College of Engineering, Northeastern University, Boston, Massachusetts.
Bouvé College of Health Sciences, Northeastern University, Boston, Massachusetts.
JAMA Netw Open. 2023 Dec 1;6(12):e2348898. doi: 10.1001/jamanetworkopen.2023.48898.
Aggressive behavior is a prevalent and challenging issue in individuals with autism.
To investigate whether changes in peripheral physiology recorded by a wearable biosensor and machine learning can be used to predict imminent aggressive behavior before it occurs in inpatient youths with autism.
DESIGN, SETTING, AND PARTICIPANTS: This noninterventional prognostic study used data collected from March 2019 to March 2020 from 4 primary care psychiatric inpatient hospitals. Enrolled participants were 86 psychiatric inpatients with confirmed diagnoses of autism exhibiting operationally defined self-injurious behavior, emotion dysregulation, or aggression toward others; 16 individuals were not included (18.6%) because they would not wear the biosensor (8 individuals) or were discharged before an observation could be made (8 individuals). Data were analyzed from March 2020 through October 2023.
Research staff performed live behavioral coding of aggressive behavior while inpatient study participants wore a commercially available biosensor that recorded peripheral physiological signals (cardiovascular activity, electrodermal activity, and motion). Logistic regression, support vector machines, neural networks, and domain adaptation were used to analyze time-series features extracted from biosensor data. Area under the receiver operating characteristic curve (AUROC) values were used to evaluate the performance of population- and person-dependent models.
There were 70 study participants (mean [range; SD] age, 11.9 [5-19; 3.5] years; 62 males [88.6%]; 1 Asian [1.4%], 5 Black [7.1%], 1 Native Hawaiian or Other Pacific Islander [1.4%], and 63 White [90.0%]; 5 Hispanic [7.5%] and 62 non-Hispanic [92.5%] among 67 individuals with ethnicity data). Nearly half of the population (32 individuals [45.7%]) was minimally verbal, and 30 individuals (42.8%) had an intellectual disability. Participant length of inpatient hospital stay ranged from 8 to 201 days, and the mean (SD) length was 37.28 (33.95) days. A total of 429 naturalistic observational coding sessions were recorded, totaling 497 hours, wherein 6665 aggressive behaviors were documented, including self-injury (3983 behaviors [59.8%]), emotion dysregulation (2063 behaviors [31.0%]), and aggression toward others (619 behaviors [9.3%]). Logistic regression was the best-performing overall classifier across all experiments; for example, it predicted aggressive behavior 3 minutes before onset with a mean AUROC of 0.80 (95% CI, 0.79-0.81).
This study replicated and extended previous findings suggesting that machine learning analyses of preceding changes in peripheral physiology may be used to predict imminent aggressive behaviors before they occur in inpatient youths with autism. Further research will explore clinical implications and the potential for personalized interventions.
攻击行为是自闭症个体中普遍存在且具有挑战性的问题。
研究通过可穿戴式生物传感器和机器学习记录的外周生理变化是否可以用于预测自闭症住院青少年即将发生的攻击行为，而无需等待行为发生。
设计、地点和参与者：这是一项非干预性预后研究，使用了 2019 年 3 月至 2020 年 3 月从四家初级保健精神病住院医院收集的数据。纳入的参与者为 86 名经确诊患有自闭症、表现出自伤、情绪失调或对他人攻击行为的精神病住院患者；16 名参与者（18.6%）因不愿佩戴生物传感器（8 名）或在观察开始前出院（8 名）而未被纳入研究。数据分析于 2020 年 3 月至 2023 年 10 月进行。
研究人员在住院研究参与者佩戴商业上可获得的生物传感器时对攻击行为进行实时行为编码，该传感器记录外周生理信号（心血管活动、皮肤电活动和运动）。逻辑回归、支持向量机、神经网络和领域自适应用于分析从生物传感器数据中提取的时间序列特征。接受者操作特征曲线下的面积（AUROC）值用于评估人群和个体依赖模型的性能。
共有 70 名研究参与者（平均[范围；标准差]年龄为 11.9[5-19；3.5]岁；62 名男性[88.6%]；1 名亚洲人[1.4%]，5 名黑人[7.1%]，1 名夏威夷原住民或其他太平洋岛民[1.4%]和 63 名白人[90.0%]；5 名西班牙裔[7.5%]和 62 名非西班牙裔[92.5%]，67 名参与者中有 6 名提供了种族数据）。近一半的人群（32 人[45.7%]）几乎无法言语，30 人（42.8%）存在智力障碍。参与者的住院时间从 8 天到 201 天不等，平均（标准差）住院时间为 37.28（33.95）天。共记录了 429 次自然观察编码会话，总计 497 小时，记录了 6665 次攻击行为，包括自伤（3983 次行为[59.8%]）、情绪失调（2063 次行为[31.0%]）和对他人的攻击（619 次行为[9.3%]）。逻辑回归是所有实验中表现最佳的整体分类器；例如，它可以在攻击行为发生前 3 分钟进行预测，平均 AUROC 为 0.80（95%CI，0.79-0.81）。
本研究复制并扩展了先前的研究结果，表明通过机器学习分析外周生理变化的先前变化可能用于预测自闭症住院青少年即将发生的攻击行为，而无需等待行为发生。进一步的研究将探索临床意义和个性化干预的潜力。
