infection


An AI expert's toughest project: writing code to save his son's life - STAT

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Cristina Might drew close to her son. He was listless and groggy after weeks of battling a puzzling illness that had filled his lungs with fluid and, hours earlier, stopped his breathing entirely. A code team had rushed to Buddy's bedside and jolted him back to life, but now the 11-year-old with the broad smile was gray, his eyes unable to focus. His mom leaned nearer still. It was time to say goodbye. But Cristina's words to her son, a brown-eyed boy who loved dolphins and his aquarium, offered no hint of her desperation: "I was telling him it was all going to be OK, that his fishies couldn't wait to see him again and that he had to hurry up and come home." Somehow, Buddy made it through that night this past May, allowing doctors at Children's Hospital of Alabama to insert a tube to drain his lungs. His illness had caused a frightening cascade of symptoms: a yellowish substance in his bones and a bulging abdomen, on top of the deluge of fluid.


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

arXiv.org 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.


Time series cluster kernels to exploit informative missingness and incomplete label information

arXiv.org Machine Learning

The time series cluster kernel (TCK) provides a powerful tool for analysing multivariate time series subject to missing data. TCK is designed using an ensemble learning approach in which Bayesian mixture models form the base models. Because of the Bayesian approach, TCK can naturally deal with missing values without resorting to imputation and the ensemble strategy ensures robustness to hyperparameters, making it particularly well suited for unsupervised learning. However, TCK assumes missing at random and that the underlying missingness mechanism is ignorable, i.e. uninformative, an assumption that does not hold in many real-world applications, such as e.g. medicine. To overcome this limitation, we present a kernel capable of exploiting the potentially rich information in the missing values and patterns, as well as the information from the observed data. In our approach, we create a representation of the missing pattern, which is incorporated into mixed mode mixture models in such a way that the information provided by the missing patterns is effectively exploited. Moreover, we also propose a semi-supervised kernel, capable of taking advantage of incomplete label information to learn more accurate similarities. Experiments on benchmark data, as well as a real-world case study of patients described by longitudinal electronic health record data who potentially suffer from hospital-acquired infections, demonstrate the effectiveness of the proposed methods.


Characterization of Overlap in Observational Studies

arXiv.org Machine Learning

Overlap between treatment groups is required for nonparametric estimation of causal effects. If a subgroup of subjects always receives (or never receives) a given intervention, we cannot estimate the effect of intervention changes on that subgroup without further assumptions. When overlap does not hold globally, characterizing local regions of overlap can inform the relevance of any causal conclusions for new subjects, and can help guide additional data collection. To have impact, these descriptions must be interpretable for downstream users who are not machine learning experts, such as clinicians. We formalize overlap estimation as a problem of finding minimum volume sets and give a method to solve it by reduction to binary classification with Boolean rules. We also generalize our method to estimate overlap in off-policy policy evaluation. Using data from real-world applications, we demonstrate that these rules have comparable accuracy to black-box estimators while maintaining a simple description. In one case study, we perform a user study with clinicians to evaluate rules learned to describe treatment group overlap in post-surgical opioid prescriptions. In another, we estimate overlap in policy evaluation of antibiotic prescription for urinary tract infections.


Deep learning approach to description and classification of fungi microscopic images

arXiv.org Artificial Intelligence

Diagnosis of fungal infections can rely on microscopic examination, however, in many cases, it does not allow unambiguous identification of the species due to their visual similarity. Therefore, it is usually necessary to use additional biochemical tests. That involves additional costs and extends the identification process up to 10 days. Such a delay in the implementation of targeted treatment is grave in consequences as the mortality rate for immunosuppressed patients is high. In this paper, we apply machine learning approach based on deep learning and bag-of-words to classify microscopic images of various fungi species. Our approach makes the last stage of biochemical identification redundant, shortening the identification process by 2-3 days and reducing the cost of the diagnostic examination.


Artificial intelligence helps to treat tuberculosis more effectively - Medical News Bulletin Health News and Medical Research

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The spread of tuberculosis (TB) has diminished in the developed world, but it is still prevalent in the developing parts of the world such as in Asia and Africa. The rise of HIV in the 1980's also saw an increase in TB infections due to the weakened immune systems of patients with HIV. Currently about 1.6 million people die from TB each year, and 10 million people develop active TB infections, which is also contagious. Tuberculosis is caused by Mycobacterium tuberculosis bacteria and it generally affects the lungs. Individuals can harbor the TB bacteria but show no symptoms.


Over million new cases daily: WHO alarmed at STD spread in era of dating apps

The Japan Times

GENEVA - The World Health Organization expressed alarm Thursday at the lack of progress on curbing sexually transmitted diseases, while one of its experts warned of complacency as dating apps are spurring sexual activity. The U.N. health agency said in a fresh report that every day globally there were more than 1 million new cases of treatable sexually transmitted diseases (STD) or infections (STI). WHO found that there were more than 376 million new cases of chlamydia, gonorrhea, trichomoniasis and syphilis registered around the world in 2016 -- the latest year for which data is available. That is basically the same number as WHO reported in its previous study, based on data from 2012. A WHO expert on sexually transmitted infections, Teodora Wi, separately told journalists there were concerns that condom use may be declining as people have lost their fear of contracting HIV in step with the emergence of available and effective antiviral treatments.


Problems With Anti-Virus Software and Alternative Solutions United States Cybersecurity Magazine

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Anti-Virus software is the layman's solution to cybersecurity. Functioning as a first line of defense, Anti-Virus software works to prevent, detect, and remove malware from your computer. However, Anti-Virus software is not a cure all solution. In fact, IMB Knowledge Center published a piece on the limitations of Anti-Virus protection. In the article, they cite file size, scan time, and nesting depth as a few of the limitations.


Problems With Anti-Virus Software and Alternative Solutions United States Cybersecurity Magazine

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Anti-Virus software is the layman's solution to cybersecurity. Functioning as a first line of defense, Anti-Virus software works to prevent, detect, and remove malware from your computer. However, Anti-Virus software is not a cure all solution. In fact, IMB Knowledge Center published a piece on the limitations of Anti-Virus protection. In the article, they cite file size, scan time, and nesting depth as a few of the limitations.


Scientists discover potent compound that can kill superbugs that pose a threat to humanity

Daily Mail - Science & tech

Scientists have discovered a compound which can destroy antibiotic-resistant bacteria and hailed their work as a'breakthrough'. They found a type of chemical called a dinuclear Ru(II) complex which may be able to destroy bacteria too strong for treatment with normal medicines. Success in lab tests has given hope for a new way to tackle superbugs, which are becoming more common, more dangerous, and more difficult to stop. Pneumonia, urinary tract infections and gonorrhoea are all caused by the gram-negative type of bacteria used in the tests. And figures estimate that, by 2050, 10million people a year will die because of infections which have evolved to be untreatable.