Multiple Sclerosis (MS) is a chronic disease developed in human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale (EDSS), composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) creates opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning and deep learning algorithms were limited in terms of prediction accuracy due to the data insufficiency or model simplicity. In this paper, we proposed an idea of using patients' multimodal longitudinal and longitudinal EHR data to predict multiple sclerosis disease severity at the hospital visit. This work has two important contributions. First, we describe a pilot effort to leverage structured EHR data, neuroimaging data and clinical notes to build a multi-modal deep learning framework to predict patient's MS disease severity. The proposed pipeline demonstrates up to 25% increase in terms of the area under the Area Under the Receiver Operating Characteristic curve (AUROC) compared to models using single-modal data. Second, the study also provides insights regarding the amount useful signal embedded in each data modality with respect to MS disease prediction, which may improve data collection processes.
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data
Science Foundation Ireland Centre for Research Training in Machine Learning（科学基金会爱尔兰机器学习研究培训中心）；School of Medicine（医学院）；School of Computer Science（计算机科学学院）；University College Dublin（都柏林大学）；Insight Centre for Data Analytics（数据分析研究所）
Chronological Contrastive Learning: Few-Shot Progression Assessment in Irreversible Diseases
Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna（维也纳医科大学生物医学成像与图像引导治疗系计算成像研究实验室）；Comprehensive Center for Artificial Intelligence in Medicine, Medical University of Vienna（维也纳医科大学综合医学人工智能中心）；Division of Rheumatology, Department of Medicine III, Medical University of Vienna（维也纳医科大学第三内科风湿病科）；Johnson & Johnson（强生公司）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（麻省总医院Athinoula A. Martinos生物医学成像中心）；Ludwig Boltzmann Institute of Arthritis and Rehabilitation（路德维希·玻尔兹曼关节炎与康复研究所）
Multiple Sclerosis Severity Classification From Clinical Text
University of Toronto（多伦多大学）；Unity Health Toronto（联合健康多伦多）；Vector Institute（向量研究所）；Ontario Institute for Cancer Research（安大略癌症研究所）；Toronto Rehabilitation Institute（多伦多康复研究所）；Bloorview Research Institute（布洛尔维尤研究所）
Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis
University of Zurich（苏黎世大学）；Oslo University Hospital（奥斯陆大学医院）；University of Oslo（奥斯陆大学）；University Hospital Zurich（苏黎世大学医院）；Bogomolets National Medical University（博格莫列茨国立医科大学）；CHU Lille（里尔大学医院中心）；Lille University（里尔大学）；University of Brescia（布雷西亚大学）；Charles University（查理大学）；University College London（伦敦大学学院）；University of Split（斯普利特大学）；University Hospital Center Split（斯普利特大学医院中心）
STEP-PD: Stage-Aware and Explainable Parkinson's Disease Severity Classification Using Multimodal Clinical Assessments
School of Computing & Information Sciences, Florida International University（佛罗里达国际大学计算机与信息科学学院）；College of AI, Cybersecurity and Computing, University of South Florida（佛罗里达州立大学人工智能、网络安全与计算学院）
Multimodal Ordinal Modeling of Alzheimer's Disease Severity Using Structural MRI and Clinical Data
Department of Neuroradiology, LMU University Hospital, Ludwig Maximilian University of Munich（神经放射科，慕尼黑路德维希-马克西米利安大学医院，慕尼黑路德维希-马克西米利安大学）；Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig Maximilian University of Munich（精神病学与心理治疗系，慕尼黑路德维希-马克西米利安大学医院，慕尼黑路德维希-马克西米利安大学）
A Machine Learning Pipeline for Multiple Sclerosis Biomarker Discovery: Comparing explainable AI and Traditional Statistical Approaches
Department of Computer Science , University of Pisa（计算机科学系，比萨大学）；Department of Clinical and Biological Sciences, University of Torino（临床与生物科学系，都灵大学）；Department of Computer Sciences, University of Torino（计算机科学系，都灵大学）；Department of Computer Science and Engineering, University of Bologna（计算机科学与工程系，博洛尼亚大学）
该研究构建整合八个 PBMC 微阵列数据集的机器学习流水线，用 XGBoost 与 SHAP 发现 MS 生物标志物，并与 DEA 比较，证明二者互补且可揭示相关疾病通路。
Learning a Distance for the Clustering of Patients with Amyotrophic Lateral Sclerosis
