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How AI, Machine Learning and SAP Drive the Healthcare Industry

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There has been a spike in healthcare concerns and crisis throughout 2019. So much that the World Health Organization's has implemented a 5-year strategic plan, the 13th General Programme of Work, to resolve some of these major issues in our society. The healthcare industry is extremely important for the well-being of our world and is constantly evolving and improving over time. While the changes in technology over the past decade are impressive, there is one in particular making its way to the top and will define the way we look at health care data. ERP systems have an extremely important role in data collection.


Edge to Core AI Futures for OEMs

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The ability of computers to autonomously learn, predict, and adapt using massive datasets is driving innovation and competitive advantage across many industries and applications. The artificial intelligence (AI) is budding faster and prompting businesses to hop aboard the next big wave of computing to uncover deeper insight, quickly resolve their most difficult problems, and differentiate their products and services. Whether the goal is to build a smarter city, power an intelligent car, or deliver personalized medicine, we've only just begun to understand the real potential of AI. For the implementation of AI, HPE OEM has the expertise, edge to core technologies and partner ecosystem to help explore different use cases, experiment with AI and data technologies, and build the solution to be enterprise-ready. HPE OEM will benefit at all stages of the journey from formulating a roadmap through implementation and data migration.


Exploring AI for radiotherapy planning with Mayo Clinic

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More than 18 million new cancer cases are diagnosed globally each year, and radiotherapy is one of the most common cancer treatments--used to treat over half of cancers in the United States. But planning for a course of radiotherapy treatment is often a time-consuming and manual process for clinicians. The most labor-intensive step in planning is a technique called "contouring" which involves segmenting both the areas of cancer and nearby healthy tissues that are susceptible to radiation damage during treatment. Clinicians have to painstakingly draw lines around sensitive organs on scans--a time-intensive process that can take up to seven hours for a single patient. Technology has the potential to augment the work of doctors and other care providers, like the specialists who plan radiotherapy treatment.


4 Penny Stocks To Watch After Recent News Sparks Market Momentum

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The premarket hours are back in the red again on Wednesday. But we're seeing plenty of penny stocks shine. The fact that most small-cap stocks are disconnected from the overall market trend is something I think is overlooked. While not all penny stocks will respond this way, many do. We saw this today with several of the breakout, midstream oil stocks that were moving hugely in after-hours trading on Tuesday evening.


Pseudo Labelling - A Guide To Semi-Supervised Learning

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There are 3 kinds of machine learning approaches- Supervised, Unsupervised, and Reinforcement Learning techniques. Supervised learning as we know is where data and labels are present. Unsupervised Learning is where only data and no labels are present. Reinforcement learning is where the agents learn from the actions taken to generate rewards. Imagine a situation where for training there is less number of labelled data and more unlabelled data.


Big Data and AI solutions for Drug Development (2019)

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The healthcare sector, that contains a diverse array of industries with activities ranging from research to manufacturing to facilities management (pharma, medical equipment, healthcare facilities), generated in 2013 something like 153 exabytes (1 exabyte 1 billion gigabytes). It is estimated that by year 2020 the healthcare sector will generate 2,134 exabytes. To put that into perspective data centres globally will have enough space only for an estimated of 985 exabytes by 2020. Meaning that two and a half times this capacity would be required to house all the healthcare data. Big data have four V's volume, velocity (real time will be crucial for healthcare), variety and veracity (noise, abnormality, and biases). Poor data quality costs the US economy $ 3,1 trillion a year. And 1 in 3 business leaders don't trust the information they use to make decisions, and this is true also for the healthcare sector.


Principal Component Analysis (PCA) with Scikit-learn

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This is the second unsupervised machine learning algorithm that I'm discussing here. This time, the topic is Principal Component Analysis (PCA). At the very beginning of the tutorial, I'll explain the dimensionality of a dataset, what dimensionality reduction means, main approaches to dimensionality reduction, reasons for dimensionality reduction and what PCA means. Then, I will go deeper into the topic PCA by implementing the PCA algorithm with Scikit-learn machine learning library. This will help you to easily apply PCA to a real-world dataset and get results very fast. In a separate article (not in this one), I will discuss the mathematics behind the principal component analysis by manually executing the algorithm using the powerful numpy and pandas libraries.


AI research has a big problem, and these powerhouse professors are calling it out

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Dozens of AI experts signed an article in Nature saying that unlike research in other scientific fields, top AI studies are often not transparent and reproducible, and they're frequently published without details such as full code, models, and methodology. Those findings are then picked up in mainstream media headlines worldwide. They point to a study also published in Nature this past January, where Google Health reported an AI system that could screen for breast cancer faster and better than radiologists. The study apparently lacked details like methodology and code. "On paper and in theory, the study is beautiful. But if we can't learn from it, then it has little to no scientific value," says lead author Benjamin Haibe-Kains, senior scientist at Princess Margaret Cancer Centre.


AI shows promise for breast cancer screening, says QF researcher

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Doha: Artificial Intelligence (AI) models are being developed and used to predict breast cancer in mammography scans with more accuracy than radiologists, thereby reducing false positives and false negatives. AI will become more common in breast cancer screening within the next ten years, said a Qatar Foundation (QF) researcher. "When using the naked eye to define abnormalities in image data or while analysing tissue, one could go wrong in the analysis. However, with artificial intelligence, classification of abnormal or normal tissue is more accurate," said Dr. Halima Bensmail, the Principal Scientist and Associate Professor at Qatar Computing Research Institute, part of QF's Hamad Bin Khalifa University. "Due to the extensive variation from patient to patient data, traditional learning methods are not reliable, and machine learning has evolved over the last few years with its ability to sift through complex and big data to be able to detect abnormalities," she told The Peninsula.


Artificial intelligence anticipates how instruments are used during surgery

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In the operating theater of the future, computer-based assistance systems will make work processes simpler and safer and thereby play a much greater role than today. "However, such support features are only possible if computers are able to anticipate important events in the operating room and provide the right information at the right time," explains Prof. Stefanie Speidel. She is head of the Department of Translational Surgical Oncology at the National Center for Tumor Diseases Dresden (NCT/UCC) in Germany. Together with the Centre for Tactile Internet with Human-in-the-loop (CeTI) at TU Dresden, she has developed a method that uses artificial intelligence (AI) to enable computers to anticipate the usage of surgical instruments before they are used. This kind of system does not just provide an important basis for the use of autonomous robotic systems that could take over simple minor tasks in the operating theater, such as blood aspiration.