Using machine learning to identify features associated with different types of self-injurious behaviors in autistic youth.

IF 5.9 2区 医学 Q1 PSYCHIATRY
Ligia Antezana, Caitlin M Conner, Safaa Eldeeb, Samuel Turecki, Matthew Siegel, Helmet T Karim, Carla A Mazefsky
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Abstract

Background: Self-injurious behaviors (SIB) are common in autistic people. SIB is mainly studied as a broad category, rather than by specific SIB types. We aimed to determine associations of distinct SIB types with common psychiatric, emotional, medical, and socio-demographic factors.

Methods: Participants included 323 autistic youth (~50% non-/minimally-speaking) with high-confidence autism diagnoses ages 4-21 years. Data were collected by the Autism Inpatient Collection during admission to a specialized psychiatric inpatient unit (www.sfari.org/resource/autism-inpatient-collection/). Caregivers completed questionnaires about their child, including SIB type and severity. The youth completed assessments with clinicians. Elastic net regressions identified associations between SIB types and factors.

Results: No single factor relates to all SIB types. SIB types have unique sets of associations. Consistent with previous work, more repetitive motor movements and lower adaptive skills are associated with most types of SIB; female sex is associated with hair/skin pulling and self-rubbing/scratching. More attention-deficit/hyperactivity disorder symptoms are associated with self-rubbing/scratching, skin picking, hair/skin pulling, and inserts finger/object. Inserts finger/object has the most medical condition associations. Self-hitting against surface/object has the most emotion dysregulation associations.

Conclusions: Specific SIB types have unique sets of associations. Future work can develop clinical likelihood scores for specific SIB types in inpatient settings, which can be tested with large community samples. Current approaches for SIB focus on the behavior functions, but there is an opportunity to further develop interventions by considering the specific SIB type in assessment and treatment. Identifying factors associated with specific SIB types may aid with screening, prevention, and treatment of these often-impairing behaviors.

使用机器学习来识别与自闭症青少年不同类型的自残行为相关的特征。
背景:自伤行为(SIB)在自闭症患者中很常见。SIB主要作为一个广泛的类别来研究,而不是具体的SIB类型。我们的目的是确定不同的SIB类型与常见的精神、情感、医学和社会人口因素的关联。方法:参与者包括323名自闭症青少年(约50%为非/最小语言),年龄在4-21岁,诊断为高自信自闭症。数据是由自闭症住院患者收集在入院期间的一个专门的精神科住院单位(www.sfari.org/resource/autism-inpatient-collection/)。照顾者完成了关于他们孩子的调查问卷,包括SIB类型和严重程度。这些年轻人完成了临床医生的评估。弹性净回归确定了SIB类型和因素之间的关联。结果:没有单一因素与所有SIB类型相关。SIB类型具有唯一的关联集。与之前的研究一致,重复性运动较多和适应性技能较低与大多数类型的SIB有关;女性性爱与拉扯头发/皮肤和自我摩擦/抓挠有关。更多的注意缺陷/多动障碍症状与自我摩擦/抓挠、抠皮、拉头发/皮肤和插入手指/物体有关。插入手指/物体具有最多的医疗状况关联。对表面/物体的自我打击与情绪失调最相关。结论:特定的SIB类型具有独特的关联集。未来的工作可以为住院患者的特定SIB类型开发临床可能性评分,这可以用大型社区样本进行测试。目前的SIB方法侧重于行为功能,但有机会通过在评估和治疗中考虑特定的SIB类型来进一步开发干预措施。识别与特定SIB类型相关的因素可能有助于筛查、预防和治疗这些通常有害的行为。
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来源期刊
Psychological Medicine
Psychological Medicine 医学-精神病学
CiteScore
11.30
自引率
4.30%
发文量
711
审稿时长
3-6 weeks
期刊介绍: Now in its fifth decade of publication, Psychological Medicine is a leading international journal in the fields of psychiatry, related aspects of psychology and basic sciences. From 2014, there are 16 issues a year, each featuring original articles reporting key research being undertaken worldwide, together with shorter editorials by distinguished scholars and an important book review section. The journal''s success is clearly demonstrated by a consistently high impact factor.
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