Data scientists and statisticians are often at odds when determining the best approach, machine learning or statistical modeling, to solve an analytics challenge. However, machine learning and statistical modeling are more cousins than adversaries on different sides of an analysis battleground. Choosing between the two approaches or in some cases using both is based on the problem to be solved and outcomes required as well as the data available for use and circumstances of the analysis. Machine learning and statistical modeling are complementary, based on similar mathematical principles, but simply using different tools in an overall analytics knowledge base. Determining the predominant approach should be based on the problem to be solved as well as empirical evidence, such as size and completeness of the data, number of variables, assumptions or lack thereof, and expected outcomes such as predictions or causality. Good analysts and data scientists should be well versed in both techniques and their proper application, thereby using the right tool for the right project to achieve the desired results.
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Microsoft Research Asia（微软亚洲研究院）
The success of machine learning applications often needs a large quantity of data. Recently, federated learning (FL) is attracting increasing attention due to the demand for data privacy and security, especially in the medical field. However, the performance of existing FL approaches often deteriorates when there exist domain shifts among clients, and few previous works focus on personalization in healthcare. In this article, we propose FedHealth 2, an extension of FedHealth \cite{chen2020fedhealth} to tackle domain shifts and get personalized models for local clients. FedHealth 2 obtains the …
Parkland Center for Clinical Innovation（帕克兰临床创新中心）；Dallas, TX 75247, USA（美国得克萨斯州达拉斯市75247（注：此条为地址，非机构，按要求不应提取，实际仅需保留Parkland Center for Clinical Innovation））
In recent times, machine learning (ML) and artificial intelligence (AI) based systems have evolved and scaled across different industries such as finance, retail, insurance, energy utilities, etc. Among other things, they have been used to predict patterns of customer behavior, to generate pricing models, and to predict the return on investments. But the successes in deploying machine learning models at scale in those industries have not translated into the healthcare setting. There are multiple reasons why integrating ML models into healthcare has not been widely successful, but from a techni…
Rice University（莱斯大学）；University of Houston-Downtown（休斯顿大学市中心分校）；Prudence Medical Technologies Ltd.（审慎医疗技术有限公司）
The Internet of Things (IoT) revolution has shown potential to give rise to many medical applications with access to large volumes of healthcare data collected by IoT devices. However, the increasing demand for healthcare data privacy and security makes each IoT device an isolated island of data. Further, the limited computation and communication capacity of wearable healthcare devices restrict the application of vanilla federated learning. To this end, we propose an advanced federated learning framework to train deep neural networks, where the network is partitioned and allocated to IoT devic…
As hospitals move towards automating and integrating their computing systems, more fine-grained hospital operations data are becoming available. These data include hospital architectural drawings, logs of interactions between patients and healthcare professionals, prescription data, procedures data, and data on patient admission, discharge, and transfers. This has opened up many fascinating avenues for healthcare-related prediction tasks for improving patient care. However, in order to leverage off-the-shelf machine learning software for these tasks, one needs to learn structured representatio…
Deep learning continues to revolutionize an ever-growing number of critical application areas including healthcare, transportation, finance, and basic sciences. Despite their increased predictive power, model transparency and human explainability remain a significant challenge due to the "black box" nature of modern deep learning models. In many cases the desired balance between interpretability and performance is predominately task specific. Human-centric domains such as healthcare necessitate a renewed focus on understanding how and why these frameworks are arriving at critical and potential…
Recognizing the patient's emotions using deep learning techniques has attracted significant attention recently due to technological advancements. Automatically identifying the emotions can help build smart healthcare centers that can detect depression and stress among the patients in order to start the medication early. Using advanced technology to identify emotions is one of the most exciting topics as it defines the relationships between humans and machines. Machines learned how to predict emotions by adopting various methods. In this survey, we present recent research in the field of using …
