IMPROVING THE PREDICTION OF DANGEROUSNESS

临床医生继续需要做出法律的决定 和常规临床目的。 因此，研究需要集中在开发 更好地完成这项任务。 拟议的研究通过以下方式做到这一点： 建模两组关系：1）病例特征和 临床医生对危险性的预测; 2）病例之间 精神病人的特点和暴力。 这些模型需要一个 这是改善对危险性预测的重要一步， 提供了一个丰富的图片，临床医生做什么，应该考虑到， 在评估时，要考虑到是否合理。 这项研究利用了一个机会，使用以前收集的数据 从调查人员收集的两组数据中。 它们有重叠， 丰富的病例特征信息集， 潜在的预测者 第一项研究有临床预测， 在大约400个案例的样本上进行了分析。 第二个两者都有 临床预测和详细的自我报告和官方帐户 约800名患者的暴力行为。 入院预测模型 评估（关于社区和单位暴力）和出院时 将使用各种标准统计方法构建， 分类树方法。 模型将在内部和 外部交叉验证。 该项目将建设一个初步的 一种使用神经网络构建神经性临床评估的方法， 临床医生如何改变这些精算模型的模型。 这一决策援助将是一个有形的和方法健全的一步， 系统化预测危险性。

Clinicians continue to need to make dangerousness determinations for legal and routine clinical purposes. Thus research needs to focus on developing methods for doing this task better. The proposed study does this by modeling two sets of relationships: 1) between case characteristics and clinicians' predictions of dangerousness and 2) between case characteristics and violence in mental patients. These models take an important step toward improving the prediction of dangerousness by providing a rich picture of what clinicians do, and should, take into account when assessing dangerousness. This study capitalizes on an opportunity to use previously collected data from two data sets gathered by the investigators. They have overlapping, rich sets of case characteristic information that will serve as a pool of potential predictors. The first study has clinical predictions of dangerousness on a sample of approximately 400 cases. The second has both clinical predictions and detailed self report and official accounts of patient violence on about 800 patients. Models for prediction at admission assessment (regarding both community and unit violence) and at discharge will be constructed using a variety of standard statistical approaches as well as classifications tree methodology. Models will be internally and externally cross-validated. The project will construct a preliminary method for structuring clinical assessments of dangerousness using the models of what clinicians actually do to alter these actuarial models. This decision aid will be a tangible and methodologically sound step toward systematizing the prediction of dangerousness.