用。
Int J Gen Med. 2012;5:381-9. doi: 10.2147/IJGM.S31079. Epub 2012 May 8.
3
Asthma length of stay in hospitals in London 2001-2006: demographic, diagnostic and temporal factors.2001-2006 年伦敦医院哮喘住院时间:人口统计学、诊断和时间因素。
PLoS One. 2011;6(11):e27184. doi: 10.1371/journal.pone.0027184. Epub 2011 Nov 2.
4
Predicting emergency department admissions.预测急诊科收治量。
Emerg Med J. 2012 May;29(5):358-65. doi: 10.1136/emj.2010.103531. Epub 2011 Jun 24.
5
Forecasting diabetes prevalence in California: a microsimulation.加利福尼亚州糖尿病患病率预测:微观模拟。
Prev Chronic Dis. 2011 Jul;8(4):A80. Epub 2011 Jun 15.
6
Assessing the suitability of fractional polynomial methods in health services research: a perspective on the categorization epidemic.评估分位数多项式方法在卫生服务研究中的适用性:对分类流行的看法。
J Health Serv Res Policy. 2011 Jul;16(3):147-52. doi: 10.1258/jhsrp.2010.010063. Epub 2011 May 4.
7
Reflections of the Hippocratic Oath in modern medicine.《希波克拉底誓言》在现代医学中的体现。
World J Surg. 2010 Dec;34(12):3075-9. doi: 10.1007/s00268-010-0604-3.
8
Implementing an emergency department patient admission predictive tool: insights from practice.实施急诊患者入院预测工具:实践中的见解。
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9
Can a health forecasting service offer COPD patients a novel way to manage their condition?健康预测服务能否为 COPD 患者提供一种管理病情的新方法?
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10
Patients' and staffs' experiences of an automated telephone weather forecasting service.患者和工作人员对自动化电话天气预报服务的体验。
J Health Serv Res Policy. 2010 Apr;15 Suppl 2:41-6. doi: 10.1258/jhsrp.2009.009101. Epub 2010 Feb 10.

健康预测概述。

An overview of health forecasting.

机构信息

Global Public Health, Jeffrey Cheah School of Medicine and Health Sciences, Monash University, 46150, Bandar Sunway, Selangor, Malaysia.

出版信息

Environ Health Prev Med. 2013 Jan;18(1):1-9. doi: 10.1007/s12199-012-0294-6. Epub 2012 Jul 28.

DOI:10.1007/s12199-012-0294-6
PMID:22949173
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3541816/
Abstract

Health forecasting is a novel area of forecasting, and a valuable tool for predicting future health events or situations such as demands for health services and healthcare needs. It facilitates preventive medicine and health care intervention strategies, by pre-informing health service providers to take appropriate mitigating actions to minimize risks and manage demand. Health forecasting requires reliable data, information and appropriate analytical tools for the prediction of specific health conditions or situations. There is no single approach to health forecasting, and so various methods have often been adopted to forecast aggregate or specific health conditions. Meanwhile, there are no defined health forecasting horizons (time frames) to match the choices of health forecasting methods/approaches that are often applied. The key principles of health forecasting have not also been adequately described to guide the process. This paper provides a brief introduction and theoretical analysis of health forecasting. It describes the key issues that are important for health forecasting, including: definitions, principles of health forecasting, and the properties of health data, which influence the choices of health forecasting methods. Other matters related to the value of health forecasting, and the general challenges associated with developing and using health forecasting services are discussed. This overview is a stimulus for further discussions on standardizing health forecasting approaches and methods that will facilitate health care and health services delivery.

摘要

健康预测是预测领域的一个新兴领域,是预测未来健康事件或情况(如对医疗服务的需求和医疗保健需求)的一种有价值的工具。它通过预先通知医疗服务提供者采取适当的缓解措施来最小化风险并管理需求,促进了预防医学和医疗保健干预策略。健康预测需要可靠的数据、信息和适当的分析工具来预测特定的健康状况或情况。没有单一的健康预测方法,因此通常采用各种方法来预测总体或特定的健康状况。同时,也没有定义明确的健康预测时间范围(时间框架)来匹配经常应用的健康预测方法/方法的选择。健康预测的关键原则也没有得到充分描述,无法指导这一过程。本文对健康预测进行了简要介绍和理论分析。它描述了健康预测的关键问题,包括:定义、健康预测的原则,以及影响健康预测方法选择的健康数据特性。还讨论了与健康预测的价值相关的其他事项,以及与开发和使用健康预测服务相关的一般挑战。这一概述为进一步讨论标准化健康预测方法和方法提供了动力,这将有助于医疗保健和医疗服务的提供。