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Pseudo Labelling - A Guide To Semi-Supervised Learning

#artificialintelligence

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

#artificialintelligence

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

#artificialintelligence

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

#artificialintelligence

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.


Machine Learning Helped Predict Short-Term Cancer Mortality – Cancer Therapy Advisor – IAM Network

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Researchers have validated a machine-learning algorithm that was integrated into an electronic health record to generate real-time, accurate predictions of the short-term mortality risk for patients with cancer, according to a recent study. Additionally, this machine-learning algorithm outperformed other prognostic indices. "Such an automated tool may complement clinician intuition and lead to improved targeting of supportive care interventions for high-risk patients with cancer," the researchers wrote. The prospective study included 24,582 patients with outpatient oncology encounters from March 2019 to April 2019. Encounters occurred at 1 tertiary and 17 general oncology practices.


Digital Healthcare in Latin America

Communications of the ACM

The healthcare system in Latin America (LATAM) has made significant improvements in the last few decades. Nevertheless, it still faces significant challenges, including poor access to healthcare services, insufficient resources, and inequalities in health that may lead to decreased life expectancy, lower quality of life, and poor economic growth. Digital Healthcare (DH) enables the convergence of innovative technology with recent advances in neuroscience, medicine, and public healthcare policy.a In this article, we discuss key DH efforts that can help address some of the challenges of the healthcare system in LATAM focusing on two countries: Brazil and Mexico. We chose to study DH in the context of Brazil and Mexico as both countries are good representatives of the situation of the healthcare system in LATAM and face similar challenges along with other LATAM countries. Brazil and Mexico have the largest economies in the region and account for approximately half of the population and geographic territory of LATAM.11


Machine Learning Intervention Triples Serious Illness Conversations Among Oncology Clinicians

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An intervention that delivered machine learning mortality predictions with behavioral nudges to oncology clinicians significantly increased the rate of …


Can We Trust AI Doctors? Google Health and Academics Battle It Out

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Machine learning is taking medical diagnosis by storm. From eye disease, breast and other cancers, to more amorphous neurological disorders, AI is routinely matching physician performance, if not beating them outright. Yet how much can we take those results at face value? When it comes to life and death decisions, when can we put our full trust in enigmatic algorithms--"black boxes" that even their creators cannot fully explain or understand? The problem gets more complex as medical AI crosses multiple disciplines and developers, including both academic and industry powerhouses such as Google, Amazon, or Apple, with disparate incentives.