Association for Behavior Analysis International

Machine learning holds great promise for improving behavior analytic practice and research (Turgeon & Lanovaz, 2020). Historically, behavior analysts have collected and analyzed data as a means of making decisions to improve socially significant client outcomes. When analyzing large data sets reflecting organizations-wide outcomes or the complex outputs that can be captured by sensors, there is a possibility for enhancing the decision-making of behavior analysts. Response effort can limit the extent to which humans can complete analyses or make predictions in time to be beneficial. By nature, machine learning can allow for rapid or even real-time analyses that would be impossible for a human. The first presentation will describe how behavior analysts at an educational center are using sensors to collect physiological and behavioral data and applying machine learning to analyze data and inform decision-making. The second presentation will share data from a multimodal model of sensor data collection and machine learning that allows for real-time prediction of behavioral escalation within a modified Practical Functional Assessment. Finally, presenters will describe a machine learning model for analyzing service delivery and satisfaction data across many organizations over the course of multiple years that allows for improvement in organizational decision-making models.

The target audience for this session includes practicing behavior analysts who oversee behavior change programs that address severe problem behavior, or who oversee ABA agencies and are responsible for making organization-level decisions. This session is also intended to be of interest to those interested in the practical or ethical context surrounding the use of machine learning and or sensors.

Behavior analysts understand behavior through observation of learners in the environment. Technological advances offer a view of what is happening inside of the learner as well. The presenter will describe a program at The Center for Discovery (TCFD) where students in a specially equipped classroom wear sensors that track physiological and movement data. Video data from this naturalistic setting are aligned with sensor data. The presenter will explain how behavior analysts from TCFD integrate data from the sensors with traditional ABA methodology to design better treatments for learners with autism spectrum disorder and maladaptive behaviors. Physiological and behavioral data representing significant clinical findings will be shared. In addition to using technology to inform clinical decisions, the presenter will describe collaborations between TCFD and computer scientists. These scientists are using machine learning and biomedical signal analysis to analyze TCFD’s rich dataset with the desired outcome of automatic detection and prediction of behaviors. The ultimate goal of this relationship is to develop technology that a.) sends alerts to caregivers that a behavior is imminent or that it is time to re-engage following a behavior and b.) detects behaviors automatically as a potential replacement for live data collection.

The delivery of ABA services involves a complex interaction of behavioral systems. Patients need to be interested in and seek out ABA services; and, once in ABA, to continue improving their quality of life. Employees need to be hired in sufficient numbers, properly trained, adequately resourced, and appropriately matched with patients they are competent to serve. And payors need to see progress being made within reasonable costs and time frames. In this presentation, we discuss how the Behavioral Health Center of Excellence is leveraging machine learning to describe and understand these complex and interacting behavioral systems. Specifically, we leveraged data collected from 500+ organizations over six years to analyze the interaction between ABA organizations’ systems and processes; staff satisfaction and turnover; service delivery (e.g., utilization rates, hours of ABA contacted); and patient satisfaction with ABA services. We also discuss how these data can predict patient reported progress, improvement in quality of life, and changes on norm-referenced and criterion-referenced assessments. This presentation provides a first look at the variables that might be important to describe and improve the complex interaction of behavioral systems that comprise ABA service delivery.