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2nd AIxIA Workshop on Artificial Intelligence for Healthcare, HC@AIxIA 2023

作者： Bernasconi, Alice Zanga, Alessio Lucas, Peter J.F. Scutari, Marco Stella, Fabio University of Milano - Bicocca Milan Italy Evaluative Epidemiology Unit Department of Epidemiology and Data Science Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy Data Science and Advanced Analytics F. Hoffmann - La Roche Ltd Basel Switzerland University of Twente Enschede Netherlands Lugano Switzerland

Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-world observational data used to train and validate these models are almost always affected by biases that can strongly impact the outcomes validity: two examples are values missing not-at-random and selection bias. Addressing them is a key element in achieving transportability and in studying the causal relationships that are critical in clinical decision making, going beyond simpler statistical approaches based on probabilistic association. In this context, we propose a novel approach that combines selection diagrams, missingness graphs, causal discovery and prior knowledge into a single graphical model to estimate the cardiovascular risk of adolescent and young females who survived breast cancer. We learn this model from data comprising two different cohorts of patients. The resulting causal network model is validated by expert clinicians in terms of risk assessment, accuracy and explainability, and provides a prognostic model that outperforms competing machine learning methods. © 2023 CEUR-WS. All rights reserved.

关键词： Risk assessment

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Wearable Ultrasound devices for continuous health monitoring: Current and future Prospects 8

Wearable Ultrasound devices for continuous health monitoring...

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8th IEEE International Energy Conference, ENERGYCON 2024

作者： Ottakath, Najmath Al-Maadeed, Somaya Bouridane, Ahmed Chowdhury, Muhammad E.H. Sadasivuni, Kishor Kumar Department of Computer Science and Engineering Qatar University Doha Qatar Center of Data Analytics and Cybersecurity University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering Qatar University Doha Qatar Center for Advanced Materials Qatar University Doha Qatar

ISBN: (纸本)9798350382150

Advancements in wearable electronics and portable sensors have enabled long-term and real-time health monitoring. Among these advancements, wearable ultrasound devices have garnered attention due to their portability, ease of use, and ability to penetrate deep tissues in the human body. The images generated by these devices can be harnessed for computer vision tasks, providing valuable insights into an individual’s health status even during physical activity. This paper explores the design of such wearable ultrasound imagers, surveys their current applications, and offers insights into future prospects, including visual image processing and real-time analysis. The study also addresses the challenges of using wearable ultrasound for long-term health monitoring, emphasizing the need for ongoing innovation and research. ©2024 IEEE.

关键词： Ultrasonics

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Mathematical modeling of the synergetic effect between radiotherapy and immunotherapy

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Mathematical Biosciences and Engineering 2025年第5期22卷 1206-1225页

作者： Xing, Yixun Moore, Casey Saha, Debabrata Nguyen, Dan Bleile, MaryLena Jia, Xun Timmerman, Robert Peng, Hao Jiang, Steve Medical Artificial Intelligence and Automation Laboratory University of Texas Southwestern Medical Center DallasTX75390 United States Department of Advanced Data Analytics University of North Texas DentonTX76205 United States Department of Radiation Oncology University of Texas Southwestern Medical Center DallasTX75390 United States Department of Statistical Science Southern Methodist University DallasTX75275 United States

The synergy between radiotherapy and immunotherapy plays a pivotal role in enhancing tumor control and treatment outcomes. To explore the underlying mechanisms of synergy, we investigated a novel treatment approach known as personalized ultra-fractionated stereotactic adaptive radiation (PULSAR) therapy, which emphasizes the impact of radiation timing. Unlike conventional daily treatments, PULSAR delivers high-dose radiation in spaced intervals over weeks or months, enabling tumors to adapt and potentially enhancing synergy with immunotherapy. Drawing on insights from small-animal radiation studies, we developed a discrete-time model based on multiple difference equations to elucidate the temporal dynamics of tumor control driven by both radiation and the adaptive immune response. By accounting for the migration and infiltration of T cells within the tumor microenvironment, we established a quantitative link between radiation therapy and immunotherapy. Model parameters were estimated using a simulated annealing algorithm applied to training data, and our model achieved high accuracy for the test data with a root mean square error of 287 mm3. Notably, our framework replicated the PULSAR effect observed in animal studies, revealing that longer intervals between radiation treatments in the context of immunotherapy yielded enhanced tumor control. Specifically, mice receiving immunotherapy alongside radiation pulses delivered at extended intervals, ten days, showed markedly improved tumor responses, whereas those treated with shorter intervals did not achieve comparable benefits. Moreover, our model offers an in-silico tool for designing future personalized ultra-fractionated stereotactic adaptive radiation trials. Overall, these findings underscore the critical importance of treatment timing in harnessing the synergy between radiotherapy and immunotherapy and highlight the potential of our model to optimize and individualize treatment protocols. ©2025 the Author(s)

关键词： T-cells

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Environment Descriptions for Usability and Generalisation in Reinforcement Learning

arXiv

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arXiv 2024年

作者： Soemers, Dennis J.N.J. Samothrakis, Spyridon Driessens, Kurt Winands, Mark H.M. Department of Advanced Computing Sciences Maastricht University Maastricht Netherlands Institute for Analytics and Data Science University of Essex Colchester United Kingdom

