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首页> 外文会议>23rd IEEE International Symposium on Computer-Based Medical Systems >Data preparation for clinical data mining to identify patients at risk of readmission
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Data preparation for clinical data mining to identify patients at risk of readmission

机译：用于临床数据挖掘的数据准备，以识别有再次入院风险的患者

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Steadily rising numbers of emergency (unplanned) inpatient admissions have been the major source of pressure on the NHS over the past twenty years. There is currently still a strong need for a consistent predictive tool and an automation of the development of re-admission risk profiles, in particular, one that addresses both data preparation and predictive modelling. This paper proposes a data preparation framework for transforming raw transactional clinical data to well-formed data sets so that data mining can be applied. In this framework, rules are created according to the statistical characteristics of the data, the metadata that characterises the host information systems and medical knowledge. These rules can be used for data pre-processing, attribute selection and data transformation in order to generate appropriately prepared data sets. The proposed data preparation framework incorporates automatic methods with heuristic pre-processing treatments for the potential challenges within a large-scaled development and its applicability is not limited to clinical data.
机译：在过去的20年中，急诊（计划外）住院病人的数量稳定增长，这是NHS面临的主要压力。当前，仍然非常需要一致的预测工具和重新入场风险概况开发的自动化，特别是同时处理数据准备和预测建模的工具。本文提出了一种将原始交易临床数据转换为格式正确的数据集的数据准备框架，以便可以应用数据挖掘。在此框架中，根据数据的统计特性，表征宿主信息系统的元数据和医学知识创建规则。这些规则可用于数据预处理，属性选择和数据转换，以便生成适当准备的数据集。拟议的数据准备框架将自动方法与启发式预处理相结合，以应对大规模开发中的潜在挑战，并且其适用性不限于临床数据。

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