Abstract
Machine learning (ML) models have demonstrated the power of utilizing clinical instruments to provide tools for domain experts in gaining additional insights toward complex clinical diagnoses. In this context these tools desire two additional properties: interpretability, being able to audit and understand the decision function, and robustness, being able to assign the correct label in spite of missing or noisy inputs. This work formulates diagnostic classification as a decision-making process and utilizes Q-learning to build classifiers that meet the aforementioned desired criteria. As an exemplary task, we simulate the process of differentiating Autism Spectrum Disorder from Attention Deficit-Hyperactivity Disorder in verbal school aged children. This application highlights how reinforcement learning frameworks can be utilized to train more robust classifiers by jointly learning to maximize diagnostic accuracy while minimizing the amount of information required.
Highlights
Our experiments focus on the use of Autism Diagnostic Interview-Revised (ADI-R) items to predict the correct Autism Spectrum Disorder (ASD)/Attention Deficit-Hyperactivity Disorder (ADHD) clinical estimate
It is key to interpret the output of the model, but the path to that decision[24]. For this reason prior work on autism diagnosis has explored largely the use of tree and forest based models. With these characteristics in mind, we outline a benchmark consisting of Decision Tree (DT) and Random Forest (RF) models that are trained on Stratified 10-fold Cross Validation (CV) to better approximate our performance on evaluation and expected generalization of the learned models
The DTrobust and RFrobust degrade much more steeply than the policy π suggesting that the underlying states and structures learned by the policy are more robust than the representations learned by the Machine learning (ML) models
Summary
In an effort to more consistently assess children for ASD symptoms, a number of different instruments consisting of a variety of clinically-relevant items have been developed that enable diagnostic algorithms While these instruments and associated algorithms are not used to diagnose, they are tools that the clinician can rely on in determining a child’s risk and need for further diagnostic observation. Many different instruments measure similar behaviors, but differ in who the observer is and how the observations are made, varying between parent/caregiver reported to direct clinician observation, leading to varying reliability and accuracy for certain observations One of those widely used instruments is the Autism Diagnostic Interview-Revised (ADI-R), which is a clinician led diagnostic interview, during which the child’s caregivers are asked a series of questions associated with evaluating the child’s communication abilities and social behaviors[18].
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