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Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

机译：用于患者内坠落预测模型和高风险亚组的挖掘成瘾评估数据

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Background Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). Methods A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. Results The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Conclusions Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.
机译：背景技术在成本和后果方面构成了患者的患者坠落。老年人的机构通常受到影响，常见的筛查工具不能预测患者持续下降。我们的目标是从大型患者评估数据的大型数据集中获得可理解的秋季风险分类模型，并评估其预测性能（AIM＃1），并从数据中识别高风险子组（AIM＃2） 。方法从医院的数据库中提取涵盖1.5年的单次患者的数据集N = 5,176个单次患者发作，占对老年医院的1.5岁的录取，并与秋季事件报告（n = 493）匹配。使用C4.5算法以及Logistic回归模型引起分类树模型，评估其预测性能。此外，从提取的分类规则中识别出高风险的子组，其支持超过100个实例。结果分类树模型显示总分类精度为66％，灵敏度为55.4％，特异性为67.1％，正负预测值为15％。 93.5％。鉴定了五个高风险群体，由高龄，低标准指数，认知障碍，多药物和共发病率定义。结论我们的结果表明，我们的模型可以正确地确定一半以上的衰落，但阳性预测值太低而无法适用。另一方面，非衰落者可能会很好地分类模型。确定的高风险亚组和危险因素（年龄，低ADL评分，认知障碍，制度化，复数和共同发病）反映了域知识，可用于筛选具有高风险落下的患者的某些亚组。使用数据挖掘方法的大数据集导出的分类模型可以与当前的专用秋季风险筛选工具竞争，但缺乏诊断精度。可以从现有的Gerittric评估数据中自动识别高风险的子组，尤其是在混合分类模型中结合域知识时。进一步的工作是在受控潜在的环境中验证我们的方法。

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