Search Results for author:
Found 24 papers, 7 papers with code
Improving Classification of Infrequent Cognitive Distortions: Domain-Specific Model vs. Data Augmentation
Performance improvements with an augmented model, MentalBERT, exceed those obtained with data augmentation.
Comparing emotion feature extraction approaches for predicting depression and anxiety
The increasing adoption of message-based behavioral therapy enables new approaches to assessing mental health using linguistic analysis of patient-generated text.
Identifying Distorted Thinking in Patient-Therapist Text Message Exchanges by Leveraging Dynamic Multi-Turn Context
In this work, we automatically identify distorted thinking in text-based patient-therapist exchanges, investigating the role of conversation history (context) in distortion prediction.
Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?
\textbf{Results}: Imperfect ASR-generated transcripts surprisingly outperformed manual transcription for distinguishing between individuals with AD and those without in the ``Cookie Theft'' task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Bridging the Skills Gap: Evaluating an AI-Assisted Provider Platform to Support Care Providers with Empathetic Delivery of Protocolized Therapy
We studied providers with and without expertise in mental health treatment delivering a therapy session using the platform with (intervention) and without (control) AI-assistance features.
Enhancing Robustness of Foundation Model Representations under Provenance-related Distribution Shifts
We focus on confounding by provenance, a form of distribution shift that emerges in the context of multi-institutional datasets when there are differences in source-specific language use and class distributions.
Personalized Jargon Identification for Enhanced Interdisciplinary Communication
We collect a dataset of over 10K term familiarity annotations from 11 computer science researchers for terms drawn from 100 paper abstracts.
Deep Representations of First-person Pronouns for Prediction of Depression Symptom Severity
These findings were generated by counting frequencies of first-person singular pronouns in text data.
Backdoor Adjustment of Confounding by Provenance for Robust Text Classification of Multi-institutional Clinical Notes
Machine learning and deep learning approaches have been used to improve the performance of clinical NLP.
APPLS: Evaluating Evaluation Metrics for Plain Language Summarization
In response, we introduce POMME, a new metric designed to assess text simplification in PLS; the metric is calculated as the normalized perplexity difference between an in-domain and out-of-domain language model.
TRESTLE: Toolkit for Reproducible Execution of Speech, Text and Language Experiments
The evidence is growing that machine and deep learning methods can learn the subtle differences between the language produced by people with various forms of cognitive impairment such as dementia and cognitively healthy individuals.
The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia Classification
Linguistic anomalies detectable in spontaneous speech have shown promise for various clinical applications including screening for dementia and other forms of cognitive impairment.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Retrieval augmentation of large language models for lay language generation
Recent lay language generation systems have used Transformer models trained on a parallel corpus to increase health information accessibility.
GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models
Deep learning (DL) techniques involving fine-tuning large numbers of model parameters have delivered impressive performance on the task of discriminating between language produced by cognitively healthy individuals, and those with Alzheimer's disease (AD).
Should Semantic Vector Composition be Explicit? Can it be Linear?
The second considers whether semantic vector composition should be explicitly described mathematically, or whether it can be a model-internal side-effect of training a neural network.
Quantum Mathematics in Artificial Intelligence
In the decade since 2010, successes in artificial intelligence have been at the forefront of computer science and technology, and vector space models have solidified a position at the forefront of artificial intelligence.
Automated Lay Language Summarization of Biomedical Scientific Reviews
Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes.
Retrofitting Vector Representations of Adverse Event Reporting Data to Structured Knowledge to Improve Pharmacovigilance Signal Detection
Adverse drug events (ADE) are prevalent and costly.
Improving Biomedical Analogical Retrieval with Embedding of Structural Dependencies
Inferring the nature of the relationships between biomedical entities from text is an important problem due to the difficulty of maintaining human-curated knowledge bases in rapidly evolving fields.
A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the Alzheimer's Type
In recent years there has been a burgeoning interest in the use of computational methods to distinguish between elicited speech samples produced by patients with dementia, and those from healthy controls.
Bringing Order to Neural Word Embeddings with Embeddings Augmented by Random Permutations (EARP)
Word order is clearly a vital part of human language, but it has been used comparatively lightly in distributional vector models.
Retrofitting Concept Vector Representations of Medical Concepts to Improve Estimates of Semantic Similarity and Relatedness
In this paper, we present a method that retrofits distributional context vector representations of biomedical concepts using structural information from the UMLS Metathesaurus, such that the similarity between vector representations of linked concepts is augmented.
