Collaborating Authors


Spotting Heart disease with AI - How far are we?


Cardiovascular Disease has long been the number one cause of death in the U.S. and some of the stats are startling: an American will have a heart attack approximately every 40 seconds for a total of 805,000 every year, At the same time, mortality and morbidity rates of CVD are increasing year by year, especially in developing regions. Studies have shown that approximately 80% of CVD-related deaths occur in low- and middle-income countries. Besides, these deaths occur at a younger age than in high-income countries. CVD represents a significant economic cost for society, around $351.2 billion in the US, chronically affecting patients' quality of life. The EU has estimated that the overall yearly cost amounts to €210 billion, allocating around 53% to healthcare costs (€111 billion), with 26% related to productivity losses (€54 billion), and the remaining 21% (€45 billion) to the informal care of people with CVD (European Cardiovascular Disease Statistics 2017).

Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects Artificial Intelligence

The significant increase in the number of individuals with chronic ailments (including the elderly and disabled) has dictated an urgent need for an innovative model for healthcare systems. The evolved model will be more personalized and less reliant on traditional brick-and-mortar healthcare institutions such as hospitals, nursing homes, and long-term healthcare centers. The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies, especially in artificial intelligence (AI) and machine learning (ML). This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment. Additionally, the paper demonstrates software integration architectures that are very significant to create smart healthcare systems, integrating seamlessly the benefit of data analytics and other tools of AI. The explained developed systems focus on several facets: the contribution of each developed framework, the detailed working procedure, the performance as outcomes, and the comparative merits and limitations. The current research challenges with potential future directions are addressed to highlight the drawbacks of existing systems and the possible methods to introduce novel frameworks, respectively. This review aims at providing comprehensive insights into the recent developments of smart healthcare systems to equip experts to contribute to the field.

Artificial intelligence continues to evolve in cardiology


Artificial intelligence continues to affect cardiology with improved capabilities to diagnose certain conditions such as atrial fibrillation, and research is underway to learn more about its use in disease management, a presenter said. Although ECG watches were patented in the early 1990s, smartwatches of today are different because of lower manufacturing costs, changes in the regulatory landscape, AI and smartphone-based data transfers, Mintu P. Turakhia, MD, MAS, associate professor and executive director of the Center for Digital Health at Stanford University School of Medicine and a Cardiology Today Next Gen Innovator, said in his presentation at the Scientific Session and Exhibition of the American Society of Nuclear Cardiology. The use of wearables today has increased due to more people having smartphones, with 81% of the population worldwide owning a smartphone. Approximately 20% of people in the U.S. now own a consumer wearable, which is increasing annually, according to the presentation. People often track several metrics using consumer wearables, including heart rate, BP and blood glucose.

Secure and Robust Machine Learning for Healthcare: A Survey Machine Learning

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research.

Medication Regimen Extraction From Clinical Conversations Machine Learning

Extracting relevant information from clinical conversations and providing it to doctors and patients might help in addressing doctor burnout and patient forgetfulness. In this paper, we focus on extracting the Medication Regimen (dosage and frequency for medications) discussed in a clinical conversation. We frame the problem as a Question Answering (QA) task and perform comparative analysis over: a QA approach, a new combined QA and Information Extraction approach and other baselines. We use a small corpus of 6,692 annotated doctor-patient conversations for the task. Clinical conversation corpora are costly to create, difficult to handle (because of data privacy concerns), and thus `scarce'. We address this data scarcity challenge through data augmentation methods, using publicly available embeddings and pretrain part of the network on a related task of summarization to improve the model's performance. Compared to the baseline, our best-performing models improve the dosage and frequency extractions' ROUGE-1 F1 scores from 54.28 and 37.13 to 89.57 and 45.94, respectively. Using our best-performing model, we present the first fully automated system that can extract Medication Regimen (MR) tags from spontaneous doctor-patient conversations with about ~71% accuracy.

An Inside Look at Apple's Biggest Step Yet in Health Care

TIME - Tech

Captain America and Black Panther were about to defend Earth from the villain Thanos when Kevin Foley first noticed something was wrong. Foley, a 46-year-old information-technology worker from Kyle, Texas, was heading into the theater to see Avengers: Infinity War when he realized he was having trouble breathing normally. The sensation struck again during another movie the following night, but more severe this time. Once the credits on the second film rolled, Foley took action: he looked at his wristwatch. It was a bigger step than you might imagine, because Foley was wearing an Apple Watch equipped with medical sensors and experimental software to track basic functions of his heart. And the watch was worried.