用机器学习预测青少年在儿童福利中的成功安置情况
Kimberlee J Trudeau1, Jichen Yang1, Jiaming Di1
1Outcome Referrals, Inc., Framingham, MA, USA.
Children and youth services review
|October 16, 2023
概括
机器学习模型可以预测成功的儿童福利安置. 这种临床决策支持系统 (CDSS) 使用行为健康数据来改善户外护理儿童的结果.
科学领域:
- 儿童福利研究儿童福利研究
- 临床决策支持系统临床决策支持系统
- 机器学习在医疗保健中的应用
背景情况:
- 家庭之外的安置决定对儿童的福祉有重大影响.
- 某些安置类型和多次安置与照顾儿童的不良结果相关.
- 临床决策支持系统 (CDSS) 可以利用现有数据来指导安置决策.
研究的目的:
- 评估开发机器学习模型的可行性,以预测最佳儿童安置.
- 根据年轻人的行为健康需求和特征,确定最佳的护理水平.
- 为未来的家庭外照顾儿童的安置决定提供信息.
主要方法:
- 开发机器学习模型,使用来自行为健康组织的标准护理数据.
- 在精神病住院治疗设施 (PRTF) 预测的治疗成功概率与其他安置 (AUROC > 0.70) 相比.
- 在全州系统中,在特定的安置类型中验证了模型 (平均AUROC>0.75).
主要成果:
- 机器学习模型成功地区分了在住宅和非住宅护理中壮成长的年轻人.
- 基于模型的建议显示,经风险调整后的结果有所改善 (推安置的平均值高出80%).
- 经过验证的模型表明,在不同类型的安置中,治疗成功的预测准确度很高.
结论:
- 使用风险调整的行为健康和功能数据的机器学习模型对预测积极的安置结果显示出希望.
- 这些模型可以为儿童福利系统中的年轻人提供信息并改善未来的安置决策.
- 对实施此类系统的道德考虑进行进一步研究是有必要的.
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