Laboratoire LIFAT, Université de Tours（图雷大学LIFAT实验室）；Laboratoire LIFO, Université d'Orléans（奥尔良大学LIFO实验室）；Service de Biochimie et Biologie Moléculaire CHRU de Tours（图雷中心医院生物化学与分子生物学服务）；UMR U1253 iBrain, Université de Tours（图雷大学UMR U1253 iBrain）；Inserm（法国国家医学研究院）
CovidLLM: A Robust Large Language Model with Missing Value Adaptation and Multi-Objective Learning Strategy for Predicting Disease Severity and Clinical Outcomes in COVID-19 Patients
School of Mathematics and Statistics, Xiamen University of Technology（厦门理工学院数学与统计学院）；School of Computer and Information Engineering, Xiamen University of Technology（厦门理工学院计算机与信息工程学院）；Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院）
A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
Technical University of Denmark（丹麦技术大学）；Athinoula A. Martinos Center for Biomedical Imaging（阿西努拉·A·马丁诺斯生物医学成像中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Technical University of Munich（慕尼黑工业大学）
Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjects
Center for Data Science, New York University（纽约大学数据科学中心）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Courant Institute of Mathematical Sciences, NYU（纽约大学柯朗数学科学研究所）
Learning Disease Progression Models That Capture Health Disparities
Cornell Tech Cornell University（康奈尔科技 康奈尔大学）；NewYork-Presbyterian Weill Cornell Medical College（纽约- Presbyterian 威廉斯堡康奈尔医学学院）；NewYork-Presbyterian Columbia University Irving Medical Center（纽约- Presbyterian 哥伦比亚大学伊文思医疗中心）；University of California Berkeley（加州大学伯克利分校）
COVID-Net S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity
University of Waterloo（滑铁卢大学）；DarwinAI Corp.（DarwinAI公司）；Stony Brook School of Medicine（石溪大学医学院）；Montefiore Medical Center（蒙蒂菲奥里医疗中心）；Albert Einstein College of Medicine（阿尔伯特·爱因斯坦医学院）
Learning Disease-Sensitive Latent Interaction Graphs From Noisy Cardiac Flow Measurements
Department of Computer Science, University of Bath（巴斯大学计算机科学系）；Department of Mechanical Engineering, University of Bath（巴斯大学机械工程系）；Christian Doppler Laboratory for Mechanical Circulatory Support, Department of Cardiac and Thoracic Aortic Surgery, Medical University of Vienna（维也纳医学大学心脏和胸主动脉外科系基督教多普勒实验室）
Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis
Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of Lausanne（洛桑大学）；Siemens Healthcare AG（西门子医疗股份公司）；Lausanne University Hospital（洛桑大学医院）；University Hospital Basel（巴塞尔大学医院）；University of Basel（巴塞尔大学）
Predicting Patient COVID-19 Disease Severity by means of Statistical and Machine Learning Analysis of Blood Cell Transcriptome Data
Bangabandhu Sheikh Mujibur Rahman Science & Technology University（班加班杜谢赫穆吉布·拉赫曼科技大学）；University of Rajshahi（拉杰沙希大学）；University of Technology Sydney（悉尼科技大学）；The University of Sydney（悉尼大学）；Jatiya Kabi Kazi Nazrul Islam University（贾蒂亚·卡比·卡齐·纳兹鲁尔·伊斯兰大学）；Imam Mohammad Ibn Saud Islamic University（伊玛目穆罕默德·伊本·沙特伊斯兰大学）；University of New South Wales（新南威尔士大学）；Deakin University（迪肯大学）；The Garvan Institute of Medical Research（加尔文医学研究所）
Interpretable machine learning predicts Parkinson's disease severity using motion-corrected QSM MRI and multiband multiecho fMRI features
Lyda Hill Department of Bioinformatics（Lyda Hill 生物信息学系）；Department of Biomedical Engineering（生物医学工程系）；University of Texas Southwestern Medical Center, Dallas, Texas, USA（德克萨斯西南医学中心，德克萨斯州达拉斯）
Gradient based Severity Labeling for Biomarker Classification in OCT
Georgia Institute of Technology（佐治亚理工学院）；Center for Signal and Information Processing CSIP（信号与信息处理中心）；School of Electrical and Computer Engineering（电气与计算机工程学院）
Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data
City University of Hong Kong（香港城市大学）；University of Sydney（悉尼大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of Southern California（南加利福尼亚大学）
A Sparse-Attention Deep Learning Model Integrating Heterogeneous Multimodal Features for Parkinson's Disease Severity Profiling
School of Computing, Mathematics, and Engineering, Charles Sturt University（计算、数学与工程学院，查尔斯·斯特劳特大学）；School of Dentistry and Medical Sciences, Charles Sturt University（牙科学院与医学科学学院，查尔斯·斯特劳特大学）；AI and Cyber Futures Centre (AICF)（人工智能与未来技术中心（AICF））；Hawkins Clinic General Medical Practice（霍金斯诊所普通医疗实践）
