Search Results for author:
Found 15 papers, 1 papers with code
Predicting Depression and Anxiety: A Multi-Layer Perceptron for Analyzing the Mental Health Impact of COVID-19
We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends, particularly anxiety and depression, during the COVID-19 pandemic.
Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination
How can AI be made accountable for its decision-making?
LLM-augmented Preference Learning from Natural Language
Comparing performances with existing methods, we see that pre-trained LLMs are able to outperform the previous SotA models with no fine-tuning involved.
PredictChain: Empowering Collaboration and Data Accessibility for AI in a Decentralized Blockchain-based Marketplace
Limited access to computing resources and training data poses significant challenges for individuals and groups aiming to train and utilize predictive machine learning models.
MentalHealthAI: Utilizing Personal Health Device Data to Optimize Psychiatry Treatment
Mental health disorders remain a significant challenge in modern healthcare, with diagnosis and treatment often relying on subjective patient descriptions and past medical history.
Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes
All of these steps were performed in engagement with medical experts, including a final evaluation of the dashboard results by an expert medical panel.
Leveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case
To enable the adoption of the ever improving AI risk prediction models in practice, we have begun to explore techniques to contextualize such models along three dimensions of interest: the patients' clinical state, AI predictions about their risk of complications, and algorithmic explanations supporting the predictions.
Semantic Modeling for Food Recommendation Explanations
Our motivation with the use of FEO is to empower users to make decisions about their health, fully equipped with an understanding of the AI recommender systems as they relate to user questions, by providing reasoning behind their recommendations in the form of explanations.
Applying Personal Knowledge Graphs to Health
Knowledge graphs that encapsulate personal health information, or personal health knowledge graphs (PHKG), can help enable personalized health care in knowledge-driven systems.
Explanation Ontology in Action: A Clinical Use-Case
We addressed the problem of a lack of semantic representation for user-centric explanations and different explanation types in our Explanation Ontology (https://purl. org/heals/eo).
Explanation Ontology: A Model of Explanations for User-Centered AI
With greater adoption of these systems and emphasis on user-centric explainability, there is a need for a structured representation that treats explainability as a primary consideration, mapping end user needs to specific explanation types and the system's AI capabilities.
Directions for Explainable Knowledge-Enabled Systems
Interest in the field of Explainable Artificial Intelligence has been growing for decades and has accelerated recently.
Foundations of Explainable Knowledge-Enabled Systems
Additionally, borrowing from the strengths of past approaches and identifying gaps needed to make explanations user- and context-focused, we propose new definitions for explanations and explainable knowledge-enabled systems.
Making Study Populations Visible through Knowledge Graphs
To address these challenges, we develop an ontology-enabled prototype system, which exposes the population descriptions in research studies in a declarative manner, with the ultimate goal of allowing medical practitioners to better understand the applicability and generalizability of treatment recommendations.
Knowledge Integration for Disease Characterization: A Breast Cancer Example
With the rapid advancements in cancer research, the information that is useful for characterizing disease, staging tumors, and creating treatment and survivorship plans has been changing at a pace that creates challenges when physicians try to remain current.
