Visible and Invisible: Causal Variable Learning and its Application in a Cancer Study

Causal visual discovery is a fundamental yet challenging problem in many research fields. Given visual data and the outcome of interest, the goal is to infer the cause-effect relation. Aside from rich visual ('visible') variables, oftentimes, the outcome is also determined by 'invisible' variables, i.e. the variables from non-visual modalities that do not have visual counterparts. This combination is particularly common in the clinical domain. Built upon the promising invariant causal prediction (ICP) framework, we propose a novel -ICP algorithm to resolve the (visible, invisible) setting. To efficiently discover -plausible causal variables and to estimate the cause-effect relation, the -ICP is learned under a min-min optimisation scheme. Driven by the need for clinical reliability and interpretability, the -ICP is implemented with a typed neural-symbolic functional language. With the built-in program synthesis method, we can synthesize a type-safe program that is comprehensible to the clinical experts. For concept validation of the -ICP, we carefully design a series of synthetic experiments on the type of visual-perception tasks that are encountered in daily life. To further substantiate the proposed method, we demonstrate the application of -ICP on a real-world cancer study dataset, Swiss CRC. This population-based cancer study has spanned over two decades, including 25 fully annotated tissue micro-array (TMA) images with at least resolution and a broad spectrum of clinical meta data for 533 patients. Both the synthetic and clinical experiments demonstrate the advantages of -ICP over the state-of-the-art methods. Finally, we discuss the limitations and challenges to be addressed in the future. Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethic

AI-Assisted Diagnosis of Bone Tuberculosis: A Design Science Research Approach

Bone Tuberculosis (TB) is a significant public health challenge requiring early and precise diagnosis for effective treatment. Traditional methods like radiography and biopsy are invasive and costly. Our study introduces a holistic AI-assisted orthopedic clinical diagnosis system developed through an Action Design Research approach. Unlike previous efforts focused solely on algorithmic design, our system is iteratively validated with real-world clinical data, ensuring both theoretical rigor and practical applicability. By fine-tuning AI algorithms to meet actual clinical needs, we bridge the gap between technological innovation and healthcare relevance. Our research offers innovative insights into the design and evaluation of AI-assisted systems, emphasizing the role of empirical data and diverse evaluation metrics. The study is expected to have broader implications for the adoption of AI in clinical settings, offering a more comprehensive and reliable solution for bone TB diagnosis

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Acute Exercise and Creativity: Embodied Cognition Approaches

This dissertation manuscript is the culmination of three years of research examining several unique, exercise-induced mechanisms underlying creativity. This collection of work addresses historical and current empirical concepts of creativity in a narrative review, providing recommendations for future research. Several reviews follow this introduction, highlighting the proposed effects of exercise on creativity, putative mechanisms for creativity, and the effects of exercise and embodied manipulations on creative behavior. Multiple experiments utilizing moderate-intensity exercise as a theoretical stimulus for higher-order cognitions were conducted to investigate associations between exercise and creativity, which lead to the final dissertation experiment. The dissertation experiment was the first to provide statistically significant evidence for acute, moderate-intensity treadmill exercise coupled with anagram problem-solving to prime subsequent RAT completion compared to a non-exercise, priming only condition. We emphasize that the additive effects of exercise plus priming may be a viable strategy for enhancing verbal convergent creativity. Future research is warranted to explore a variety of priming effects on the relationship between exercise, embodied interventions, and creativityThis dissertation manuscript is the culmination of three years of research examining several unique, exercise-induced mechanisms underlying creativity. This collection of work addresses historical and current empirical concepts of creativity in a narrative review, providing recommendations for future research. Several reviews follow this introduction, highlighting the proposed effects of exercise on creativity, putative mechanisms for creativity, and the effects of exercise and embodied manipulations on creative behavior. Multiple experiments utilizing moderate-intensity exercise as a theoretical stimulus for higher-order cognitions were conducted to investigate associations between exercise and creativity, which lead to the final dissertation experiment. The dissertation experiment was the first to provide statistically significant evidence for acute, moderate-intensity treadmill exercise coupled with anagram problem-solving to prime subsequent RAT completion compared to a non-exercise, priming only condition. We emphasize that the additive effects of exercise plus priming may be a viable strategy for enhancing verbal convergent creativity. Future research is warranted to explore a variety of priming effects on the relationship between exercise, embodied interventions, and creativit

A biopsychosocial formulation of pain communication

We present a detailed framework for understanding the numerous and complicated interactions among psychological and social determinants of pain through examination of the process of pain communication. The focus is on an improved understanding of immediate dyadic transactions during painful events in the context of broader social phenomena. Fine-grain consideration of social transactions during pain leads to an appreciation of sociobehavioral events affecting both suffering persons as well as caregivers. Our examination considers knowledge from a variety of perspectives, including clinical health psychology, social and developmental processes, evolutionary psychology, communication studies, and behavioral neuroscience

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMsThis research work was partially supported by the Sejong University Research Faculty Program (20212023)S