ReSpeaker has released its 4-Microphone Raspberry Pi HAT (Hardware Attached on Top), a quad-microphone expansion board for Raspberry Pi which has been designed for AI assistant and voice applications. The board can be used in robotics or in smart homes, IoT scenarios, and conference rooms, and is customisable as you add new modules. The Grove system uses a modular, building block approach to building electronics systems with buttons sensors such as motion detectors or heart rate sensors. It is compatible with and will support Raspberry Pi Zero and Zero W, Raspberry Pi B, Raspberry Pi 2 B and Raspberry Pi 3 B.
A classifier is a tool in data mining that takes a bunch of data representing things we want to classify and attempts to predict which class the new data belongs to. Orange, an open-source data visualization and analysis tool for data mining, implements C4.5 in their decision tree classifier. Support vector machine (SVM) learns a hyperplane to classify data into 2 classes. The balls represent data points, and the red and blue color represent 2 classes.
Compared to the established risk prediction algorithm, all four algorithms improved overall prediction accuracy, from 1.7% to 3.6%, as determined by a metric called the'Area Under the Receiver Operating Characteristic curve'. The highest achieving algorithm (neural networks) correctly predicted 7.6% more patients who eventually developed cardiovascular disease compared to the standard algorithm." The study concludes that the improved predictions offered by self-teaching algorithms are better at predicting the absolute number of cardiovascular disease cases correctly, while successfully excluding non-cases. It is also one of the most popular universities in the UK among graduate employers and was named University of the Year for Graduate Employment in the 2017 The Times and The Sunday Times Good University Guide.
Given the obvious possible benefits, it's well worth exploring the opportunities machine learning has to offer – including to clinical trials. They state: "We can build models with the platform using the patient's own biology in order to stratify the population by response to the trial drug as well as monitor patient response over time at a biological level, which may lead to more successful trials." Data on key risk factors, such as smoking status and blood pressure, was used to develop and test four different machine learning algorithms for predicting cardiovascular risk. The report suggests that "these algorithms were better than existing medical risk models at both predicting the number of people who would develop cardiovascular disease and excluding people who would not get heart problems."
And to spot new events such as supernovas or pulsars, scientists use automated surveys to scan the sky day in and day out. So how does this kind of machine learning work, for instance, in astronomy? Traditionally, people would then look at those plots and determine what is and isn't a promising candidate, said Ryan Lynch, an astronomer at National Radio Astronomy Observatory who uses machine learning to find pulsars. "The decision tree algorithm works by looking at the features and attempt to find the best feature to separate the data that was given into branches.
Methods and results: Using data from the National Cardiovascular Data Registry for implantable cardioverter–defibrillators (ICDs) linked to Medicare administrative claims for longitudinal follow-up, we applied three statistical approaches to safety-signal detection for commonly used dual-chamber ICDs that used two propensity score (PS) models: one specified by subject-matter experts (PS-SME), and the other one by machine learning-based selection (PS-ML). Cumulative device-specific unadjusted 3-year event rates varied for three surveyed safety signals: death from any cause, 12.8%–20.9%; Conclusion: Three statistical approaches, including one machine learning method, identified important safety signals, but without exact agreement. This work is published and licensed by Dove Medical Press Limited. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.
Verily and its sister company Google Research are developing an AI-powered test for heart disease that analyses retinal imagery. "Our results indicate that deep learning of retinal fundus images alone can predict multiple cardiovascular risk factors, including as age, gender, and systolic blood pressure," write the study authors. "That these risk factors are core components used in multiple cardiovascular risk calculators indicates that our model can potentially predict cardiovascular risk directly." A significantly larger dataset or a population with more cardiovascular events may enable more accurate deep learning models to be trained and evaluated with high confidence."
The artificial intelligence technology was developed by Dr. Xiaowei Ding. The Chinese based researcher formed the company VoxelCloud to develop technology can can read medical images and provide diagnostic insights. The VoxelCloud example is with the construction of systems based on artificial intelligence, together with cloud computing technology, and machine learning. The technology developed to date by VoxelCloud is aimed at pulmonary disease, screening for cancer (using a liquid biopsy imaging solution, aimed at early cancer screening), and assessing coronary artery disease.
New research from Alphabet's life science company, Verily, and Google shows that one day there might be a far easier test to determine risk of heart disease. Doctors today rely heavily on blood tests to determine risk of heart disease; a potential test based on retinal images would be less invasive, easier to obtain, and faster to analyze with AI. While mainstream medicine has found a number of signals that indicate heart disease on retinal images, the Google and Verily algorithm was able to find indicators of age, gender, whether the patient smoked, blood pressure, and information about the level of sugar in a person's blood on its own. It's worth noting again that the paper hasn't been reviewed or accepted by any accredited journal, and Alphabet stands to benefit greatly if it can develop new software for indicating heart disease risk without needing to take a blood sample.
An online pharmacy is planning to use drones to deliver the morning-after pill and Viagra following successful UK trials. But in the trial, the emergency contraception pill was carried at below 25C by a drone and successfully delivered in Broadstairs, Kent. We're considering making drone delivery part of our future service and are in talks to work out how we can do this. A high street retailer has become the first to launch a generic emergency hormonal contraceptive pill (EHC) at half the price of branded versions – prompting concerns it could result in a rise in ectopic pregnancies and STDs.