Infections and Infectious Diseases

How Data and Artificial Intelligence Can Help Improve Healthcare


Unlocking patterns and trends in medical data will be integral to solving healthcare's most pressing problems in predictive and preventative care. In 1854, Dr. John Snow mapped hundreds of homes in London to determine that the cholera outbreak originated at a single water pump. Dr. Snow's work was the birth of modern medical data analysis. Coupling those analytics with artificial intelligence, wearable medical devices, cloud computing, and aggregated and integrated patient data will soon provide you with real-time knowledge to improve patient care and outcomes at the individual and macro-levels of society. Physicians at the University of Miami Health System are already using predictive analytics software that scans and interprets thousands of patients' medical records to uncover trends.

Could artificial intelligence prevent sepsis in hospital patients? Sentara thinks so.


During your stay in a hospital, computer systems are collecting and analyzing all sorts of data about you. In the background of all the beeping and gadgetry, an electronic medical record contains thousands of bits of information about your medical history, vital signs and laboratory results. Sentara Healthcare is now deploying artificial intelligence to use that data to stop patients from contracting life-threatening sepsis. Earlier this year the system launched a sepsis prediction tool that alerts doctors and nurses when a patient is at risk of developing the deadly infection. The tool "looks at relationships in order to predict what might happen in the future," said Dr. David Mohr, Sentara's vice president of clinical informatics and transformation.

Top 5 Deep Learning and AI Stories - August 2, 2019


Artificial Intelligence for Good – Also Makes Business Sense 2. 3 Ways You Can Use Artificial Intelligence to Grow Your Business Right Now 3. Tangible Use Cases Are Key to AI Adoption 4. Why Genuine Human Intelligence is Key for the Development of AI 5. NVIDIA GPUs Powering the Most Energy Efficient Supercomputers in the World 4. 4 ARTIFICIAL INTELLIGENCE FOR GOOD – ALSO MAKES BUSINESS SENSE Artificial intelligence is being used as a potential solution to many of the world's most pressing problems, from poverty to hunger. "Another aimed the opioid epidemic currently plaguing the US, by harnessing machine learning to determine which patients were more likely to become addicts after being prescribed opioid treatments. " "Other initiatives include driving data-driven research into multiple sclerosis, developing AI-driven systems to assist those on low incomes with managing their finances, assisting the UN in driving its sustainable development goals and predict outbreaks of Zika virus." READ ARTICLE 5. 5 3 WAYS YOU CAN USE ARTIFICIAL INTELLIGENCE TO GROW YOUR BUSINESS RIGHT NOW Utilizing AI in your business marketing is more cost- effective and simpler to implement than you think. "Finding a logical starting point for bringing artificial intelligence integration into your marketing strategy is intimidating, no matter what your budget looks like. However, for the SMB and startup entrepreneur without the wiggle room to experiment, getting started often feels incredibly risky or out of reach. But we're reaching a point where it's becoming a necessity for any brand looking to stay competitive."

Machine Learning for Rapid Diagnosis of Antimicrobial Resistance in I Streptococcus pneumoniae /I ---Chinese Academy of Sciences


Streptococcus pneumoniae is the most common human respiratory pathogen, and β-lactam antibiotics have been employed to treat infections caused by S. pneumoniae for decades. However, the high variability of PBPs in clinical isolates and their mosaic gene structure hamper the predication of resistance level according to the PBP gene sequences. A research group led by Prof. FENG Jie at Institute of Microbiology of the Chinese Academy of Sciences developed a systematic strategy for applying supervised machine learning (SL) to predict antimicrobial susceptibility testing (AST) of β-lactam antibiotic resistance. The study was published in Briefings in Bioinformatics. The published PBP sequences with minimum inhibitory concentration (MIC) values and the sequences from NCBI database without MIC values were served as labelled data and unlabeled data, respectively.

Artificial Intelligence For Good - Also Makes Business Sense


Artificial Intelligence (AI) has been put forward as a potential solution for many of the gravest problems facing society, from the opioid crisis to poverty and famine. But although technology clearly has the potential to do a great deal of good, there's a sound business reason that tech companies often pour large amounts of resources into social projects that don't seem to align with their core business of selling software and services. This is down to the fact that tackling social issues often involves developing solutions to problems very similar to those faced by businesses. Additionally, working with governments or NGOs on building these solutions can often mean access to new datasets. Learning derived from these datasets can later be developed into products and services to offer to clients (even if the data itself isn't).