The majority of current reinforcement learning (RL) research involves training and deploying agents in environments that are implemented by engineers in general-purpose programming languages and more advanced frameworks such as CUDA or JAX. This makes the application of RL to novel problems of interest inaccessible to small organisations or private individuals with insufficient engineering expertise. This position paper argues that, to enable more widespread adoption of RL, it is important for the research community to shift focus towards methodologies where environments are described in user-friendly domain-specific or natural languages. Aside from improving the usability of RL, such language-based environment descriptions may also provide valuable context and boost the ability of trained agents to generalise to unseen environments within the set of all environments that can be described in any language of choice. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural environment

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ACCELERATING DISTRIBUTED STOCHASTIC OPTIMIZATION VIA SELF-REPELLENT RANDOM WALKS

arXiv

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arXiv 2024年

作者： Hu, Jie Doshi, Vishwaraj Eun, Do Young Department of Electrical and Computer Engineering North Carolina State University United States Data Science & Advanced Analytics IQVIA Inc.

We study a family of distributed stochastic optimization algorithms where gradients are sampled by a token traversing a network of agents in random-walk fashion. Typically, these random-walks are chosen to be Markov chains that asymptotically sample from a desired target distribution, and play a critical role in the convergence of the optimization iterates. In this paper, we take a novel approach by replacing the standard linear Markovian token by one which follows a nonlinear Markov chain - namely the Self-Repellent Radom Walk (SRRW). Defined for any given ‘base’ Markov chain, the SRRW, parameterized by a positive scalar α, is less likely to transition to states that were highly visited in the past, thus the name. In the context of MCMC sampling on a graph, a recent breakthrough in Doshi et al. (2023) shows that the SRRW achieves O(1/α) decrease in the asymptotic variance for sampling. We propose the use of a ‘generalized’ version of the SRRW to drive token algorithms for distributed stochastic optimization in the form of stochastic approximation, termed SA-SRRW. We prove that the optimization iterate errors of the resulting SA-SRRW converge to zero almost surely and prove a central limit theorem, deriving the explicit form of the resulting asymptotic covariance matrix corresponding to iterate errors. This asymptotic covariance is always smaller than that of an algorithm driven by the base Markov chain and decreases at rate O(1/α2) - the performance benefit of using SRRW thereby amplified in the stochastic optimization context. Empirical results support our theoretical findings. © 2024, CC0.

关键词： Optimization

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Modeling the Impact of Transportation Availability and Travel Distance on Healthcare Outcomes: A Bagged Random Forest Approach

Modeling the Impact of Transportation Availability and Trave...

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IISE Annual Conference and Expo 2023

作者： Kasasbeh, Anemone Yildirim, Mehmet Booth, Amy Khan, Naseem Sayama, Hiroki Department of Systems Science and Industrial Engineering State University of New York at Binghamton BinghamtonNY13902 United States Advanced Data Analytics United Health Services Hospitals Inc. VestalNY13850 United States

ISBN: (纸本)9781713877851

The lack of access to healthcare can lead to poor health outcomes. Factors that prevent patients from getting to their medical care appointments can be associated with an increased risk of adverse health conditions. These factors, such as limited access to transportation and long travel distance, may lead to higher no-show rates and gaps in care. No-shows and gaps in care are claimed to be associated with more emergency department (ED) visits as well as more hospitalizations. In this study, we investigated the impact of travel distance and transportation availability on attending a medical care appointment. data used in this research were extracted from a community hospital in Broome County, New York. The acquired data spans over the time between January 2021 and July 2022. The response variable in this study is the patient-associated risk score. A bagged random forest meta-ensemble predictive model was developed using a k-fold cross-validation training approach. The model adequacy was assessed using performance measures, including accuracy, sensitivity, and ROC. Results showed that travel distance between a patient’s residential address and their healthcare provider negatively impacted their health outcomes. This finding can help design solutions and interventions such as transportation funding programs and/or expanding bus routes to provide broader transportation coverage. © IISE and Expo *** rights reserved.

关键词： Health risks

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MULABS: Multi-Task Learning with Attention-Based Scoring for Click-Through Rate Prediction on Sparse data in Healthcare Real-World Scenarios

MULABS: Multi-Task Learning with Attention-Based Scoring for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tu Tu Jing Zhang Yue Wang Hui Jin Megan Weihong He Data Science and Advanced Analytics IQVIA Inc. Shanghai China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Personalized recommender systems have found wide applications in healthcare since it has the potential to inform individual users about the most needed medical resources. However, most recommender systems levering deep learning techniques demand a rich amount of data to learn patterns. In this paper, we aim to improve the performance of a recommender system when data are sparse and in low volume. In particular, we introduce a multi-task learning approach to alleviate the learning pressure and propose an attention-based scheme to boost the model performance by fully utilizing users’ historical data. In the experiments, we develop a novel dataset from a public dataset to mimic the real-world scenarios in healthcare. The results demonstrate that this multi-task learning with attention-based scoring (MULABS) aided recommender system is more accurate in predicting user preference with an AUC of 0.7541, requiring limited computation and memory resources.

