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Artificial Intelligence In Healthcare Market Report, 2022-2030

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The global artificial intelligence in healthcare market size was valued at USD 10.4 billion in 2021 is expected to expand at a compound annual growth rate (CAGR) of 38.4% from 2022 to 2030. The growing datasets of patient health-related digital information, increasing demand for personalized medicine, and the rising demand for reducing care expenses are some of the major driving forces of the market growth. The growing global geriatric population, changing lifestyles, rising prevalence of chronic diseases has contributed to the surge in demand for diagnosing and improved understanding of diseases in their initial stages. Artificial Intelligence (AI) and machine learning (ML) algorithms are being widely adopted and integrated into healthcare systems to accurately predict diseases in their early stage based on historical health datasets. Furthermore, deep learning technologies, predictive analytics, content analytics, and Natural Language Processing (NLP) tools are enabling care professionals to diagnose patients' underlying health conditions at an earlier stage. The Covid-19 pandemic positively influenced the demand for AI technologies and unearthed the potential held by these advanced technologies.


Papers to Read on using Long Short Term Memory(LSTM) architecture in forecasting

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Abstract: The spread of COVID-19 has coincided with the rise of Graph Neural Networks (GNNs), leading to several studies proposing their use to better forecast the evolution of the pandemic. Many such models also include Long Short TermMemory (LSTM) networks, a common tool for time series forecasting. In this work, we further investigate the integration of these two methods by implementing GNNs within the gates of an LSTM and exploiting spatial information. In addition, we introduce a skip connection which proves critical to jointly capture the spatial and temporal patterns in the data. We validate our daily COVID-19 new cases forecast model on data of 37 European nations for the last 472 days and show superior performance compared to state-of-the-art graph time series models based on mean absolute scaled error (MASE).


Protein structure prediction using AlphaFold2

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My name is Dima and here I want to share my small project. It is about implementation of deep-learning tool in protein structure prediction. In the late December 2021 I was lucky to find online internship in the field of Bioinformatics. That was NyBerMan Merit Internship from LLBio-IT School and the main focus was, surprisingly (not), Covid investigation. After some technical interviews and huge competition (near 1000 participants for 20 places) I was planning next weeks of learning and doing.


A Hybrid Feature Extraction Method for Nepali COVID-19-Related Tweets Classification

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COVID-19 is one of the deadliest viruses, which has killed millions of people around the world to this date. The reason for peoples' death is not only linked to its infection but also to peoples' mental states and sentiments triggered by the fear of the virus. People's sentiments, which are predominantly available in the form of posts/tweets on social media, can be interpreted using two kinds of information: syntactical and semantic. Herein, we propose to analyze peoples' sentiment using both kinds of information (syntactical and semantic) on the COVID-19-related twitter dataset available in the Nepali language. For this, we, first, use two widely used text representation methods: TF-IDF and FastText and then combine them to achieve the hybrid features to capture the highly discriminating features. Second, we implement nine widely used machine learning classifiers (Logistic Regression, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, Decision Trees, Random Forest, Extreme Tree classifier, AdaBoost, and Multilayer Perceptron), based on the three feature representation methods: TF-IDF, FastText, and Hybrid. To evaluate our methods, we use a publicly available Nepali-COVID-19 tweets dataset, NepCov19Tweets, which consists of Nepali tweets categorized into three classes (Positive, Negative, and Neutral). The evaluation results on the NepCOV19Tweets show that the hybrid feature extraction method not only outperforms the other two individual feature extraction methods while using nine different machine learning algorithms but also provides excellent performance when compared with the state-of-the-art methods. Natural language processing (NLP) techniques have been developed to assess peoples' sentiments on various topics.


