Search Results for author:
Found 21 papers, 2 papers with code
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.
A Theoretically Grounded Benchmark for Evaluating Machine Commonsense
Preliminary results suggest that the benchmark is challenging even for advanced language representation models designed for discriminative CSR question answering tasks.
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.
Geospatial Reasoning with Shapefiles for Supporting Policy Decisions
Using a policy evaluation pipeline that mixes OWL reasoning and GeoSPARQL, our approach implements the relevant geospatial relationships, according to a set of requirements elicited by radio spectrum domain experts.
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.
Commonsense Knowledge Mining from Term Definitions
Commonsense knowledge has proven to be beneficial to a variety of application areas, including question answering and natural language understanding.
Dimensions of Commonsense Knowledge
Recently, the focus has been on large text-based sources, which facilitate easier integration with neural (language) models and application to textual tasks, typically at the expense of the semantics of the sources and their harmonization.
Exploring and Analyzing Machine Commonsense Benchmarks
Commonsense question-answering (QA) tasks, in the form of benchmarks, are constantly being introduced for challenging and comparing commonsense QA systems.
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.
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).
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.
Semantically-aware population health risk analyses
One primary task of population health analysis is the identification of risk factors that, for some subpopulation, have a significant association with some health condition.
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.
Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text
Inspired by recent successes in neural machine translation, we treat the triples within a given knowledge graph as an independent graph language and propose an encoder-decoder framework with an attention mechanism that leverages knowledge graph embeddings.
Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion
In the medical domain, identifying and expanding abbreviations in clinical texts is a vital task for both better human and machine understanding.
Contextual Data Collection for Smart Cities
The sharing of these documents may be a convenient way for the data provider to convey and publish data but it is not the ideal way for data consumers to reuse the data.
From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards
In the context of Smart Cities, indicator definitions have been used to calculate values that enable the comparison among different cities.
Human-Aware Sensor Network Ontology: Semantic Support for Empirical Data Collection
Significant efforts have been made to understand and document knowledge related to scientific measurements.
