Explainable AI meets Healthcare: A Study on Heart Disease Dataset
Pandit Deendayal Petroleum University（潘迪特·迪恩达亚尔石油大学）；Confirm SFI Centre for Smart Manufacturing（Confirm SFI智能制造中心）；Data Science Institute（数据科学研究所）；National University of Ireland Galway（爱尔兰国立大学戈尔韦分校）
Devam Dave, Het Naik, Smiti Singhal, Pankesh Patel
With the increasing availability of structured and unstructured data and the swift progress of analytical techniques, Artificial Intelligence (AI) is bringing a revolution to the healthcare industry. With the increasingly indispensable role of AI in healthcare, there are growing concerns over the lack of transparency and explainability in addition to potential bias encountered by predictions of the model. This is where Explainable Artificial Intelligence (XAI) comes into the picture. XAI increases the trust placed in an AI system by medical practitioners as well as AI researchers, and thus, eventually, leads to an increasingly widespread deployment of AI in healthcare.
In this paper, we present different interpretability techniques. The aim is to enlighten practitioners on the understandability and interpretability of explainable AI systems using a variety of techniques available which can be very advantageous in the health-care domain. Medical diagnosis model is responsible for human life and we need to be confident enough to treat a patient as instructed by a black-box model. Our paper contains examples based on the heart disease dataset and elucidates on how the explainability techniques should be preferred to create trustworthiness while using AI systems in healthcare.
University of Lille（里尔大学）；ENSAIT（国立高等艺术与纺织工业学院）；GEMTEX–Laboratoire de Génie et Matériaux Textiles（纺织工程与材料实验室）；University of Kent（肯特大学）
Deep learning play a vital role in classifying different arrhythmias using the electrocardiography (ECG) data. Nevertheless, training deep learning models normally requires a large amount of data and it can lead to privacy concerns. Unfortunately, a large amount of healthcare data cannot be easily collected from a single silo. Additionally, deep learning models are like black-box, with no explainability of the predicted results, which is often required in clinical healthcare. This limits the application of deep learning in real-world health systems. In this paper, we design a new explainable a…
Institute for Informatics and Telematics (IIT), National Research Council of Italy (CNR)（意大利国家研究委员会（CNR）信息学与电信研究所（IIT））
Nowadays Artificial Intelligence (AI) has become a fundamental component of healthcare applications, both clinical and remote, but the best performing AI systems are often too complex to be self-explaining. Explainable AI (XAI) techniques are defined to unveil the reasoning behind the system's predictions and decisions, and they become even more critical when dealing with sensitive and personal health data. It is worth noting that XAI has not gathered the same attention across different research areas and data types, especially in healthcare. In particular, many clinical and remote health appl…
University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；University Of Maryland（马里兰大学）
Heart Rate Variability (HRV) measures the variation of the time between consecutive heartbeats and is a major indicator of physical and mental health. Recent research has demonstrated that photoplethysmography (PPG) sensors can be used to infer HRV. However, many prior studies had high errors because they only employed signal processing or machine learning (ML), or because they indirectly inferred HRV, or because there lacks large training datasets. Many prior studies may also require large ML models. The low accuracy and large model sizes limit their applications to small embedded devices and…
A Brief Review of Explainable Artificial Intelligence in Healthcare
Dalhousie University（达尔豪斯大学）；Deakin University（迪肯大学）；TU Wien University（维也纳技术大学）；University of Kotli Azad Jammu and Kashmir（科特利阿扎德查谟和克什米尔大学）；Dibrugarh University（迪布鲁格尔大学）；Al-Balqa Applied University（巴尔卡应用大学）；The University of Jordan（约旦大学）；University of Granada（格拉纳达大学）；Jan Evangelista Purkyně University in Ústí nad Labem（拉贝河畔乌斯季扬·埃万杰利斯塔·普尔基涅大学）；Charles University（查理大学）；Harvard University（哈佛大学）；University of Florida（佛罗里达大学）
Explainable Machine Learning Framework for Cardiovascular Disease Diagnosis and Prognosis
Engineering Bangladesh University, Dhaka-1207, Bangladesh.；Technology University, Santosh, Tangail-1902.；Engineering University of Information Technology \ Sciences, Dhaka, Bangladesh. moradul\；Dept. of Computer Science Maharishi International University, Fairfield, IA 52557, United States.；\ Bangladesh University, Dhaka-1207, Bangladesh.；a Deparment of Computer Science \& Engineering, Bangladesh University, Dhaka-1207, Bangladesh.；Tecnology University, Tangail-1902, Bangladesh.；c Deparment of Computer Science \& Engineering, University of Information Technology of Sciences, Dhaka, Bangladesh.；d Department of Computer Science,Fairfield, IA 52557, Maharishi International University, United States.
Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook
Department of Computer Science, University of York, Heslington, United Kingdom（英国约克大学计算机科学系）；French-Azerbaijani University under Azerbaijan State Oil and Industry University（阿塞拜疆国家石油和工业大学下的法语-阿塞拜疆大学）
Explainable Uncertainty Estimation for Reliable Medical AI
College of Computing and Data Science (CCDS)（计算与数据科学学院）；Nanyang Technological University (NTU)（南洋理工大学）；A*STAR Centre for Frontier AI Research（新加坡科技研究局前沿人工智能研究中心）；School Of Computing and Digital Technologies（计算与数字技术学院）；Sheffield Hallam University（谢菲尔德哈勒姆大学）；School of Computing（计算机学院）；University of Utah（犹他大学）；Tan Tock Seng Hospital (TTSH)（陈笃生医院）；Lee Kong Chian School of Medicine（李光前医学院）
Breast Cancer Diagnosis: A Comprehensive Exploration of Explainable Artificial Intelligence (XAI) Techniques
Canadian Institute of Cybersecurity (CIC)（加拿大网络安全研究所）；University of New Brunswick（新不伦瑞克大学）；Dipartimento di Informatica, Università di Verona（威尼斯大学信息学院）；Università di Verona（威尼斯大学）；Department of Computer Science, School of Mathematics and Computer Science, Institute of Business Administration（计算机科学系，数学与计算机科学学院，商学院）；LIRIS, CNRS, Université Claude Bernard Lyon 1（LIRIS，国家科学研究中心，克莱尔伯恩大学 Lyon 1 分校）；LHC, UMR5516, Université de Saint-Etienne（LHC，UMR5516，圣艾蒂安大学）
BACON: A fully explainable AI model with graded logic for decision making problems
Department of Information Systems University of Maryland, Baltimore County (UMBC)（信息系统系大学马里兰大学巴尔的摩县（UMBC））；Department of Computer Science San Francisco State University（计算机科学系旧金山州立大学）；Department of Information Systems UMBC（信息系统系大学马里兰大学巴尔的摩县（UMBC））
Amsterdam University Medical Center, University of Amsterdam（阿姆斯特丹大学医学中心，阿姆斯特丹大学）；Institute for Logic, Language, and Computation, University of Amsterdam（阿姆斯特丹大学逻辑、语言与计算研究所）；Pacmed；Amsterdam Business School, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹商学院）
The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the debate around adoption of this technology. One thread of such debate concerns Explainable AI (XAI) and its promise to render AI devices more transparent and trustworthy. A few voices active in the medical AI space have expressed concerns on the reliability of Explainable AI techniques and especially feature attribution methods, questioning their use and inclusion in guidelines and standards. Despite valid concerns, we argue that existing criticism on the viability of post-hoc local explainability me…
Metropolitan University（大都会大学）；University of Windsor（温莎大学）；Lakehead University（湖首大学）
Studies show that Studies that cardiovascular diseases (CVDs) are malignant for human health. Thus, it is important to have an efficient way of CVD prognosis. In response to this, the healthcare industry has adopted machine learning-based smart solutions to alleviate the manual process of CVD prognosis. Thus, this work proposes an information fusion technique that combines key attributes of a person through analysis of variance (ANOVA) and domain experts' knowledge. It also introduces a new collection of CVD data samples for emerging research. There are thirty-eight experiments conducted exhau…
National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Royal Brompton Hospital（皇家布朗普顿医院）；Imperial Institute of Advanced Technology（帝国理工高等研究院）；Hangzhou Ocean’s Smart Boya Co., Ltd（杭州海洋智能博雅有限公司）；University of California, San Diego（加州大学圣地亚哥分校）；Shenzhen Second People’s Hospital（深圳市第二人民医院）
Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made. This research field inspects the measures and models involved in decision-making and seeks solutions to explain them explicitly. Many of the machine learning algorithms can not manifest how and why a decision has been cast. This is particularly true of the most popular deep neural network approaches currently in use. Consequently, our confidence in AI systems can be hindered by the lack of explainability in these black-box models. The XAI bec…
Institute of Logic, Language, and Computation, University of Amsterdam（阿姆斯特丹大学逻辑、语言与计算研究所）；Pacmed（Pacmed公司）；Amsterdam Business School, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹商学院）
The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the debate around adoption of this technology. One thread of such debate concerns Explainable AI and its promise to render AI devices more transparent and trustworthy. A few voices active in the medical AI space have expressed concerns on the reliability of Explainable AI techniques, questioning their use and inclusion in guidelines and standards. Revisiting such criticisms, this article offers a balanced and comprehensive perspective on the utility of Explainable AI, focusing on the specificity of clin…
We developed an explainable artificial intelligence (AI) early warning score (xAI-EWS) system for early detection of acute critical illness. While maintaining a high predictive performance, our system explains to the clinician on which relevant electronic health records (EHRs) data the prediction is grounded. Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these parameters, such as Early Warning Scores …
A Comprehensive Review on Financial Explainable AI
Nanyang Technological University（南洋理工大学）；National University of Singapore（新加坡国立大学）；Asian Institute of Digital Finance at NUS（新加坡国立大学亚洲数字金融研究所）；Imperial College London（帝国理工学院）
