Personalized- ABA : Personalized Treatment Plan Generation for Applied Behavior Analysis using Natural Language Processing (2024)

Abstract: Autism Spectrum Disorder (ASD) is a neurological and developmental disability that affects how an individual learns, communicates, interacts with others. Applied Behavior Analysis (ABA) is a gold standard therapy for children and adults suffering from ASD to improve their learning, social, and communication skills. Today, 1 in 36 children are diagnosed with ASD with expectations that this rate will only continue to rise. The supply of certified ABA providers is alarmingly insufficient to meet the needs of children with ASD. In fact, waitlists to receive ABA therapy in the United States exceed 10 months in most states. Clinicians or Board Certified Behavior Analysts (BCBAs) are now experiencing intense bottlenecks around diagnostic evaluations and developing treatment plans quickly enough to support timely access to care. Over the past few years, Artificial Intelligence has changed the way industries operate by offering powerful ways to process, analyze, generate, and predict data. In this paper, we have addressed the problem of both time and supply restrictions faced by ABA providers by proposing a novel method for personalized treatment plan generation and program prediction by leveraging the capabilities of Deep Learning and Large Language Models (LLM). Additionally, we have introduced two separate models for behavior program prediction (F1-Score: 0.671) and skill acquisition program predictions (Rouge-1 Score: 0.476) which will help ABA providers in treatment plan implementation. Results are promising: an AI-generated treatment plan demonstrates a high similarity (Average Similarity Score: 0.915) to the original treatment plan written by a BCBA. Finally, as we partnered with a multi-state ABA provider in building this product, we ran a single-blind study that concluded that BCBAs prefer an AI-generated treatment plan 65 percent of the time compared to a BCBA-generated one.

## introduction over the past years, there has been a significant rise in the cases of autism spectrum disorder (asd). according to data collected by the autism and developmental disabilities monitoring (addm) @xcite , 1 in 36 children in united states have autism. increased awareness and screening, broadened diagnostic criteria, and better infrastructure for autism research have played a vital role in the rise of asd prevalence. applied behavior analysis (aba) is regarded as a gold-standard therapy and is one of the most widely accepted therapies. today, it is 100 percent covered by insurance and children are diagnosed as earlier as 2 years of age. effective aba treatment relies on early diagnosis and effective treatment planning to individualize the behavior reduction goals and skill acquisition needs of every child. the type of aba treatment plan is conventionally determined by a trained board certified behavior analyst (bcba) via integrated assessment like vb-mapp @xcite , ablls(partington and analysts, 2010), vineland, etc. and information derived from detailed patient intake forms, diagnostic reports, and the functional analysis of the patient. aba treatment plans are comprehensive and tailored to meet the specific needs of individuals with autism spectrum disorder (asd). these plans encompass various components that work together to improve behaviors and enhance overall functioning. however, with a rise in asd cases and increased demand for aba services, there has been a shortage of aba providers or board certified behavior analysts (bcbas) @xcite . as the demand for aba therapy escalates, maintaining a balance between workload and service quality therapy becomes increasingly complex for bcbas. due to this, individuals diagnosed with asd face challenges in accessing the necessary therapy and the required support. the scarcity of quality aba therapy affects the progress of the individual in managing asd, impacting mental health. to address this problem, we have introduced a novel approach for creating individualized treatment plans tailored to the specific needs of any client using the advancements in the field of deep learning. we have also proposed two separate transformer-based(vaswani et al., 2023) models to predict "behavior reduction" and "skill acquisition" programs, considering client's assessments (like vb-mapp, ablls, etc.), diagnostic reports, and parent interviews. we have further shown our proposed models outperforms other state-of-the-art models in prediction tasks. the first-draft generated via our model will save a significant amount of time, as we are able to effectively analyze thousands of clinical documents to create and individualize a treatment plan. through the experiments conducted for measuring the similarity of the aigenerated treatment plan versus the original one, the potential of the proposed method is revealed. the main contribution of the paper can be summarized as follows: recent advancements in the domain of deep learning has significantly impacted the field of autism spectrum disorder (asd) by offering new means for diagnosis and treatment. @xcite proposes a method for asd detection using eye movement of an individual. deep learning, particularly involving neural networks have shown promising results in the field of asd. for instance, @xcite uses convolutional neural network (cnn) for classification of asd on functional mri data. similarly, there has also been use of recurrent neural networks (rnn) @xcite and long short-term memory (lstm) for analyzing time-series data for diagnosis of asd. for instance, @xcite uses lstm to diagnose children with asd based on raw video data. deep learning techniques are also used for dimensionality reduction and feature extraction which helps in asd research. @xcite uses variational auto-encoder (vae) for representation learning, enhancing the interpretability of complex datasets. personalization of treatment @xcite for an individual with asd has also benefited from the advancements in deep learning while there has also been work on ensemble learning for classification of asd @xcite . as the field of deep learning continue to evolve, it can play a crucial role in asd research. ## methodology this section presents the proposed method for an end-to-end treatment plan generation personalized for each individual (as depicted in figure 1 ) along with the details about the prediction of behavior and skill acquisition programs. it takes the raw assessment file (generally in excel format for ablls, vb-mapp, etc), diagnostic reports, and the parent interview documents of the client as inputs. our proposed method has been divided into different sections for pre-processing and the analysis of the input, prediction, and generation of goals tailored to meet the specific requirements of the client. ## conclusion in this work, we propose a novel method for full treatment plan generation specific to the needs of a particular individual. our proposed method outperforms other state-of-the-art model in similar domain in program prediction tasks. we further validate the effectiveness of our approach by calculating the similarity of the generated treatment plan with the original treatment plan. the treatment plan generated using our approach possess very high similarity with the original treatment plan. our proposed method will not only help in automating the treatment plan generation process but will also reduce the time taken by a bcba in formulating the treatment plan. on average, a bcba spends anywhere between 10-15 hours creating a single treatment plan from start to finish. we believe that our ai-generated treatment plan will enable them to do so in 1 hour or less. the applicability of our proposed method on other neurodevelopmental disorders outside of autism are open avenues for future work.