关键词： Industries Deep learning Costs Memory management Medical services Multitasking Motion pictures

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Design of functional estimation-based controller for the stabilization of time-delayed systems

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The European Physical Journal Special Topics 2024年 1-12页

作者： Gomathi, D. Harshavarthini, S. Sathiyaraj, T. Harikaran, B. Sakthivel, R. Department of Mathematics School of Advanced Sciences VIT University Chennai India Institute of Actuarial Science and Data Analytics UCSI University Cheras Malaysia Department of Applied Mathematics Bharathiar University Coimbatore India

This research delves in addressing the stabilization challenge in linear time-varying delayed systems through the utilization of functional observers (FOs) in feedback control. More precisely, a linear function or functions of the system states are estimated, driven by the available system inputs and outputs. Instead of using traditional methods that estimate the complete set of states, this strategic inclusion enables the estimation of certain functions of the states. The benefit of this novel approach is that it does not require the observed system to be observable. We define stability conditions in terms of linear matrix inequalities, based on the Lyapunov theory. The gain matrices are taken into consideration based on predetermined standards. Finally, we provide numerical illustrations and simulation outcomes to showcase the feasibility and efficiency of our proposed control approach.

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Additive Manufacturing of Ti6Al4V Using Directed Energy Deposition: An Elaborative Approach of Process Modelling via Computational Fluid Dynamics

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Lasers in Manufacturing and Materials Processing 2025年第1期12卷 201-225页

作者： Ur Rehman, Asif Azher, Kashif Rehman, Aqsa Shah, Mussadiq Mahmood, Muhammad Arif Tüfekci, Celal Sami Salamci, Metin Uymaz Department of Mechanical Engineering Gazi University Ankara Turkey Bartlett School of Sustainable Construction University College London LondonWC1E 6BT United Kingdom Department of Mechanical Engineering King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Data Science and Analytics Institute The University of Oklahoma NormanOK73019 United States Institute of Chemical Technologies and Analytics TU Wien Getreidemarkt 9 Vienna1060 Austria Intelligent Systems Center Missouri University of Science and Technology RollaMO65409 United States Advanced Manufacturing Technologies Center of Excellence-URTEMM Ankara Turkey Additive Manufacturing Technologies Application and Research Center-EKTAM Ankara Turkey

Additive manufacturing (AM) has revolutionized many industries. Directed energy deposition (DED) offers remarkable utility in size and is an effective method in terms of energy efficiency. The interaction of powder with a laser before coming in contact with the substrate helps to attain fair melting and deposition. However, there is a problem associated with non-uniformity and accumulation of metallic melt on the edges of the manufactured parts which needs to be understood. Herein, the DED process of Ti6Al4V alloy is presented. The phenomenon of recoil pressure and Marangoni actions along the single tracks of the melt pool is defined via the computational fluid dynamics (CFD) analysis. The discrete element modeling (DEM) improves the simulation model output as variable particle diameters are considered, thus mimicking the actual powder size distribution. The simulation results disclosed intricate facts about the flow dynamics of molten material under varying conditions. The stream traces and thermal gradients are evaluated to recognize the effect of laser movement on material cooling and surface tension. The material accumulation occurred at curves and borders due to cooling-induced surface tension alterations, affecting the attribute of the deposited layers. The bi-directional flow patterns are prompted by Marangoni convection and recoil pressure, causing distinct features, including lateral menisci and depression modes. The cooling rates impacted the liquid portion and dilution levels, critical for attaining optimal bonding between deposited layers and substrate. Experimental endorsement using Synchrotron X-ray imaging favored the simulation results, confirming the simulated Marangoni convection effects. This comparison highlights the efficiency of CFD models in predicting molten material behavior, offering a worthy tool for adjusting DED printing parameters. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature

关键词： Titanium alloys

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A Rationale-Centric Framework for Human-in-the-loop Machine Learning 60

A Rationale-Centric Framework for Human-in-the-loop Machine ...

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60th Annual Meeting of the Association for Computational Linguistics, ACL 2022

作者： Lu, Jinghui Yang, Linyi Namee, Brian Mac Zhang, Yue The Insight Centre for Data Analytics University College Dublin Ireland School of Computer Science University College Dublin Ireland School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China SenseTime Research

ISBN: (纸本)9781955917216

We present a novel rationale-centric framework with human-in-the-loop - Rationales-centric Double-robustness Learning (RDL) - to boost model out-of-distribution performance in few-shot learning scenarios. By using static semi-factual generation and dynamic human-intervened correction, RDL exploits rationales (i.e. phrases that cause the prediction), human interventions and semi-factual augmentations to decouple spurious associations and bias models towards generally applicable underlying distributions, which enables fast and accurate generalisation. Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-distribution tests compared to many state-of-the-art benchmarks-especially for few-shot learning scenarios. We also perform extensive ablation studies to support in-depth analyses of each component in our framework. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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