Interview and Discussion on the Potential of AI to Transform Healthcare with Dr. Ingrid Vasiliu-Feltes

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Artificial intelligence (AI) plays a crucial role in the healthcare industry by helping doctors, patients and hospital administrators. Artificial Intelligence (AI) is defined as computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. For the purposes of this article, Machine Learning and Deep Learning (Deep Neural Networks) are defined as sub-branches of AI. See the Appendix for a more detailed explanation of these areas. Healthcare systems were already under a substantial strain before the arrival of the Covid-19 pandemic. This strain has only increased since the pandemic and may cause challenges that persist for many years. It takes many years and costly resources to train healthcare workers. Specialist medical practitioners tend to be in short supply and often work long hours.


Detection of COVID -- 19 using Deep Learning

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"Coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by severe acute respiratory syndrome coronavirus 2". "The disease first originated in December 2019 from Wuhan, China and since then it has spread globally across the world affecting more than 200 countries. The impact is such that the World Health Organization(WHO) has declared the ongoing pandemic of COVID-19 a Public Health Emergency of International Concern." As of 29th April, there are a total of 31,30,191 cases with 2,17,674 deaths in more than 200 countries across the world. So, in this particular scenario, one primary thing that needs to be done and has already started in the majority of the countries is Manual testing, so that the true situation can be understood and appropriate decisions can be taken.


MIT and Tsinghua scholars use DeepMind's AlphaFold approach to boost COVID-19 antibodies

ZDNet

The world of structural biology was stunned in late 2020 by the arrival of AlphaFold 2, a second version of the deep learning neural network developed by the Google artificial intelligence unit DeepMind. AlphaFold solved a decades-old problem of how proteins fold, a key fact governing their function. Recent research shows the approaches pioneered by AlphaFold are spreading to the broader biology community. In a paper in the journal PNAS this month, "Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization," scientists describe modifying a known antibody against COVID-19 in such a way as to boost its efficacy against multiple variants of the disease. "We enable expanded antibody breadth and improved potency by 10- to 600-fold against SARS-CoV-2 variants, including Delta," starting from an antibody that had no effectiveness against the Delta variant, the scientists write.


Covid-19 Detection Using X-Ray Images

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The COVID-19 outbreak is causing havoc on the world's economy and public health. Until now, more than 27 million confirmed cases have been reported around the world. Because of the rising number of confirmed instances and the challenges posed by COVID-19 mutations, it is critical to classify healthy and infected people as soon as possible in order to control and treat COVID-19. During initial phases of, manual testing technique was used to detect the COVID -19 patients. However, manual testing has several disadvantages, like limited availability of testing kits, high prices, and inefficient blood tests. As a result, Deep Learning can be used to overcome these obstacles in order to provide a more effective and efficient treatment.


Modality specific U-Net variants for biomedical image segmentation: a survey - Artificial Intelligence Review

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With the advent of advancements in deep learning approaches, such as deep convolution neural network, residual neural network, adversarial network; U-Net architectures are most widely utilized in biomedical image segmentation to address the automation in identification and detection of the target regions or sub-regions. In recent studies, U-Net based approaches have illustrated state-of-the-art performance in different applications for the development of computer-aided diagnosis systems for early diagnosis and treatment of diseases such as brain tumor, lung cancer, alzheimer, breast cancer, etc., using various modalities. This article contributes in presenting the success of these approaches by describing the U-Net framework, followed by the comprehensive analysis of the U-Net variants by performing (1) inter-modality, and (2) intra-modality categorization to establish better insights into the associated challenges and solutions. Besides, this article also highlights the contribution of U-Net based frameworks in the ongoing pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19. Finally, the strengths and similarities of these U-Net variants are analysed along with the challenges involved in biomedical image segmentation to uncover promising future research directions in this area.


AI for protein folding

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The software, which uses an AI technique called deep learning, can predict the shape of proteins to the nearest atom, the first time a computer has matched the slow but accurate techniques used in the lab. Scientific teams around the world have started using it for research on cancer, antibiotic resistance, and covid-19. DeepMind has also set up a public database that it's filling with protein structures as AlphaFold2 predicts them. It currently has around 800,000 entries, and DeepMind says it will add more than 100 million--nearly every protein known to science--in the next year. DeepMind has spun off this work into a company called Isomorphic Labs, which it says will collaborate with existing biotech and pharma companies.