Cardiovascular disease (CVD) is a serious illness affecting millions world-wide and is the leading cause of death in the US. Recent years, however, have seen tremendous growth in the area of personalized medicine, a field of medicine that places the patient at the center of the medical decision-making and treatment process. Many CVD-focused personalized medicine innovations focus on genetic biomarkers, which provide person-specific CVD insights at the genetic level, but do not focus on the practical steps a patient could take to mitigate their risk of CVD development. In this work we propose longitudinal inverse classification, a recommendation framework that provides personalized lifestyle recommendations that minimize the predicted probability of CVD risk. Our framework takes into account historical CVD risk, as well as other patient characteristics, to provide recommendations. Our experiments show that earlier adoption of the recommendations elicited from our framework produce significant CVD risk reduction.
MyDigiTwin: A Privacy-Preserving Framework for Personalized Cardiovascular Risk Prediction and Scenario Exploration
organization= Netherlands eScience Center , addressline= Matrix THREE, Science Park 402 , city= Amsterdam , postcode= 1098 XH , country= The Netherlands；organization= Department of Radiology \& Nuclear Medicine, Erasmus MC University Medical Center Rotterdam , addressline= Dr. Molewaterplein 40 , city= Rotterdam , postcode= 3015 GD , country= The Netherlands；organization= Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London , addressline= 62 Huntley St , city= London , postcode= WC1E 6DD , country= United Kingdom；organization= Department of Cardiology, Amsterdam Cardiovascular Science, Amsterdam University Medical Centers, University of Amsterdam , addressline= Meibergdreef 9 , city= Amsterdam , postcode= 1105 AZ , country= The Netherlands；organization= Department of Epidemiology, Erasmus MC University Medical Center Rotterdam , addressline= Dr. Molewaterplein 40 , city= Rotterdam , postcode= 3015 GD , country= The Netherlands；organization= Department of Cardiology, University Medical Center Utrecht , addressline= Heidelberglaan 100 , city= Utrecht , postcode= 3584 CX , country= The Netherlands
Northeastern University（东北大学）；University of Maryland（马里兰大学）；New York University School of Medicine（纽约大学医学院）；NYU Langone Health（纽约大学朗格尼健康中心）
Comorbid chronic conditions are common among people with type 2 diabetes. We developed an Artificial Intelligence algorithm, based on Reinforcement Learning (RL), for personalized diabetes and multi-morbidity management with strong potential to improve health outcomes relative to current clinical practice. In this paper, we modeled glycemia, blood pressure and cardiovascular disease (CVD) risk as health outcomes using a retrospective cohort of 16,665 patients with type 2 diabetes from New York University Langone Health ambulatory care electronic health records in 2009 to 2017. We trained a RL …
Cardiovascular disorders account for nearly 1 in 3 deaths in the United States. Care for these disorders are often determined during visits to acute care facilities, such as hospitals. While the length of stay in these settings represents just a small proportion of patients' lives, they account for a disproportionately large amount of decision making. To overcome this bias towards data from acute care settings, there is a need for longitudinal monitoring in patients with cardiovascular disorders. Longitudinal monitoring can provide a more comprehensive picture of patient health, allowing for m…
Leveraging Personalized PageRank and Higher-Order Topological Structures for Heterophily Mitigation in Graph Neural Networks
School of New Media and Communication, Tianjin University（天津大学新媒体与传播学院）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学院）；Yazhou Bay Innovation Institute, Hainan Tropical Ocean University（海南热带海洋大学亚龙湾创新研究院）；Key Lab of Education Blockchain and Intelligent Technology, Guangxi Normal University（广西师范大学教育区块链与智能技术重点实验室）
Few-Label Multimodal Modeling of SNP Variants and ECG Phenotypes Using Large Language Models for Cardiovascular Risk Stratification
University of New South Wales, Australia（新南威尔士大学，澳大利亚）；University of Surrey, United Kingdom（萨里大学，英国）；Mohamed bin Zayed University of Artificial Intelligence, UAE（穆罕默德·本·扎耶德人工智能大学，阿联酋）