Ava of 'Ex Machina' Is Just Sci-Fi (for Now) techsocialnetwork


Are technology companies running too fast into the future and creating things that could potentially wreak havoc on humankind? That question has been swirling around in my head ever since I saw the enthralling science-fiction film "Ex Machina." The movie offers a clever version of the robots versus humans narrative. But what makes "Ex Machina" different from the usual special-effects blockbuster is the ethical questions it poses. Foremost among them is something that most techies don't seem to want to answer: Who is making sure that all of this innovation does not go drastically wrong?

AI protein-folding algorithms solve structures faster than ever


Predicting protein structures from their sequences would aid drug design.Credit: Edward Kinsman/Science Photo Library The race to crack one of biology's grandest challenges -- predicting the 3D structures of proteins from their amino-acid sequences -- is intensifying, thanks to new artificial-intelligence (AI) approaches. At the end of last year, Google's AI firm DeepMind debuted an algorithm called AlphaFold, which combined two techniques that were emerging in the field and beat established contenders in a competition on protein-structure prediction by a surprising margin. And in April this year, a US researcher revealed an algorithm that uses a totally different approach. He claims his AI is up to one million times faster at predicting structures than DeepMind's, although probably not as accurate in all situations. More broadly, biologists are wondering how else deep learning -- the AI technique used by both approaches -- might be applied to the prediction of protein arrangements, which ultimately dictate a protein's function.

Worrying About Artificial Intelligence Starting a Nuclear War: Eye on A.I.


An organization that won the Nobel Prize in 2017 for its work to eliminate nuclear weapons is sounding the alarm about the possibility of artificial intelligence leading to unintended wars. Beatrice Fihn, executive director of the International Campaign to Abolish Nuclear Weapons, is worried that hackers could breach A.I. technologies that are used in nuclear programs or that they could use A.I. to dupe countries into launching attacks. For example, deepfakes, or realistic-looking computer-altered videos, may be used to "create a perceived threat that might not be there," she warns, prompting governments to overreact. Fihn told Fortune that she wants to convene a meeting in the fall with nuclear weapons experts and some of the leading companies in A.I. and cybersecurity. Participants in the off-the-record event, she said, would produce a document that her group would use to inform governments and others about the danger.

Machine learning approach significantly expands inovirus diversity


To answer the question, "Where's Waldo?" readers need to look for a number of distinguishing features. Several characters may be spotted with a striped scarf, striped hat, round-rimmed glasses, or a cane, but only Waldo will have all of these features. As described July 22, 2019, in Nature Microbiology, a team led by scientists at the U.S. Department of Energy (DOE) Joint Genome Institute (JGI), a DOE Office of Science User Facility, developed an algorithm that a computer could use to conduct a similar type of search in microbial and metagenomic databases. In this case, the machine "learned" to identify a certain type of bacterial viruses or phages called inoviruses, which are filamentous viruses with small, single-stranded DNA genomes and a unique chronic infection cycle. "We're not sure why we systematically manage to miss them; maybe it's due to the way we currently isolate and extract viruses," said the study's lead author Simon Roux, a JGI research scientist in the Environmental Genomics group.

Learning Multimorbidity Patterns from Electronic Health Records Using Non-negative Matrix Factorisation Machine Learning

Multimorbidity, or the presence of several medical conditions in the same individual, have been increasing in the population both in absolute and relative terms. However, multimorbidity remains poorly understood, and the evidence from existing research to describe its burden, determinants and consequences have been limited. Many of these studies are often cross-sectional and do not explicitly account for multimorbidity patterns' evolution over time. Some studies were based on small datasets, used arbitrary or narrow age range, or lacked appropriate clinical validations. In this study, we applied Non-negative Matrix Factorisation (NMF) in a novel way to one of the largest electronic health records (EHR) databases in the world (with 4 million patients), for simultaneously modelling disease clusters and their role in one's multimorbidity over time. Furthermore, we demonstrated how the temporal characteristics that our model associates with each disease cluster can help mine disease trajectories/networks and generate new hypotheses for the formation of multimorbidity clusters as a function of time/ageing. Our results suggest that our method's ability to learn the underlying dynamics of diseases can provide the field with a novel data-driven / exploratory way of learning the patterns of multimorbidity and their interactions over time.