Artificial Intelligence (AI) has become an integral part of domains such as security, finance, healthcare, medicine, and criminal justice. Explaining the decisions of AI systems in human terms is a key challenge--due to the high complexity of the model, as well as the potential implications on human interests, rights, and lives . While Explainable AI is an emerging field of research, there is no consensus on the definition, quantification, and formalization of explainability. In fact, the quantification of explainability is an open challenge. In our previous work, we incorporated domain knowle…
IBM Research AI（IBM研究院AI）；IBM Design（IBM设计）；IBM Research（IBM研究院）
A pervasive design issue of AI systems is their explainability--how to provide appropriate information to help users understand the AI. The technical field of explainable AI (XAI) has produced a rich toolbox of techniques. Designers are now tasked with the challenges of how to select the most suitable XAI techniques and translate them into UX solutions. Informed by our previous work studying design challenges around XAI UX, this work proposes a design process to tackle these challenges. We review our and related prior work to identify requirements that the process should fulfill, and according…
Carnegie Mellon University（卡内基梅隆大学）；Faculty of Engineering of the University of Porto（波尔图大学工程学院）；INESC TEC（葡萄牙国家工程与系统创新研究所科技中心）
Advances in data-driven deep learning for chest X-ray image analysis underscore the need for explainability, privacy, large datasets and significant computational resources. We frame privacy and explainability as a lossy single-image compression problem to reduce both computational and data requirements without training. For Cardiomegaly detection in chest X-ray images, we propose HeartSpot and four spatial bias priors. HeartSpot priors define how to sample pixels based on domain knowledge from medical literature and from machines. HeartSpot privatizes chest X-ray images by discarding up to 97…
The increasing availability of large collections of electronic health record (EHR) data and unprecedented technical advances in deep learning (DL) have sparked a surge of research interest in developing DL based clinical decision support systems for diagnosis, prognosis, and treatment. Despite the recognition of the value of deep learning in healthcare, impediments to further adoption in real healthcare settings remain due to the black-box nature of DL. Therefore, there is an emerging need for interpretable DL, which allows end users to evaluate the model decision making to know whether to acc…
University of Oxford（牛津大学）；University of Glasgow（格拉斯哥大学）
Predicting the incidence of complex chronic conditions such as heart failure is challenging. Deep learning models applied to rich electronic health records may improve prediction but remain unexplainable hampering their wider use in medical practice. We developed a novel Transformer deep-learning model for more accurate and yet explainable prediction of incident heart failure involving 100,071 patients from longitudinal linked electronic health records across the UK. On internal 5-fold cross validation and held-out external validation, our model achieved 0.93 and 0.93 area under the receiver o…
From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?
School of Computer Science and Electronic Engineering, University of Essex（埃塞克斯大学计算机科学与电子工程学院）；Harvard Medical School（哈佛医学院）；Boston Children’s Hospital（波士顿儿童医院）；University of Southampton（南安普顿大学）；Athena Research Centre（雅典娜研究中心）
Navigating Healthcare Insights: A Birds Eye View of Explainability with Knowledge Graphs
Hajim School of Engineering and Applied Sciences, University of Rochester（罗切斯特大学哈吉米工程与应用科学学院）；College of Engineering and Applied Sciences, University at Albany, State University of New York（纽约州立大学奥尔巴尼分校工程与应用科学学院）；Deloitte LLC（德勤有限责任公司）
Deciphering Heartbeat Signatures: A Vision Transformer Approach to Explainable Atrial Fibrillation Detection from ECG Signals
Walkky（沃基公司）；Chobanian & Avedisian School of Medicine, Boston University（波士顿大学乔巴尼亚与阿夫迪西安医学院）；Clemson University（克莱姆森大学）；Northwell Health Physician Partners（诺斯韦尔健康医师伙伴）；Lenox Hill Hospital（莱诺克斯山医院）
Explainable Artificial Intelligence For The Detection and Characterisation of Stage B Heart Failure
Division of Cardiovascular Sciences, University of Leicester（莱斯特大学心血管科学部）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）
Differential Privacy-Driven Framework for Enhancing Heart Disease Prediction
School of Computer Science and Technology（计算机科学与技术学院）；Algoma University（阿尔戈马大学）；School of Electrical Engineering and Computer Science（电气工程与计算机科学学院）；University of Ottawa（渥太华大学）
College of Computing and Information Technology, University of Bisha（比沙大学计算与信息技术学院）
Nowadays, heart disease is the leading cause of death worldwide. Predicting heart disease is a complex task since it requires experience along with advanced knowledge. Internet of Things (IoT) technology has lately been adopted in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Many researchers have focused on the diagnosis of heart disease, yet the accuracy of the diagnosis results is low. To address this issue, an IoT framework is proposed to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN). The smartwat…
