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Data Science to Improve Patient Management System

机译:数据科学改善患者管理系统

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The rate at which people miss hospital appointments has decreased but remains a big concern for health care professionals as well as funding agencies. This research paper used an open data obtained from the NHS database to determine the factors that may lead to missed appointments and create a model that can be used to predict the likelihood of a patient missing an appointment. Logistic regression models and bivariate analysis were used to determine whether there was a meaningful relationship/association between "did not attend" and forgetfulness, gender, apathy, and transportation. An extensive literature review was conducted to narrow down the reasons that might lead to missed appointments. In conclusion, the research showed there was a significant difference between gender, type of clinic and apathy in organizations.
机译:人们错过医院预约的比率有所下降,但仍然是医疗保健专业人员和资助机构的主要关切。该研究论文使用从NHS数据库获得的开放数据来确定可能导致错过约会的因素,并创建可用于预测患者错过约会的可能性的模型。使用逻辑回归模型和双变量分析来确定“不参加”与健忘,性别,冷漠和交通之间是否存在有意义的关系/关联。进行了广泛的文献综述,以缩小可能导致约会失败的原因。总之,研究表明,性别,诊所类型和组织的冷漠之间存在显着差异。

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