Data-based Decision Rules to Personalize Depression Follow-up.
Data-based Decision Rules to Personalize Depression Follow-up.
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DOI：
10.1038/s41598-018-23326-1
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发表时间：
2018-03-22
影响因子：
4.6
通讯作者：
Liu S
中科院分区：
文献类型：
--
作者：
Lin Y;Huang S;Simon GE;Liu S
抑郁症是一种常见的精神疾病，具有复杂和异质的进展动力学。根据纵向模式对抑郁症治疗人群进行风险分组，有可能实现具有成本效益的监测政策设计。本文建立了一种基于规则的方法，通过整合数据转换，规则发现和规则评估，从个人层面的纵向疾病测量识别一组风险预测模式。我们进一步扩展所确定的规则，以创建基于规则的监测策略，以适应性地监测具有不同疾病严重程度的个体。我们将基于规则的方法应用于抑郁症治疗人群的电子健康记录（EHR）数据集，该数据集包含个人水平的纵向患者健康问卷（PHQ）-9评分，用于评估抑郁症的严重程度。识别出12个风险预测规则，并且基于所识别的规则的基于规则的预后模型能够比其他预后模型（包括RuleFit、逻辑回归和支持向量机）更准确地预测疾病严重程度。两种基于规则的监测策略通过提供更高的灵敏度和特异性而优于最新的基于PHQ-9的监测策略。基于规则的方法可以更好地理解疾病动力学，实现更准确的疾病进展特征，个性化随访间隔，并在临床实践中设计具有成本效益的患者监测。
Depression is a common mental illness with complex and heterogeneous progression dynamics. Risk grouping of depression treatment population based on their longitudinal patterns has the potential to enable cost-effective monitoring policy design. This paper establishes a rule-based method to identify a set of risk predictive patterns from person-level longitudinal disease measurements by integrating the data transformation, rule discovery and rule evaluation. We further extend the identified rules to create rule-based monitoring strategies to adaptively monitor individuals with different disease severities. We applied the rule-based method on an electronic health record (EHR) dataset of depression treatment population containing person-level longitudinal Patient Health Questionnaire (PHQ)-9 scores for assessing depression severity. 12 risk predictive rules are identified, and the rule-based prognostic model based on identified rules enables more accurate prediction of disease severity than other prognostic models including RuleFit, logistic regression and Support Vector Machine. Two rule-based monitoring strategies outperform the latest PHQ-9 based monitoring strategy by providing higher sensitivity and specificity. The rule-based method can lead to a better understanding of disease dynamics, achieving more accurate prognostics of disease progressions, personalizing follow-up intervals, and designing cost-effective monitoring of patients in clinical practice.
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DOI：
10.1136/amiajnl-2014-002733
发表时间：
2014-11
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Huang SH;LePendu P;Iyer SV;Tai-Seale M;Carrell D;Shah NH
通讯作者：
Shah NH
影响因子：
17.7
作者：
Simon, GE;Katon, WJ;Ludman, E
通讯作者：
Ludman, E
影响因子：
--
作者：
King, Michael;Walker, Carl;Nazareth, Irwin
通讯作者：
Nazareth, Irwin
影响因子：
6.6
作者：
Oquendo, Maria A.;Turret, Jason;Grunebaum, Michael F.;Burke, Ainsley K.;Poh, Ernest;Stevenson, Ellen;Mann, J. John;Galfalvy, Hanga
通讯作者：
Galfalvy, Hanga
DOI：
10.1176/appi.ps.201200587
发表时间：
2013-12-01
期刊：
Psychiatric services (Washington, D.C.)
影响因子：
--
作者：
Simon GE;Rutter CM;Peterson D;Oliver M;Whiteside U;Operskalski B;Ludman EJ
通讯作者：
Ludman EJ