ABA Forecast

The Problem

In Applied Behavior Analysis, therapy is extremely reactive. Most strategies focus on de-escalating children with autism when dysregulated, challenging for both therapist and client. However, behavior escalation usually stems from factors that can be easily measured and tracked. Identifying and analyzing these factors can help predict escalations before they occur, allowing therapists to manipulate a child's environment accordingly and be better prepared. ABA Forecast uses a trained Random Forest Classifier to recognize each child's behavior patterns based on real-world factors such as sleep, weather, day of the week, and toileting to predict behavior risk.

The Solution

ABA Forecast combines machine learning with real-time environmental data to deliver actionable behavioral predictions. The system features a Next.js frontend where therapists input daily observations (sleep quality, meals, bathroom visits, transitions, social context) and receive immediate risk assessments. A Flask backend processes this data through a Random Forest Classifier trained on synthetic behavioral patterns, achieving high accuracy in predicting escalation risk.

Key features include automatic weather integration via OpenWeatherMap API, which factors current conditions into predictions, and a comprehensive dashboard displaying risk levels with confidence scores. The system calculates critical variables like time since last meal/void and recent toileting accidents, transforming raw data into meaningful behavioral indicators. Most importantly, therapists receive Board Certified Behavior Analyst-level recommendations for session planning, including specific antecedent interventions and monitoring priorities.

Why It Matters

ABA Forecast provides immense value to the field of ABA, specifically therapists who have to manage the behaviors directly. It allows therapists to better anticipate and understand behavior before it reaches a point of escalation, which can be stressful for both the child and the therapist. The goal is to help shift ABA from reactive care to proactive care.

How Claude Is Used

Claude serves as the clinical reasoning engine, transforming raw prediction data into actionable ABA strategies. After the Random Forest model generates risk predictions, Claude receives a comprehensive prompt containing the prediction results, confidence scores, and all relevant antecedent factors (motivating operations, environmental context, toileting status).

Claude then performs a detailed functional behavior analysis, identifying likely antecedent patterns and probable behavioral functions (escape, attention, tangible, automatic). The AI generates session-ready recommendations following ABA best practices: antecedent interventions, proactive reinforcement schedules, and replacement behavior training. Each recommendation is written in concrete "do this" language (e.g., "Before starting work, provide a 2-step visual 'first/then' with a preferred item") that therapists can immediately implement.

Claude also identifies key risk factors from the data, protective factors the team can leverage, and monitoring priorities for documenting early warning signs. Additionally, the system includes an interactive chatbot feature where therapists can ask follow-up questions, request clarification on specific strategies, or explore behavioral scenarios in greater depth. This transforms numerical predictions into practical clinical guidance that respects the complexity of ABA methodology while remaining accessible to RBTs and therapists working directly with clients.