Budapest-based startup HeartBit is looking to refresh the fitness wearables category with an electrocardiogram (ECG) device created in partnership with IBM. Instead, it's a system of five sensors spanning three leads and embedded in a silicone chest strap. Each sensor gathers electrical signal data generated by a heartbeat at a rate of 2,000Hz per electrode. That adds up for a total of 10,000 data points per second, compared to the standard measurements taken by other wearables at around two to three data points per second. That makes HeartBit easily capable of being the most comprehensive heart-measuring wearable ever created.
UK Prime Minister Theresa May has announced plans to invest in a "whole new industry around AI in healthcare". Researchers at the University of Southern California have developed a new predictive model for heart disease, which makes use of a smartphone app. Machine-learning techniques are poised to hit the mainstream over the next few years. Machine learning has long been touted as the next big thing for healthcare. With countless startups investing in that promise, applications are emerging across everything from diagnostics to drug discovery.
Artificial intelligence, or AI, is largely an experimental science—at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments.
The complete book in a single file.
At this moment, databanks worldwide contain brain images of previously unimaginable numbers. Combined with developments in data science, these massive data provide the potential to better understand the genetic underpinnings of brain diseases. However, different datasets, which are stored at different institutions, cannot always be shared directly due to privacy and legal concerns, thus limiting the full exploitation of big data in the study of brain disorders. Here we propose a federated learning framework for securely accessing and meta-analyzing any biomedical data without sharing individual information. We illustrate our framework by investigating brain structural relationships across diseases and clinical cohorts. The framework is first tested on synthetic data and then applied to multi-centric, multi-database studies including ADNI, PPMI, MIRIAD and UK Biobank, showing the potential of the approach for further applications in distributed analysis of multi-centric cohorts