A recent study by IBM Research, together with Sage Bionetworks, Kaiser Permanente Washington Health Research Institute, and the University of Washington School of Medicine, has uncovered how combining machine learning algorithms and assessments by radiologists could improve the overall accuracy of breast cancer screenings. Mammogram screenings, commonly used by radiologists for the early detection of breast cancer, according to IBM researcher Stefan Harrer, frequently rely on a radiologist's expertise to visually identify signs of cancer, which is not always accurate. "Through the current state of human interpretation of mammography images, two things happen: Misdiagnosis in terms of missing the cancer and also diagnosing cancer when it's not there," Harrer told ZDNet. "Both cases are highly undesirable -- you never want to miss a cancer when it's there, but also if you're diagnosing a cancer and it's not there, it creates enormous pressure on patients, on the healthcare system, that could be avoided. "That is exactly where we aim to improve things through the incorporation of AI (artificial intelligence) to decrease the rate of false positives, which is the diagnosis of cancer, and also to decrease missing the cancer when there is one." The research used more than 310,800 de-identified mammograms and clinical data from Kaiser Permanente Washington (KPWA) and the Karolinska Institute (KI) in Sweden. Of the combined datasets, KI contributed around 166,500 examinations from 6,800 women, of which 780 were cancer positive; while the remaining 144,200 examinations were provided by KPWA from 85,500 women, of which 941 were cancer positive. "We had hundreds of thousands of mammograms that were annotated.
Artificial intelligence (AI) and machine learning are increasingly becoming a part of drug discovery and development beginning with identifying new compounds to structuring and designing clinical trials and targeting clinical trial populations. A recent example came out of Linköping University in Sweden. The investigators utilized an artificial neural network to create maps of biological networks based on how different genes or proteins interact with each other. The AI was then taught to find patterns of gene expression. And in mid-February, a drug developed using AI began testing in human clinical trials.
Human Bias is a serious issue in training datasets for Machine Learning and AI algorithms. The algorithms today are used in many critical situations and even in life-threatening scenarios, so limiting the bias is a requirement. Unbiased Data Marketplace address the issue of bias in training datasets with its unique approach and automation tools helping companies. In addition, Unbiased solution is transparent as all the events are recorded on the blockchain.
Data from Strategy Analytics show that smart speaker shipments in the Nordic countries of Denmark, Norway, and Sweden reached 900,000 in 2019. That figure is up sharply from shipments of only about 200,000 in 2018. These figures indicate impressive growth and smart speaker interest in countries that collectively claim only about 20 million in population. Strategy Analytics estimates the household installed base for smart speakers these Nordic countries is about 6%. Finland and Iceland were not included in the analysis since none of the leading smart speaker makers have offerings with language localization for these countries.
In the U.S. and other countries, aging populations and growing logistics demand have resulted in shortages of truck drivers. Autonomous trucks could help relieve those shortages. Einride AB today announced that it plans to hire what it called "the first autonomous and remote truck operator in the freight mobility space." The Stockholm-based company said it will hire drivers in Sweden next month, followed by the U.S. in the third quarter. The remote operators would begin commercial services in Sweden in Q3 2020 and in the U.S. in Q4 2020.
Researchers have developed an artificial neural network using deep learning to identify genes that are related to disease. An artificial neural network has revealed patterns in huge amounts of gene expression data and discovered groups of disease-related genes. The developers, from Linköping University, Sweden, hope that the method can eventually be applied within precision medicine and individualised treatment. The scientists created maps of biological systems based on how different proteins or genes interact with each other. Using artificial intelligence (AI), they investigated whether it is possible to discover biological networks with deep learning, in which entities known as artificial neural networks are trained by experimental data.
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Boston and San Francisco, January 7, 2020 – Pear Therapeutics, Inc., the leader in Prescription Digital Therapeutics (PDTs), announced today that it has entered into agreements with multiple technology innovators, including Firsthand Technology, Inc., leading researchers from the Karolinska Institute in Sweden, Cincinnati Children's Hospital Medical Center, Winterlight Labs, Inc., and NeuroLex Laboratories, Inc. These new agreements continue to bolster Pear's PDT platform, by adding to its library of digital biomarkers, machine learning algorithms, and digital therapeutics. Pear's investment in these cutting-edge technologies further supports its strategy to create the broadest and deepest toolset for the development of PDTs that redefine standard of care in a range of therapeutic areas. With access to these new technologies, Pear is positioned to develop PDTs in new disease areas, while leveraging machine learning to personalize and improve its existing PDTs. "We are excited to announce these agreements, which expand the leading PDT platform," said Corey McCann, M.D., Ph.D., President and CEO of Pear.
In this paper a new method for heat load prediction in district energy systems is proposed. The method uses a nominal model for the prediction of the outdoor temperature dependent space heating load, and a data driven latent variable model to predict the time dependent residual heat load. The residual heat load arises mainly from time dependent operation of space heating and ventilation, and domestic hot water production. The resulting model is recursively updated on the basis of a hyper-parameter free implementation that results in a parsimonious model allowing for high computational performance. The approach is applied to a single multi-dwelling building in Lulea, Sweden, predicting the heat load using a relatively small number of model parameters and easily obtained measurements. The results are compared with predictions using an artificial neural network, showing that the proposed method achieves better prediction accuracy for the validation case. Additionally, the proposed methods exhibits explainable behavior through the use of an interpretable physical model.