University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
Developing new drugs for target diseases is a time-consuming and expensive task, drug repurposing has become a popular topic in the drug development field. As much health claim data become available, many studies have been conducted on the data. The real-world data is noisy, sparse, and has many confounding factors. In addition, many studies have shown that drugs effects are heterogeneous among the population. Lots of advanced machine learning models about estimating heterogeneous treatment effects (HTE) have emerged in recent years, and have been applied to in econometrics and machine learnin…
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection
University Center Salhi Ahmed（萨希·艾哈迈德大学中心）；University of Medea（迈代阿大学）；University of Dubai（迪拜大学）；Ecole Nationale Polytechnique（国立理工学院）；Texas A&M University（德克萨斯农工大学）；Northumbria University（诺森比亚大学）；Qatar University（卡塔尔大学）
Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）；Wellframe Inc.（Wellframe公司）
It is not clear how to target patients who are most likely to benefit from digital care management programs ex-ante, a shortcoming of current risk score based approaches. This study focuses on defining impactability by identifying those patients most likely to benefit from technology enabled care management, delivered through a digital health platform, including a mobile app and clinician web dashboard. Anonymized insurance claims data were used from a commercially insured population across several U.S. states and combined with inferred sociodemographic data and data derived from the patient-h…
Missing data imputation for noisy time-series data and applications in healthcare
Faculty of Mathematics and Computer Science, University of Science（胡志明市科学大学数学与计算机科学学院）；Vietnam National University（越南国家大学）；University of Medicine and Pharmacy at Ho Chi Minh City（胡志明市医药大学）；Simula Metropolitan（西穆拉都会研究院）；AISIA Research Laboratory（AISIA研究院）
Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；School of Automation, Guangdong University of Technology（广东工业大学自动化学院）
Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Information Technology University (ITU) Punjab（旁遮普信息技术大学（ITU））
Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, have gained popularity as a viable solution in such settings. In this paper, we leverage the capabilities of edge computing in medicine by analyzing and evaluating the potential of intelligent processing of clinical visual data at the …
Diffmv: A Unified Diffusion Framework for Healthcare Predictions with Random Missing Views and View Laziness
The Hong Kong University of Science and Technology（香港科学与技术大学）；College of Management and Economics & Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University（管理学院及复杂管理系统计算与分析实验室，天津大学）
针对 EHR 多视图中的随机缺失与视图惰性问题，提出基于扩散生成与视图重加权的统一框架 Diffmv，并在三个数据集的多视图、多模态医疗预测任务中验证其优越性能。
Electronic Healthcare records contain large volumes of unstructured data in different forms. Free text constitutes a large portion of such data, yet this source of richly detailed information often remains under-used in practice because of a lack of suitable methodologies to extract interpretable content in a timely manner. Here we apply network-theoretical tools to the analysis of free text in Hospital Patient Incident reports in the English National Health Service, to find clusters of reports in an unsupervised manner and at different levels of resolution based directly on the free text desc…
Explainable Machine Learning in Healthcare: Methods, Interpretation, and Applications for Clinical Research
Madrigal Pharmaceuticals Inc（马德里加尔制药公司）；Boston University School of Medicine（波士顿大学医学院）；AbbVie Inc.（艾伯维公司）；St. Elizabeth Healthcare（圣伊丽莎白医疗中心）；Thermo Fisher Scientific（赛默飞世尔科技）
Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
Large scale contextual representation models, such as BERT, have significantly advanced natural language processing (NLP) in recently years. However, in certain area like healthcare, accessing diverse large scale text data from multiple institutions is extremely challenging due to privacy and regulatory reasons. In this article, we show that it is possible to both pretrain and fine tune BERT models in a federated manner using clinical texts from different silos without moving the data.
DRIM: Learning Disentangled Representations from Incomplete Multimodal Healthcare Data
IUCT-Oncopole Institut Claudius Regaud（克劳迪乌斯·雷戈德研究所肿瘤学中心）；IRT Saint Exupéry（圣埃克苏佩里技术研究院）；INSERM UMR 1037, Cancer Research Center of Toulouse (CRCT)（法国国家健康与医学研究院UMR 1037，图卢兹癌症研究中心）
Investigating Quantum Feature Maps in Quantum Support Vector Machines for Lung Cancer Classification
Quantum Physics and Spintronics Team, Condensed Matter Physics Laboratory (LPMC), Faculty of Sciences Ben M’Sik, Hassan II University of Casablanca, Morocco（量子物理与自旋电子学团队，凝聚态物理实验室（LPMC），科学学院本马克，卡萨布兰卡哈桑二世大学，摩洛哥）
