Using Verb Fluency, Natural Language Processing, and Machine Learning to Detect Alzheimer's Disease.

Alzheimer's disease (AD) causes significant impairments in memory and other cognitive domains. As there is no cure to the disease yet, early detection and delay of disease progression are critical for management of AD. Verbal fluency is one of the most common and sensitive neuropsychological methods used for detection and evaluation of the cognitive declines in AD, in which a subject is required to name as many items as possible in 30 or 60 seconds that belong to a certain category. In this study, we develop an approach to detect AD using a verb fluency (VF) task, a specific subset of verbal fluency analyzing the subjects' listing of verbs in a given time period. We use machine learning techniques including random forest (RF), neural network (NN), recurrent NN (RNN), and natural language processing (NLP) to detect the risk of AD. The results show that the developed models can stratify subjects into the corresponding AD and control groups with up to 76% accuracy using RF, but at a cost of having to preprocess the data. This accuracy is slightly lower, but not significantly, at 67% using RNN and NLP, which involves almost no manual preprocessing of the data. This study opens up a powerful approach of using simple VF tasks for early detection of AD.

Predicting Severity in People with Aphasia: A Natural Language Processing and Machine Learning Approach.

Speech language pathologists need an accurate assessment of the severity of people with aphasia (PWA) to design and provide the best course of therapy. Currently, severity is evaluated manually by an increasingly scarce pool of experienced and well-trained clinicians, taking considerable time resources. By analyzing the transcripts from three discourse elicitation methods, this study combines natural language processing (NLP) and machine learning (ML) to predict the severity of PWA, both by score and severity level. By engineering language features from PWA tasks, an unstructured k-means clustering presents distinct aphasia types, showing validity of the selected features. We develop regression models to predict severity scores along with a classification of severity by level (Mild, Moderate, Severe, and Very Severe) to assist clinicians to easily plan and monitor the course of treatment. Our best ML regression model uses a deep neural network and results in a mean absolute error (MAE) of 0.0671 and root mean squared error (RMSE) of 0.0922. Our best classification model uses a random forest and result in an overall accuracy of 73%, with the highest accuracy of 87.5% for mild severity. Our results suggest that using NLP and ML provides an accurate and cost-effective approach to evaluate the severity levels in PWA to consequently help clinicians determine rehabilitation procedures.

Improving Deep Reinforcement Learning With Transitional Variational Autoencoders: A Healthcare Application.

Reinforcement learning is a powerful tool for developing personalized treatment regimens from healthcare data. Yet training reinforcement learning agents through direct interactions with patients is often impractical for ethical reasons. One solution is to train reinforcement learning agents using an 'environment model,' which is learned from retrospective patient data, and can simulate realistic patient trajectories. In this study, we propose transitional variational autoencoders (tVAE), a generative neural network architecture that learns a direct mapping between distributions over clinical measurements at adjacent time points. Unlike other models, the tVAE requires few distributional assumptions, and benefits from identical training, and testing architectures. This model produces more realistic patient trajectories than state-of-the-art sequential decision-making models, and generative neural networks, and can be used to learn effective treatment policies.