National Technical University of Athens（雅典国立技术大学）；University of Athens（雅典大学）
Cardiovascular Disease (CVD) is an important cause of disability and death among individuals with Diabetes Mellitus (DM). International clinical guidelines for the management of Type 2 DM (T2DM) are founded on primary and secondary prevention and favor the evaluation of CVD related risk factors towards appropriate treatment initiation. CVD risk prediction models can provide valuable tools for optimizing the frequency of medical visits and performing timely preventive and therapeutic interventions against CVD events. The integration of explainability modalities in these models can enhance human…
From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification
BCN MedTech, Department of Engineering, Universitat Pompeu Fabra（BCN MedTech，工程系，庞培法布拉大学）；European Commission, Joint Research Centre (JRC)（欧洲委员会，联合研究中心（JRC））
Real-Time Surrogate Modeling for Personalized Blood Flow Prediction and Hemodynamic Analysis
Laboratory of Hemodynamics and Cardiovascular Technology, EPFL（EPFL血液动力学与心血管技术实验室）；Stanford DBDS and HAI, Stanford University（斯坦福大学DBDS与HAI）；Medical School, National and Kapodistrian University of Athens（雅典大学医学院）
Massachusetts Institute of Technology（麻省理工学院）；Massachusetts General Hospital（麻省总医院）
Current clinical practice guidelines for managing Coronary Artery Disease (CAD) account for general cardiovascular risk factors. However, they do not present a framework that considers personalized patient-specific characteristics. Using the electronic health records of 21,460 patients, we created data-driven models for personalized CAD management that significantly improve health outcomes relative to the standard of care. We develop binary classifiers to detect whether a patient will experience an adverse event due to CAD within a 10-year time frame. Combining the patients' medical history an…
The Sound of Death: Deep Learning Reveals Vascular Damage from Carotid Ultrasound
German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany RPTU University Kaiserslautern-Landau, 67663 Kaiserslautern, Germany University Medical Center of the Johannes Gutenberg-University Mainz, Germany German Center for Cardiovascular Research (DZHK), Germany
Northeastern University（东北大学）；Partners Connected Health Innovation（帕特纳医疗健康创新中心）；Harvard Medical School（哈佛医学院）
Hypertension is a major risk factor for stroke, cardiovascular disease, and end-stage renal disease, and its prevalence is expected to rise dramatically. Effective hypertension management is thus critical. A particular priority is decreasing the incidence of uncontrolled hypertension. Early identification of patients at risk for uncontrolled hypertension would allow targeted use of personalized, proactive treatments. We develop machine learning models (logistic regression and recurrent neural networks) to stratify patients with respect to the risk of exhibiting uncontrolled hypertension within…
Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions
Department of Industrial Engineering and Management Systems University of Central Florida（工业工程与管理系统系 佛罗里达中央大学）；Institute for Simulation and Training University of Central Florida（模拟与培训研究所 佛罗里达中央大学）；Department of Internal Medicine University of Central Florida（内科医学系 佛罗里达中央大学）；Department of Statistics and Data Science University of Central Florida（统计学与数据科学系 佛罗里达中央大学）
Physics-informed self-supervised learning for predictive modeling of coronary artery digital twins
Chair of Mathematical Data Science, EPFL, Lausanne, Switzerland（数学数据科学教授职位，苏黎世联邦理工学院，拉沃斯，瑞士）；LTS4 laboratory, EPFL, Lausanne, Switzerland（LTS4实验室，苏黎世联邦理工学院，拉沃斯，瑞士）；School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University, China（人工智能与高级计算学院，西安交通大学利物浦大学，中国）；Cardiology Department, Lausanne University Center Hospital, Lausanne, Switzerland（心血管科，拉沃斯大学中心医院，拉沃斯，瑞士）；OLV Hospital, Aalst, Belgium（OLV医院，阿尔斯特，比利时）；AI Center, EPFL, Lausanne, Switzerland（人工智能中心，苏黎世联邦理工学院，拉沃斯，瑞士）
Whole-body Representation Learning For Competing Preclinical Disease Risk Assessment
Institute of Diagnostic；Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany Chair for AI in Healthcare；Medicine, Technical University of Munich (TUM)；TUM University Hospital, Munich, Germany Department of Computing, Imperial College London, London, UK German Cancer Consortium (DKTK), Munich partner site, Heidelberg, Germany
