Endocrinology


Can AI Transform Patient Care from Reactive Craft to Strategic Art? -

#artificialintelligence

Personalized Analytics is becoming essential in healthcare, stemming from the movement from fee-for-service to a value-based market. The need to preempt and prevent disease on a more personal level, rather than merely reacting to symptoms, has created a significant opportunity for machine learning-based applications. This "analytics of one" approach (using advanced mathematical models and artificial intelligence techniques) is already impacting several key areas: Prime examples include cardiac imaging analysis that aides physicians in assessing conditions, including heart attacks and coronary artery disease, and retinal image analysis to detect diabetic retinopathy. The anticipated goal for AI in healthcare is to enhance and expand the "four Ps" of care delivery – predictive, preventative, personalized and participatory. Predictive: Predictions have existed in healthcare for some decades now, as statistical models based on structured data sources.


Data Privacy Splits Global AI Race

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In the world of AI research, Europe has drawn a line in the sand, declaring that R&D must focus squarely on "Edge AI." This proclamation draws a stark contrast to "Cloud-based AI," the model aggressively pursued by China and the United States. During "Innovation Days" hosted here by French research institute CEA-Leti this past week, Emmanuel Sabonnadiere, CEA-Leti's CEO, discussed the "two schools of AI research" that have split the world in two. Both the U.S. and China have been collecting massive amounts of data which they use for training AIs, the basis for their claims they lead the world AI race. Strict data privacy regulations in Europe might be seen as impeding European companies' progress in AI, but that's not necessarily the case.


Patients Report Mixed Views on Health-Tech and AI

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Nearly 50% of patients consider biometric monitoring devices and artificial intelligence (AI) a great opportunity, while 11% view the technologies as a great danger, according to the findings of a study published in npj Digital Medicine. Through open-ended questions, 47% of patients said they believe the technologies offer great opportunities and identified 47 potential benefits. The respondents said that health-tech could improve their follow-up and the reactivity of care (55%), reduce their burden of treatment (23%) and facilitate physicians' work (21%). "Coupled with the progress of AI, the thousands of data points collected from (biometric monitoring devices) may help in informing diagnosis, predicting patient outcomes and helping care professionals select the best treatment for their patients," the study authors wrote. One participant, a 35-year-old man with diabetes and Hashimoto's thyroiditis, said that new technologies are, "the only way for a physician to simultaneously take into account all multiple parameters necessary to adjust diabetes treatment: insulin sensitivity, duration of action, blood sugar levels, physical activity, continuous measurement…" But others viewed the technologies as a great danger.


Even identical twins don't react the same way to the same foods -- which is why most diet advice doesn't work

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Dietary advice seems to change every decade. Fat is bad, then suddenly it's good again. Nowadays, for many people, carbs are the enemy. But it turns out that healthy dietary guidelines can't be boiled down into simple rules. A new crop of studies, which leverage the latest health testing and machine learning technologies, are finding that there's no single diet that works for everyone.


AI In Healthcare: Fact Or Fiction?

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With the lag of tech in healthcare, will AI/ML improve patient care or remain a smart idea? Technology experts have promised artificial intelligence (AI) and machine learning (ML) will revolutionize healthcare. Applications have the potential to streamline workflows and reduce human errors, speeding drug discovery, assisting surgery, and provisioning better billing and coding methods. But, in an industry that typically lags in digital maturity by as much as 10 years, according to a 2017 study, is AI in healthcare an empty promise or truly a forward-thinking and innovative reality? Technology experts have promised artificial intelligence (AI) and machine learning (ML) will revolutionize healthcare.


An Introduction to AI and Machine Learning

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Get a free IBM Cloud account. Machine learning is branching out across numerous fields, one of the most interesting fields is health care. In this tech talk, we will go through an overview of what Machine Learning and Artificial Intelligence are, explaining at a high level key concepts such as models and classifiers. After, we will go through an example of how to train a machine learning model to predict type 2 diabetes using synthesized patient health records. Anwesha will demo preparing data using Apache Spark, visualizing data relationships using PixieDust, training a model, and deploying it to receive predictions.


Correlating Twitter Language with Community-Level Health Outcomes

arXiv.org Machine Learning

We study how language on social media is linked to diseases such as atherosclerotic heart disease (AHD), diabetes and various types of cancer. Our proposed model leverages state-of-the-art sentence embeddings, followed by a regression model and clustering, without the need of additional labelled data. It allows to predict community-level medical outcomes from language, and thereby potentially translate these to the individual level. The method is applicable to a wide range of target variables and allows us to discover known and potentially novel correlations of medical outcomes with life-style aspects and other socioeconomic risk factors.


Errors-in-variables Modeling of Personalized Treatment-Response Trajectories

arXiv.org Machine Learning

Estimating the effect of a treatment on a given outcome, conditioned on a vector of covariates, is central in many applications. However, learning the impact of a treatment on a continuous temporal response, when the covariates suffer extensively from measurement error and even the timing of the treatments is uncertain, has not been addressed. We introduce a novel data-driven method that can estimate treatment-response trajectories in this challenging scenario. We model personalized treatment-response curves as a combination of parametric response functions, hierarchically sharing information across individuals, and a sparse Gaussian process for the baseline trend. Importantly, our model considers measurement error not only in treatment covariates, but also in treatment times, a problem which arises in practice for example when treatment information is based on self-reporting. In a challenging and timely problem of estimating the impact of diet on continuous blood glucose measurements, our model leads to significant improvements in estimation accuracy and prediction.


Four Steps To Implementing Artificial Intelligence In Clinical Settings – Flarrio

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The clinical implementation of Artificial Intelligence (AI) is the most viable means of uniting the interests of the healthcare industry's capital constituents: the patient, the payer, and the provider. AI can improve healthcare outcomes while reducing costs when used to address patient compliance, chronic care management, genome sequencing, and physician diagnostics by classifying treatment options. Its widespread clinical deployment is poised to transform the healthcare industry into one that maintains wellness instead of merely combating illness. Maximizing AI's clinical value depends on the proper execution of four interrelated steps, each of which represents emerging developments within the industry: The proper implementation of each of these steps will ensure a future in which AI substantially contributes to decreased costs of chronic care and patient non-adherence, while achieving patient objectives in accordance with contemporary physician economics. Their implementation will also provide physicians with a vital support tool for conducting remote diagnostics, treatment classifications and accelerated care management.


People using Tinder and other dating apps are 'more likely to use steroids'

Daily Mail - Science & tech

People who use dating apps such as Tinder may be up to 27 times as likely to use drastic or unhealthy techniques to try and stay slim. Deliberately vomiting, taking laxatives and even using anabolic steroids is more common among dating app users, a study found. Researchers found'unrealistic' desires to look like celebrities on television and social media are driving people to damaging behaviour. And with an estimated 50million people around the world signed up to Tinder the scientists warned experts must better understand its damaging effects. Researchers said social media and TV shows reinforce'ideal' body images which drive men to try and become more muscly and women slimmer, which may drive them to drastic weight loss measures (Pictured: Love Island contestants Anton Danyluk and Amber Gill – the show is well-known for displaying young people with extremely honed bodies.