This demo presents SeizNet, an innovative system for predicting epileptic seizures benefiting from a multi-modal sensor network and utilizing Deep Learning (DL) techniques. Epilepsy affects approximately 65 million people worldwide, many of whom experience drug-resistant seizures. SeizNet aims at providing highly accurate alerts, allowing individuals to take preventive measures without being disturbed by false alarms. SeizNet uses a combination of data collected through either invasive (intracranial electroencephalogram (iEEG)) or non-invasive (electroencephalogram (EEG) and electrocardiogram (ECG)) sensors, and processed by advanced DL algorithms that are optimized for real-time inference at the edge, ensuring privacy and minimizing data transmission. SeizNet achieves > 97% accuracy in seizure prediction while keeping the size and energy restrictions of an implantable device.

Artificial Intelligence-based (AI) analysis of large, curated medical datasets is promising for providing early detection, faster diagnosis, and more effective treatment using low-power Electrocardiography (ECG) monitoring devices information. However, accessing sensitive medical data from diverse sources is highly restricted since improper use, unsafe storage, or data leakage could violate a person's privacy. This work uses a Federated Learning (FL) privacy-preserving methodology to train AI models over heterogeneous sets of high-definition ECG from 12-lead sensor arrays collected from six heterogeneous sources. We evaluated the capacity of the resulting models to achieve equivalent performance compared to state-of-the-art models trained in a Centralized Learning (CL) fashion. Moreover, we assessed the performance of our solution over Independent and Identical distributed (IID) and Non-IID federated data. Our methodology involves machine learning techniques based on Deep Neural Networks and Long-Short-Term Memory models. It has a robust data preprocessing pipeline with feature engineering, selection, and data balancing techniques. Our AI models demonstrated comparable performance to models trained using CL, IID, and Non-IID approaches. They showcased advantages in reduced complexity and faster training time, making them well-suited for cloud-edge architectures.

It's January 17, 2020-- the world has yet to change; Wuhan locks down six days later -- and Emil Jimenez is on a train from Vienna to Prague. "She's like, 'Daddy,' y'know, 'what is this?'" Jimenez tells me on a video call from the Czech Republic. Jimenez tells her it's Siri, and encourages her to talk to the digital assistant. Her first question is if Siri has a mother. From there, she peppers the artificial intelligence with the kinds of questions kids ask -- do you like ice cream?

Andrew Bird is the data and analytics manager for Vesparum Capital. He leads the software and data science teams at Vesparum, overseeing full stack web development in Django / React. He is an Australian actuary (FIAA, CERA), who has previously worked with Deloitte Consulting in financial services. Andrew also currently works as a full-stack developer for Draftable Pvt. Ltd. He manages ongoing development of the donation portal for Effective Altruism Australia website, on a voluntary basis.

The situation called for a new approach. They needed information services that would help them decide what varieties to plant, when they should sow and how they should manage their crops. A consortium formed with the government, Colombia's National Cereals and Legumes Federation (FENALCE), and big-data scientists at the International Center for Tropical Agriculture (CIAT). The researchers used big-data tools, based on the data farmers helped collect, and yields increased substantially. The study, published in September in Global Food Security, shows how machine learning of data from multiple sources can help make farming more efficient and productive even as the climate changes. "Today we can collect massive amounts of data, but you can't just bulk it, process it in a machine and make a decision," said Daniel Jimenez, a data scientist at CIAT and the study's lead author.

What does the youngest chief executive in Big Pharma want? When Vas Narasimhan, 41, took the helm of drug giant Novartis in February, he'd already put the project in motion. Novartis scouts were dispatched to visit air traffic control towers and the Swiss electrical grid to see how other industries dealt with torrents of data. Working with McKinsey's QuantumBlack unit, they built a software system called Nerve that not only keeps track of every data point on all 550 clinical trials testing Novartis drugs, but also uses analytic software to predict potential hiccups in the execution of those studies. Soon Narasimhan will be able to walk into mission control at the company's Basel, Switzerland, headquarters and call up whatever information he needs in an instant. "When you look at history, it takes the medical establishment 50 to 75 years to actually change how we do clinical studies," Narasimhan says.

Navid Alipour, a co-founder and managing partner of San Diego's Analytics Ventures, said his firm's portfolio company CureMatch is taking a direct-to-consumer approach, in which cancer patients pay CureMatch to recommend the top three combinations of chemotherapy drugs for each patient's cancer. The recommendations, based on information in a patient's own medical record, is intended to help cancer specialists choose a treatment regimen. CureMatch says it uses supercomputer processing to sort through millions of possible three-drug combinations, assessing each combination for factors like unwanted drug-drug interactions, and correlating genomic data to rank the best drug combinations for a specific patient. CureMetrix, another company in Analytics Ventures' portfolio, uses machine learning to analyze mammography images for breast cancer--and must still get FDA approval before it can be used in the United States, Alipour said. "It will be a [software as a service] model," Alipour said.

That would mean combining the stream of data from glucose monitoring, insulin measurements, patient activity and meals, and applying machine learning to derive insights so the software can send alerts and recommendations back to patients and their doctors, she said. "But where we are in our maturity as an industry is just publishing numbers," "So we're just telling people what their glucose number is, which is critical for a type 1 diabetic. But a type 2 diabetic needs to engage with an app, and be compelled to interact with the insights. The ultimate goal, perhaps, would be to develop a user interface that uses the insights gained from machine learning to actually prompt diabetic patients to change their behavior. This point was echoed by Jean Balgrosky, an investor who spent 20 years as the CIO of large, complex healthcare organizations such as San Diego's Scripps Health.

This story is part of an ongoing Xconomy series on A.I. in healthcare. These are heady times for using artificial intelligence to extract insights from healthcare data--in particular, from the tidal wave of information coming out of fields like genomics and medical imaging. Yet as innovations proliferate, some age-old business questions have come to the fore. How can startups make money in this emerging field? How can healthcare companies use AI to "bend the curve" of increasing healthcare costs? And, ultimately, how can they get buy-in from government regulators, insurers, doctors, and patients?