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Presenting the Best of CES 2021 winners!

Engadget

As Wednesday draws to a close, so does a grand social experiment: the first-ever online-only CES. In the end, the experience was invariably different. We particularly missed being able to wander The Sands and have learn about smaller, up-and-coming startups. And if seeing is believing, the oddest entries at the show remained locked behind our computer screen, with no chance of getting hands-on time. And yet, we were kept busy this week. Most of the usual tech giants had news to share, and many of those were able to show us their wares in person, ahead of the three-day gadget extravaganza.


Cheating on your Mediterranean diet with traditional Western can speed up brain aging, study reveals

Daily Mail - Science & tech

Cheating on your diet could lead to weight gain, but if you follow the Mediterranean diet and switch to unhealthy foods you may also make your brain age faster. A team from Rush University Medical Center found that adding in foods from the Western diet, such as pizza, sweets and processed meats, reverse cognitive benefits from the Mediterranean diet. The study examined more than 5,000 individuals over the age of 65 from 1993 to 2021 and over the course of three years participants were asked to complete cognitive tests and report on how often the ate certain foods. Researcher recently compiled the data and found those who stuck to the Mediterranean diet had brains that were nearly six years younger than their peers on the Western diet. The Mediterranean diet is inspired by the eating habits of Spain, Italy and Greece, and focuses on consuming more fruit and fish and limiting sugar, dairy and processed foods.


Using artificial intelligence to find new uses for existing medications

#artificialintelligence

The intent of this work is to speed up drug repurposing, which is not a new concept -- think Botox injections, first approved to treat crossed eyes and now a migraine treatment and top cosmetic strategy to reduce the appearance of wrinkles. But getting to those new uses typically involves a mix of serendipity and time-consuming and expensive randomized clinical trials to ensure that a drug deemed effective for one disorder will be useful as a treatment for something else. The Ohio State University researchers created a framework that combines enormous patient care-related datasets with high-powered computation to arrive at repurposed drug candidates and the estimated effects of those existing medications on a defined set of outcomes. Though this study focused on proposed repurposing of drugs to prevent heart failure and stroke in patients with coronary artery disease, the framework is flexible -- and could be applied to most diseases. "This work shows how artificial intelligence can be used to'test' a drug on a patient, and speed up hypothesis generation and potentially speed up a clinical trial," said senior author Ping Zhang, assistant professor of computer science and engineering and biomedical informatics at Ohio State.


Opportunities for machine learning use in cystic fibrosis care

AIHub

Accurately predicting how an individual's chronic illness is going to progress is critical to delivering better-personalised, precision medicine. Yet there is an enormous challenge in accurately predicting the clinical trajectories of people for chronic health conditions such as cystic fibrosis (CF), cancer, cardiovascular disease and Alzheimer's disease. AI technology developed by the Cambridge Centre for AI in Medicine and their colleagues offers a glimpse of the future of precision medicine, and the predictive power which may be available to clinicians caring for individuals with the life-limiting condition cystic fibrosis. "Prediction problems in healthcare are fiendishly complex," said Professor Mihaela van der Schaar, Director of the Cambridge Centre for AI in Medicine (CCAIM). "Even machine learning approaches, which deal in complexity, struggle to deliver meaningful benefits to patients and clinicians, and to medical science more broadly. Off-the-shelf machine learning solutions, so useful in many areas, simply do not cut it in predictive medicine."


Deep Learning Based Classification of Unsegmented Phonocardiogram Spectrograms Leveraging Transfer Learning

arXiv.org Artificial Intelligence

Cardiovascular diseases (CVDs) are the main cause of deaths all over the world. Heart murmurs are the most common abnormalities detected during the auscultation process. The two widely used publicly available phonocardiogram (PCG) datasets are from the PhysioNet/CinC (2016) and PASCAL (2011) challenges. The datasets are significantly different in terms of the tools used for data acquisition, clinical protocols, digital storages and signal qualities, making it challenging to process and analyze. In this work, we have used short-time Fourier transform (STFT) based spectrograms to learn the representative patterns of the normal and abnormal PCG signals. Spectrograms generated from both the datasets are utilized to perform three different studies: (i) train, validate and test different variants of convolutional neural network (CNN) models with PhysioNet dataset, (ii) train, validate and test the best performing CNN structure on combined PhysioNet-PASCAL dataset and (iii) finally, transfer learning technique is employed to train the best performing pre-trained network from the first study with PASCAL dataset. We propose a novel, less complex and relatively light custom CNN model for the classification of PhysioNet, combined and PASCAL datasets. The first study achieves an accuracy, sensitivity, specificity, precision and F1 score of 95.4%, 96.3%, 92.4%, 97.6% and 96.98% respectively while the second study shows accuracy, sensitivity, specificity, precision and F1 score of 94.2%, 95.5%, 90.3%, 96.8% and 96.1% respectively. Finally, the third study shows a precision of 98.29% on the noisy PASCAL dataset with transfer learning approach. All the three proposed approaches outperform most of the recent competing studies by achieving comparatively high classification accuracy and precision, which make them suitable for screening CVDs using PCG signals.


