A plush, robotic duck may soon become a fixture in the world of children who have cancer -- a social robot that can be silly, happy, angry, scared or sick just like them, and help them cope creatively with their illness through the power of play. The duck, developed by robotics expert Aaron Horowitz and his company, is undergoing testing and is expected to be widely distributed by the end of this year. Horowitz said he was diagnosed as a child with human growth development deficiency and had to give himself daily injections for five years. The experience, he said, made him want to help other children with illnesses, which led to his co-founding of the Rhode Island-based company Sproutel with a partner he met at Northwestern University. Health care facilities from children's hospitals to nursing homes have been experimenting for more than a decade with the use of robots for social companionship and emotional health.
A plush, robotic duck may soon become a fixture in the world of children who have cancer - a social robot that can be silly, happy, angry, scared or sick just like them, and help them cope creatively with their illness through the power of play. The duck, developed by robotics expert Aaron Horowitz and his company, is undergoing testing and is expected to be widely distributed by the end of this year. Horowitz said he was diagnosed as a child with human growth development deficiency and had to give himself daily injections for five years. The'social robot' can be silly, happy, angry, scared or sick, and help them cope creatively with their illness through the power of play. Revealed at CES in Las Vegas, it could be on sale later this year.
IMAGE: This figure depicts an AI-driven drug discovery workflow schematic. Wednesday, Nov. 29, 2017, London, UK: Researchers from the Biogerontology Research Foundation, Insilico Medicine, Life Extension and other institutions announce the publication of a landmark study in the journal Aging on the identification of natural mimetics of metformin and rapamycin. Metformin, a common type 2 diabetes drug, and rapamycin, a common anti-rejection drug, have both been shown to have substantial anti-aging and anti-cancer effects in a variety of model organisms. However, both compounds have known side effects and are regulated drugs for existing disease indications, factors that problematize their off-label use as healthspan extending drugs. In this study, the researchers applied deep-learned neural networks to profile the safety and gene- and pathway-level similarity of more than 800 natural compounds to metformin and rapamycin, in an effort to identify natural compounds that can mimic the effects of these anti-cancer and anti-aging drugs while remaining free of the adverse effects associated with them.
We selected them for significance, novelty, and (in several cases) common task focus. Every year, AI Magazine devotes one fourth of its annual production to a special issue based on the Innovative Applications of Artificial Intelligence (IAAI) conference. Because IAAI is the premier venue for documenting the transition of AI technology into application, these special issues provide a snapshot of the state of the art in AI with the practical syllogism in mind; they present work that has value because it delivers value in use. As a result, it is good to read these articles from a practical perspective. Papers that document deployed systems clarify the motivating application constraints, the match (and mismatch) between problems and technology, the innovations required to surmount barriers to deployment, and the impact of technology on application through practical measures of cost and benefit.
It is a difficult task for physicians, who must manually interpret large volumes of blood glucose data to tailor therapy to the needs of each patient. This paper describes three emerging applications that employ AI to ease this task: (1) case-based decision support for diabetes management; (2) machine-learning classification of blood glucose plots; and (3) support vector regression for blood glucose prediction. The first application provides decision support by detecting blood glucose control problems and recommending therapeutic adjustments to correct them. The second provides an automated screen for excessive glycemic variability. The third aims to build a hypoglycemia predictor that could alert patients to dangerously low blood glucose levels in time to take preventive action.
If you or a family member are showing the telltale signs of a dreaded illness, it may be time to consult a doctor posthaste. In the meantime, in an age when much information can be accessed online through smartphones or laptops, alarmed individuals can do some checking on the triggers, symptoms, and treatment options for different health issues prior to or even after seeing a doctor. This year, Google Trends experts noted that Brits had searched for pressing health issues online. Among their most googled health conditions were cancer, diabetes, blood pressure and a condition called sepsis, the BBC News reported. Such health online checks are replicated in many other parts of the world.
Medial EarlySign, a developer of machine learning tools for data-driven medicine, announced the results of its clinical data study on identifying and stratifying prediabetic patients at high risk for progressing to diabetes within one year. This research has been completed at a period with no existing standards to identify prediabetic patients at risk of progressing to diabetes within a given timeframe, and offers care managers new opportunities to allocate diabetes prevention-focused resources and plan for care accordingly. The study, based on a database of 645,000 prediabetics, found that by isolating less than 20% of the prediabetic population, EarlySign's artificial intelligence (AI)-based algorithm platform successfully identified 64% of patients who became diabetic within 12 months. The algorithm utilizes more than 25 parameters derived from routine medical data stored in Electronic Health Records (EHR). It also ranks the 20% based on risk prioritization.
About one in seven U.S. adults has diabetes now, according to the Centers for Disease Control and Prevention. With this in mind, this is what we are going to do today: Learning how to use Machine Learning to help us predict Diabetes. The diabetes data set was originated from UCI Machine Learning Repository and can be downloaded from here. "Outcome" is the feature we are going to predict, 0 means No diabetes, 1 means diabetes. The k-NN algorithm is arguably the simplest machine learning algorithm.
A deep learning algorithm can detect metastases in sections of lymph nodes from women with breast cancer; and a deep learning system (DLS) has high sensitivity and specificity for identifying diabetic retinopathy, according to two studies published online Dec. 12 in the Journal of the American Medical Association. Babak Ehteshami Bejnordi, from the Radboud University Medical Center in Nijmegen, Netherlands, and colleagues compared the performance of automated deep learning algorithms for detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer with pathologists' diagnoses in a diagnostic setting. The researchers found that the area under the receiver operating characteristic curve (AUC) ranged from 0.556 to 0.994 for the algorithms. The lesion-level, true-positive fraction achieved for the top-performing algorithm was comparable to that of the pathologist without a time constraint at a mean of 0.0125 false-positives per normal whole-slide image. Daniel Shu Wei Ting, M.D., Ph.D., from the Singapore National Eye Center, and colleagues assessed the performance of a DLS for detecting referable diabetic retinopathy and related eye diseases using 494,661 retinal images.
I started my career as a MIS professional and then made my way into Business Intelligence (BI) followed by Business Analytics, Statistical modeling and more recently machine learning. Each of these transition has required me to do a change in mind set on how to look at the data. But, one instance sticks out in all these transitions. This was when I was working as a BI professional creating management dashboards and reports. Due to some internal structural changes in the Organization I was working with, our team had to start reporting to a team of Business Analysts (BA).