Introduction

The CANS is the most popular measurement tool in the System of Care (SoC), with the potential to generate an estimated 1.9 million evaluations per year in the United States. This dataset has broad potential for decision support and outcomes monitoring, yet many SoC services do not yet leverage this information asset. We report here the results of a pilot project in which we applied machine learning methods to CANS data for the purpose of identifying clinical profiles associated with improvement in a public community-based behavioral health program in Pennsylvania.

Methods

We analyzed over 7,000 CANS from 3,385 children who participated in Pennsylvania’s Medicaid-funded behavioral health rehabilitation services (BHRS) program during 2013–2019. A gradient boosting classifier was developed to identify children most likely to experience a total score improvement on the CANS while participating in BHRS. Separate models were constructed for children with and without autism spectrum disorder (ASD). CANS-based clinical profiles associated with improvement were also identified. Analyses were run using Python Sci-Ki version 0.20.3 and Linearly Interpretable Model-agnostic Explanations (LIME) version 0.1.1.33.

Results

The mean age of BHRS participants was 9.85 years (standard deviation: 3.47) and the majority were white (54%) or African American (11.5%). The median length of stay was 963 days (range: 541–2,008) and 39% (N = 1,330) had a diagnosis of ASD. A total of 49.9% of children had a CANS total score improvement. Precision of the gradient boosting classifier was 70% and 79% for children with and without ASD, respectively. Fourteen profiles were associated with improvement in ASD (mean probability of improvement per profile = 0.95, range: 0.88–1.0) and 55 such profiles were identified in children without ASD (mean probability of improvement: 0.91, range: 0.86–1.0).

Conclusion

Machine learning can be applied to the CANS to identify children who have high probability of improvement in BHRS. These methods may have utility as an adjunct to existing decision support systems for SoC services.

中文翻译：

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一种使用儿童和青少年的需求和优势（CANS）来确定由Medicaid资助，基于社区的行为健康计划中成功预测因素的机器学习方法

介绍

CANS是护理系统（SoC）中最流行的测量工具，在美国每年有望产生190万次评估。该数据集具有用于决策支持和结果监视的广泛潜力，但是许多SoC服务尚未利用此信息资产。我们在这里报告了一个试点项目的结果，在该项目中，我们将机器学习方法应用于CANS数据，目的是识别与宾夕法尼亚州基于公共社区的行为健康计划得到改善相关的临床资料。

方法

我们分析了2013-2019年期间参加宾夕法尼亚州医疗补助资助的行为健康康复服务（BHRS）计划的3,385名儿童中的7,000多个CANS。开发了梯度增强分类器，以识别最有可能在参加BHRS时在CANS上获得总成绩改善的孩子。为患有和没有自闭症谱系障碍（ASD）的儿童建立了单独的模型。还确定了与改善相关的基于CANS的临床资料。使用Python Sci-Ki 0.20.3版和线性可解释模型不可知解释（LIME）0.1.1.33版进行分析。

结果

BHRS参与者的平均年龄为9.85岁（标准差：3.47），大多数为白人（54％）或非裔美国人（11.5％）。中位住院时间为963天（范围：541–2,008），其中39％（N = 1,330）被诊断为ASD。总计49.9％的儿童的CANS总成绩有所改善。有和没有ASD的儿童，梯度增强分类器的精确度分别为70％和79％。十四个特征与ASD改善相关（每个特征改善的平均概率= 0.95，范围：0.88-1.0），在没有ASD的儿童中鉴定出55个这样的特征（平均改善概率：0.91，范围：0.86-1.0）。

结论

可以将机器学习应用于CANS，以识别出BHRS改善可能性较高的儿童。这些方法可以作为现有的SoC服务决策支持系统的辅助工具。