2,242 research outputs found
Exploring and Promoting Diagnostic Transparency and Explainability in Online Symptom Checkers
Online symptom checkers (OSC) are widely used intelligent systems in health contexts such as primary care, remote healthcare, and epidemic control. OSCs use algorithms such as machine learning to facilitate self-diagnosis and triage based on symptoms input by healthcare consumers. However, intelligent systems’ lack of transparency and comprehensibility could lead to unintended consequences such as misleading users, especially in high-stakes areas such as healthcare. In this paper, we attempt to enhance diagnostic transparency by augmenting OSCs with explanations. We first conducted an interview study (N=25) to specify user needs for explanations from users of existing OSCs. Then, we designed a COVID-19 OSC that was enhanced with three types of explanations. Our lab-controlled user study (N=20) found that explanations can significantly improve user experience in multiple aspects. We discuss how explanations are interwoven into conversation flow and present implications for future OSC designs
Towards a Reference Architecture for Female-Sensitive Drug Management
Due to various biological factors, males and females differ in their response to drug treatment. However, there is still a lack of knowledge of the effects resulting from sex-differences in the medical field, especially due to the issue of underrepresentation of females in clinical studies. Considering severe diseases that are related to the cardiovascular system, which are likely to be perilous, counteracting this lack and emphasizing the need for sex-dependent drug treatment is of high importance. Thus, this research-in-progress paper aims at strengthening the female perspective in drug management by proposing design considerations on IS regarding recommender systems in healthcare for reinforcing shared decision-making and person-centered care. The resulting artefact presented will be a reference architecture with a mobile application as the interface to patients and healthcare professionals as well as a data- driven backend to collect and process data on sex specificity in the medical treatment of cardiovascular diseases (CVD)
AI for Explaining Decisions in Multi-Agent Environments
Explanation is necessary for humans to understand and accept decisions made
by an AI system when the system's goal is known. It is even more important when
the AI system makes decisions in multi-agent environments where the human does
not know the systems' goals since they may depend on other agents' preferences.
In such situations, explanations should aim to increase user satisfaction,
taking into account the system's decision, the user's and the other agents'
preferences, the environment settings and properties such as fairness, envy and
privacy. Generating explanations that will increase user satisfaction is very
challenging; to this end, we propose a new research direction: xMASE. We then
review the state of the art and discuss research directions towards efficient
methodologies and algorithms for generating explanations that will increase
users' satisfaction from AI system's decisions in multi-agent environments.Comment: This paper has been submitted to the Blue Sky Track of the AAAI 2020
conference. At the time of submission, it is under review. The tentative
notification date will be November 10, 2019. Current version: Name of first
author had been added in metadat
Machine Learning Framework: Competitive Intelligence and Key Drivers Identification of Market Share Trends Among Healthcare Facilities
The necessity of data driven decisions in healthcare strategy formulation is
rapidly increasing. A reliable framework which helps identify factors impacting
a Healthcare Provider Facility or a Hospital (from here on termed as Facility)
Market Share is of key importance. This pilot study aims at developing a data
driven Machine Learning - Regression framework which aids strategists in
formulating key decisions to improve the Facilitys Market Share which in turn
impacts in improving the quality of healthcare services. The US (United States)
healthcare business is chosen for the study; and the data spanning across 60
key Facilities in Washington State and about 3 years of historical data is
considered. In the current analysis Market Share is termed as the ratio of
facility encounters to the total encounters among the group of potential
competitor facilities. The current study proposes a novel two-pronged approach
of competitor identification and regression approach to evaluate and predict
market share, respectively. Leveraged model agnostic technique, SHAP, to
quantify the relative importance of features impacting the market share. The
proposed method to identify pool of competitors in current analysis, develops
Directed Acyclic Graphs (DAGs), feature level word vectors and evaluates the
key connected components at facility level. This technique is robust since its
data driven which minimizes the bias from empirical techniques. Post
identifying the set of competitors among facilities, developed Regression model
to predict the Market share. For relative quantification of features at a
facility level, incorporated SHAP a model agnostic explainer. This helped to
identify and rank the attributes at each facility which impacts the market
share.Comment: 7 Pages 5 figures 6 tables To appear in ICHA 202
Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AI
While research on explainable AI (XAI) is booming and explanation techniques
have proven promising in many application domains, standardised human-centred
evaluation procedures are still missing. In addition, current evaluation
procedures do not assess XAI methods holistically in the sense that they do not
treat explanations' effects on humans as a complex user experience. To tackle
this challenge, we propose to adapt the User-Centric Evaluation Framework used
in recommender systems: we integrate explanation aspects, summarise explanation
properties, indicate relations between them, and categorise metrics that
measure these properties. With this comprehensive evaluation framework, we hope
to contribute to the human-centred standardisation of XAI evaluation.Comment: This preprint has not undergone any post-submission improvements or
corrections. This work was an accepted contribution at the XAI world
Conference 202
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