Multimodal AI in Healthcare
A Paradigm Shift in Health Intelligence
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Artificial Intelligence for Personalized Care, Wellness, and Longevity Research
Artificial intelligence (AI) has the potential to transform personalized medicine by enabling healthcare professionals to deliver more precise, targeted treatments that are tailored to the individual needs of ...
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Chapter
Multimodal Artificial Intelligence: Next Wave of Innovation in Healthcare and Medicine
Multimodality refers to the utilization of different data types with different representational modes. Medical and health data are becoming more and more multimodal. Emerging multimodal technologies enable use...
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Book
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Book
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Chapter
Digital Technologies for Clinical, Public and Global Health Surveillance
Digital intelligent technologies are widely used to support the monitoring, detection, and prevention of diseases among individuals or communities. Artificial Intelligence offers a wide range of tools, methodo...
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Chapter and Conference Paper
Towards an AI Planning-Based Pipeline for the Management of Multimorbid Patients
Treatment of patients with multimorbidity is one of the greatest challenges for clinical decision support. While evidence-based management of specific diseases is supported by clinical practice guidelines, con...
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Chapter
Measuring Physiological Markers of Stress During Conversational Agent Interactions
Conversational agent (CA) technology is rapidly becoming ubiquitous. Understanding how CAs impact users on multiple levels, including physiology, thus becomes increasingly important. In this study, we examined...
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Book and Conference Proceedings
Artificial Intelligence in Medicine
20th International Conference on Artificial Intelligence in Medicine, AIME 2022, Halifax, NS, Canada, June 14–17, 2022, Proceedings
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Chapter
An Approach to Combining Adherence-to-Therapy and Patient Preference Models for Evaluation of Therapies in Patient-Centered Care
Adherence to therapy is one of the major determinants of therapy success, while non-adherence leads to worsening of patient condition and increased healthcare costs. The aim of our work is to evaluate therapie...
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Chapter
Explainability and Interpretability: Keys to Deep Medicine
Deep medicine, which aims to push the boundaries of artificial intelligence to reshape the health and medical intelligence and decision making, is a promising concept that is gaining attention over traditional...
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Chapter and Conference Paper
MitPlan 2.0: Enhanced Support for Multi-morbid Patient Management Using Planning
The complexity of patient care is growing due to an ageing population. As chronic illnesses become more common, the incidence of multi-morbidity increases. Generating disease management plans for multi-morbid...
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Chapter
From Precision Medicine to Precision Health: A Full Angle from Diagnosis to Treatment and Prevention
Health Intelligence, a term that encompasses a broad range of techniques and methods from artificial intelligence and data science that provide better insights and improved decision making about individuals’ h...
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Chapter and Conference Paper
MitPlan: A Planning Approach to Mitigating Concurrently Applied Clinical Practice Guidelines
As the overall population ages, patient complexity and the scope of their care is increasing. Over 60% of the population over 65 years of age suffers from multi-morbidity, which is associated with over two tim...
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Article
Learning Neuroscience with Technology: a Scaffolded, Active Learning Approach
Mobile applications (apps) for learning technical scientific content are becoming increasingly popular in educational settings. Neuroscience is often considered complex and challenging for most students to und...
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Article
Ideating Mobile Health Behavioral Support for Compliance to Therapy for Patients with Chronic Disease: A Case Study of Atrial Fibrillation Management
Poor patient compliance to therapy results in a worsening condition that often increases healthcare costs. In the MobiGuide project, we developed an evidence-based clinical decision-support system that deliver...
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Article
Open AccessHealth intelligence: how artificial intelligence transforms population and personalized health
Advances in computational and data sciences for data management, integration, mining, classification, filtering, visualization along with engineering innovations in medical devices have prompted demands for mo...
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Chapter and Conference Paper
Using Constraint Logic Programming for the Verification of Customized Decision Models for Clinical Guidelines
Computer-interpretable implementations of clinical guidelines (CIGs) add knowledge that is outside the scope of the original guideline. This knowledge can customize CIGs to patients’ psycho-social context or a...
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Chapter and Conference Paper
Using First-Order Logic to Represent Clinical Practice Guidelines and to Mitigate Adverse Interactions
Clinical practice guidelines (CPGs) were originally designed to help with evidence-based management of a single disease and such single disease focus has impacted research on CPG computerization. This computer...
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Chapter and Conference Paper
Using Constraint Logic Programming to Implement Iterative Actions and Numerical Measures during Mitigation of Concurrently Applied Clinical Practice Guidelines
There is a pressing need in clinical practice to mitigate (identify and address) adverse interactions that occur when a comorbid patient is managed according to multiple concurrently applied disease-specific c...
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Chapter and Conference Paper
A Constraint Logic Programming Approach to Identifying Inconsistencies in Clinical Practice Guidelines for Patients with Comorbidity
This paper describes a novel methodological approach to identifying inconsistencies when concurrently using multiple clinical practice guidelines. We discuss how to construct a formal guideline model using Con...
