Four ways to scale up solutions in Artificial Intelligence for health


At least half of the world's population cannot obtain essential health services. But low-cost, easy-to-use technologies powered by Artificial Intelligence (AI) promise to deliver quality and affordable health care to the people who need it most, no matter how hard to reach. At the AI for Good Global Summit last week, entrepreneurs, AI experts, academics and UN representatives described many AI technologies for health, allowing for the early detection of various pathologies such as osteoarthritis, diabetic retinopathy, child malnutrition, snakebites and others. These technologies don't place a heavy burden on doctors, and can lead to prompt diagnosis and effective treatment. They agreed that AI can add tremendous value in developing countries where there is a low density of physicians.

The Laser Battle Against Blood-Sucking Parasites of the Deep


Back and forth he walks across the polished hardwood floors of a barge anchored in a fjord off the southwestern coast of Norway. The barge sits alongside one of the world's largest salmon farms. It's November and the sky is cloudless, the mountains are snow-capped, the water is a clear sapphire blue. The control room has the feel of a W Hotel lobby with its elegant lighting and spare Scandinavian design. On one wall are huge monitors streaming video from nine underwater cages nearby. Aarskog scans the footage--masses of salmon swimming in circles like glittering cyclones--and mutters what I take to be Norwegian profanities.

From The Olympics To A Unicorn: How 'Cyber Immune System' Darktrace Hit A $1.3BN Valuation

Forbes Technology

Darktrace has become a rarity in the British tech scene: a Unicorn. The cybersecurity company, which provides what it calls a network "immune system" powered by artificial intelligence, hit a $1.25 billion valuation last month, two sources close to the deal told Forbes. The new valuation came after a secondary round of financing in which former investors sold off their stakes. Vitruvian Partners, sources said, is the acquirer of the stock. How did Darktrace find itself at the vaunted Unicorn status?

Drone Tests in Reno Focus on Emergency Medical Supplies

U.S. News

Flirtey drones already have delivered automated external defibrillators used to jumpstart the hearts of cardiac arrest victims as part of a joint emergency program with first-responders in Reno. The company also anticipates future deliveries of EpiPens for severe allergic reactions and Narcan for opioid overdoses.

AI detects patterns of gut microbes for cholera risk: A hundred kinds of microbes out of 4,000 determine susceptibility to cholera


"These are patterns that even the most sophisticated scientist couldn't detect by eye," said Lawrence A. David, Ph.D., a senior author of the study and assistant professor of molecular genetics and microbiology at Duke School of Medicine. "While some people are warning about artificial intelligence leading to killer robots, we are showing the positive impact of AI in its potential to overcome disease." The research, published this week in the Journal of Infectious Diseases, suggests that a focus on gut microbes may be important for developing improved vaccines and preventive approaches for cholera and other infectious diseases. "Our study found that this'predictive microbiota' is as good at predicting who gets ill with cholera as the clinical risk factors that we've known about for decades," said Regina C. LaRocque, M.D., MPH, of the Massachusetts General Hospital Division of Infectious Diseases, a senior author of the study and assistant professor of medicine at Harvard Medical School. "We've essentially identified a whole new component of cholera risk that we did not know about before."

Modeling Dengue Vector Population Using Remotely Sensed Data and Machine Learning Machine Learning

Mosquitoes are vectors of many human diseases. In particular, Aedes \ae gypti (Linnaeus) is the main vector for Chikungunya, Dengue, and Zika viruses in Latin America and it represents a global threat. Public health policies that aim at combating this vector require dependable and timely information, which is usually expensive to obtain with field campaigns. For this reason, several efforts have been done to use remote sensing due to its reduced cost. The present work includes the temporal modeling of the oviposition activity (measured weekly on 50 ovitraps in a north Argentinean city) of Aedes \ae gypti (Linnaeus), based on time series of data extracted from operational earth observation satellite images. We use are NDVI, NDWI, LST night, LST day and TRMM-GPM rain from 2012 to 2016 as predictive variables. In contrast to previous works which use linear models, we employ Machine Learning techniques using completely accessible open source toolkits. These models have the advantages of being non-parametric and capable of describing nonlinear relationships between variables. Specifically, in addition to two linear approaches, we assess a Support Vector Machine, an Artificial Neural Networks, a K-nearest neighbors and a Decision Tree Regressor. Considerations are made on parameter tuning and the validation and training approach. The results are compared to linear models used in previous works with similar data sets for generating temporal predictive models. These new tools perform better than linear approaches, in particular Nearest Neighbor Regression (KNNR) performs the best. These results provide better alternatives to be implemented operatively on the Argentine geospatial Risk system that is running since 2012.

Investment in artificial intelligence is essential for our future health


Artificial intelligence may still be in its infancy, but it's moving fast. Nowhere is this more apparent than in the data-rich health sector. AI has the potential to provide more precise, personalised care, as well as help us to shift our focus from treatment to prevention and tackle some of the world's biggest global health issues. The WHO estimates that achieving the health-related targets under the Sustainable Development Goals – from ending tuberculosis to ensuring universal access to sexual and reproductive healthcare services by 2030 – will cost between $134bn-$371bn (£97bn-£270bn) a year over current health spending. AI startups raised $15.2bn last year alone, adding to investments made by tech giants like Google, Facebook, and Alibaba and a host of research institutions.

Revealing patterns in HIV viral load data and classifying patients via a novel machine learning cluster summarization method Machine Learning

HIV RNA viral load (VL) is an important outcome variable in studies of HIV infected persons. There exists only a handful of methods which classify patients by viral load patterns. Most methods place limits on the use of viral load measurements, are often specific to a particular study design, and do not account for complex, temporal variation. To address this issue, we propose a set of four unambiguous computable characteristics (features) of time-varying HIV viral load patterns, along with a novel centroid-based classification algorithm, which we use to classify a population of 1,576 HIV positive clinic patients into one of five different viral load patterns (clusters) often found in the literature: durably suppressed viral load (DSVL), sustained low viral load (SLVL), sustained high viral load (SHVL), high viral load suppression (HVLS), and rebounding viral load (RVL). The centroid algorithm summarizes these clusters in terms of their centroids and radii. We show that this allows new viral load patterns to be assigned pattern membership based on the distance from the centroid relative to its radius, which we term radial normalization classification. This method has the benefit of providing an objective and quantitative method to assign viral load pattern membership with a concise and interpretable model that aids clinical decision making. This method also facilitates meta-analyses by providing computably distinct HIV categories. Finally we propose that this novel centroid algorithm could also be useful in the areas of cluster comparison for outcomes research and data reduction in machine learning.

Predicting C. Diff Risk with Big Data and Machine Learning


A new model analyzes a wealth of information to better predict which patients are more prone to the dangerous infection. Nearly 30,000 Americans die each year from an aggressive, gut-infecting bacteria called Clostridium difficile. Resistant to many common antibiotics, C. diff can flourish when antibiotic treatment kills off beneficial bacteria that normally keep the deadly infection at bay. But doctors often struggle to determine when to take preventive action. New machine learning models tailored to individual hospitals could offer a much earlier prediction of which patients are most likely to develop C. diff, potentially helping stave off infection before it starts.