Sepsis remains one of the most costly and deadly of medical conditions. Sepsis is not a disease per se, but a syndrome, a collection of signs and symptoms, that indicated the presence of an overwhelming infection. Many, if not all, severely ill patients with COVID-19 had viral sepsis. Bacterial causes are more common, but sepsis in all its microbial forms carries a high mortality. Academics have long tortured clinical hospital data to find some statistical means of identifying sepsis or its incipient signs, because early intervention is associated with better outcomes.
Many medical imaging techniques have played a pivotal role in the early detection, diagnosis, and treatment of diseases, such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), mammography, and X-ray. AI has made significant progress which allows machines to automatically represent and explain complicated data. It is widely applied in the medical field, especially in some domains that need imaging data analysis. According to Vivantil et al by using deep learning models based on longitudinal liver CT studies, new liver tumours could be detected automatically with a true positive rate of 86%, while the stand-alone detection rate was only 72% and this method achieved a precision of 87% and an improvement of 39% over the traditional SVM mode. CNN models which use ultrasound images to detect liver lesions were also developed. According to Liu et al by using a CNN model based on liver ultrasound images, the proposed method can effectively extract the liver capsules and accurately diagnose liver cirrhosis, with the diagnostic AUC being able to reach 0.968.
'Phrenology' has an old-fashioned ring to it. It sounds like it belongs in a history book, filed somewhere between bloodletting and velocipedes. We'd like to think that judging people's worth based on the size and shape of their skull is a practice that's well behind us. However, phrenology is once again rearing its lumpy head. In recent years, machine-learning algorithms have promised governments and private companies the power to glean all sorts of information from people's appearance.
When solving machine learning problems, simply training a model based on a problem-specific training machine learning algorithm does not guarantee either that the resulting model fully captures the underlying concept hidden in the training data or that the optimum parameter values were chosen for model training. Failing to test a model's performance means an underperforming model could be deployed on the production system, resulting in incorrect predictions. Choosing one model from the many available options based on intuition alone is risky. By generating different metrics, the efficacy of the model can be assessed. Use of these metrics reveals how well the model fits the data on which it was trained.
Cybersecurity is of the utmost concern for financial institutions (FIs) of all types, ranging from community credit unions to multibillion-dollar international banking conglomerates to everyday consumers. More than 2 million fraud reports were issued to the Federal Trade Commission in 2020, reaching a total loss of more than $3 billion. One survey found that 47 percent of businesses around the world have reported being victimized by digital crime within the past two years, with losses totaling $42 billion. Fraudsters are also growing more advanced in their tactics, leveraging sophisticated technologies like artificial intelligence (AI) and machine learning (ML) to deploy millions of attacks simultaneously. The overwhelming volume of attacks has put organizations on the back foot, scrambling to find countermeasures to the account takeovers (ATOs), phishing attacks and other schemes they face by the thousands every day.
We can see Pr value here, and there are three stars associated with this Pr value. This basically means that we can reject the null hypothesis which states that there is no relationship between the age and the target columns. But since we have three stars over here, this null hypothesis can be rejected. There is a strong relationship between the age column and the target column. Now, we have other parameters like null deviance and residual deviance.
The field of machine learning is continuously evolving which led to a significant rise of the same in demand and importance. The application of machine learning models is now everywhere -- in our day-to-day life starting from movie recommendations on Netflix to product recommendations on Amazon. Starting from hiring a new employee to financial product approval decisions are now automatically done through machine learning models. It is assumed that a huge amount of data analyzed through improved machine learning algorithms can guide better decisions and smart actions in real-time without human intervention. However, this widespread usage of machine learning models leads to risk -the risk of bias.
Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods. Machine learning is a branch of computer science that broadly aims to enable computers to "learn" without being directly programmed (1). It has origins in the artificial intelligence movement of the 1950s and emphasizes practical objectives and applications, particularly prediction and optimization. Computers "learn" in machine learning by improving their performance at tasks through "experience" (2, p. xv). In practice, "experience" usually means fitting to data; hence, there is not a clear boundary between machine learning and statistical approaches. Indeed, whether a given methodology is considered "machine learning" or "statistical" often reflects its history as much as genuine differences, and many algorithms (e.g., least absolute shrinkage and selection operator (LASSO), stepwise regression) may or may not be considered machine learning depending on who you ask. Still, despite methodological similarities, machine learning is philosophically and practically distinguishable. At the liberty of (considerable) oversimplification, machine learning generally emphasizes predictive accuracy over hypothesis-driven inference, usually focusing on large, high-dimensional (i.e., having many covariates) data sets (3, 4). Regardless of the precise distinction between approaches, in practice, machine learning offers epidemiologists important tools. In particular, a growing focus on "Big Data" emphasizes problems and data sets for which machine learning algorithms excel while more commonly used statistical approaches struggle. This primer provides a basic introduction to machine learning with the aim of providing readers a foundation for critically reading studies based on these methods and a jumping-off point for those interested in using machine learning techniques in epidemiologic research.