How AI Technology Driving Health Care Advanced

#artificialintelligence

The word'artificial intelligence' (AI Technology) conjures up mental visions of futuristic-looking robots for many individuals, making our lives simpler through realistic emotions and behaviors. Unfortunately, Rosie, the Robot is not rolling through the door any time soon, as much as we would all love to have an animatronic housekeeper. The truth is that through a carefully balanced collaboration of human judgment and data-driven research, AI technology makes our lives simpler in several ways. Our online interactions are more personalized and tailored to our desires than ever before, with AI's assistance. Since businesses are programming chatbots to answer the most commonly asked questions, we no longer have to call a customer service phone line and wait on hold for 15 minutes.


A random shuffle method to expand a narrow dataset and overcome the associated challenges in a clinical study: a heart failure cohort example

arXiv.org Machine Learning

Heart failure (HF) affects at least 26 million people worldwide, so predicting adverse events in HF patients represents a major target of clinical data science. However, achieving large sample sizes sometimes represents a challenge due to difficulties in patient recruiting and long follow-up times, increasing the problem of missing data. To overcome the issue of a narrow dataset cardinality (in a clinical dataset, the cardinality is the number of patients in that dataset), population-enhancing algorithms are therefore crucial. The aim of this study was to design a random shuffle method to enhance the cardinality of an HF dataset while it is statistically legitimate, without the need of specific hypotheses and regression models. The cardinality enhancement was validated against an established random repeated-measures method with regard to the correctness in predicting clinical conditions and endpoints. In particular, machine learning and regression models were employed to highlight the benefits of the enhanced datasets. The proposed random shuffle method was able to enhance the HF dataset cardinality (711 patients before dataset preprocessing) circa 10 times and circa 21 times when followed by a random repeated-measures approach. We believe that the random shuffle method could be used in the cardiovascular field and in other data science problems when missing data and the narrow dataset cardinality represent an issue.


Council Post: How AI Technology Is Driving Health Care Forward

#artificialintelligence

For many people, the term "artificial intelligence" (AI) conjures up mental images of futuristic-looking robots making our lives easier through realistic emotions and behaviors. Sadly, as much as we'd all love to have an animatronic housekeeper, Rosie the Robot isn't rolling through that door any time soon. The reality is that AI is making our lives easier in a variety of ways through a delicately balanced collaboration of human judgment and data-driven science. With the help of AI, our online experiences are more personalized and targeted to our interests than ever before. We no longer have to call a customer service phone line and wait on hold for 15 minutes, since companies are programming chatbots to answer the most frequently asked questions.


5 recent studies exploring AI in healthcare: In the past decade, the medical research community has become increasingly interested in artificial intelligence's potential to transform healthcare for the better by reducing workflow inefficiencies, predicting health outcomes and speeding up diagnoses.

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"Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model": Researchers from New Hyde Park, N.Y.-based Northwell Health's research arm developed an artificial intelligence tool to predict which patients will remain stable overnight and don't need to be awoken for vital monitoring. The tool cut in half the number of patients who were awoken during the night for vital sign checks, misclassifying less than two of 10,000 cases. "Evaluation of the use of combined artificial intelligence and pathologist assessment to review and grade prostate biopsies": Researchers developed an artificial intelligence tool to improve pathologists' grading of prostate needle biopsies, finding significant increases in grading agreement. "Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: A multicentre retrospective diagnostic study": The research team developed and validated a real-time deep convolutional neural networks system for the detection of early gastric cancer. "Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography": Researchers developed a deep learning-based artificial intelligence algorithm to help detect myocardial infarction using electrocardiography to speed up the diagnosis process.


Intrinsic persistent homology via density-based metric learning

arXiv.org Machine Learning

We address the problem of estimating intrinsic distances in a manifold from a finite sample. We prove that the metric space defined by the sample endowed with a computable metric known as sample Fermat distance converges a.s. in the sense of Gromov-Hausdorff. The limiting object is the manifold itself endowed with the population Fermat distance, an intrinsic metric that accounts for both the geometry of the manifold and the density that produces the sample. This result is applied to obtain sample persistence diagrams that converge towards an intrinsic persistence diagram. We show that this method outperforms more standard approaches based on Euclidean norm with theoretical results and computational experiments.