If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
There are many metrics to measure the performance of your machine learning model depending on the type of machine learning you are looking to conduct. In this article, we take a look at performance measures for classification and regression models and discuss which is better-optimized. Sometimes the metric to look at will vary according to the problem that is initially being solved. The True Positive Rate, also called Recall, is the go-to performance measure in binary/non-binary classification problems. Most of the time -- if not all of the time -- we are only interested in correctly predicting one class.
Artificial intelligence can be used to accurately examine electrocardiogram (ECG) test results, according to the findings of two preliminary studies being presented at the American Heart Association Scientific Sessions 2019 in Philadelphia, PA. In the first study, researchers evaluated 1.1 million ECGs that did indicate atrial fibrillation (AF) from more than 237,000 patients. They used specialized computational hardware to train a deep neutral network to assess 30,000 data points for each respective ECG. The results showed that approximately one in three people received an AF diagnosis within a year. Moreover, the model demonstrated the capacity for long-term prognostic significance as patients predicted to develop AF after one year had a 45% higher hazard rate in developing AF over a follow-up duration of 25-years compared to other patients.
SINGAPORE: By 2022, people living in Singapore will be able to report municipal issues via a chatbot that asks for details in real time and automatically identifies the correct government agency in charge. This will be made possible by artificial intelligence (AI), which is also set to power a tool that helps in the detection of diabetic eye disease and an automated marking system for English in primary and secondary education by the same year. More initiatives tapping on AI technologies, such as machine learning and computer vision, are in the pipeline over the next decade, according to five projects unveiled on Wednesday (Nov 13) as part of Singapore's new "National AI Strategy". The new strategy, which maps out how Singapore will develop and use AI to transform the economy and improve peoples' lives, was announced by Deputy Prime Minister Heng Swee Keat at the final day of the "Singapore FinTech Festival (SFF) x the Singapore Week of Innovation and TeCHnology (SWITCH) Conference". Describing it as the next step in Singapore's Smart Nation Journey, Mr Heng said: "Countries will need to keep pace with technology, and harness it to tackle common challenges and national priorities."
The Pima Indians of Arizona and Mexico have the highest reported prevalence of diabetes of any population in the world. A small study has been conducted to analyse their medical records to assess if it is possible to predict the onset of diabetes based on diagnostic measures. The dataset is downloaded from Kaggle, where all patients included are females at least 21 years old of Pima Indian heritage. The objective of this project is to build a predictive machine learning model to predict based on diagnostic measurements whether a patient has diabetes. This is a binary (2-class) classification project with supervised learning.
A new artificial intelligence system is capable of detecting diabetic retinopathy 95.5% of the time. The technology was described at the annual meeting of the American Academy of Ophthalmology (12–15 October, San Francisco). The system, named EyeArt, was used to screen 893 patients with diabetes in 15 different medical locations. It can provide a reading within 60 seconds. EyeArt displayed 95.5% sensitivity and 86% specificity, while more than 90% of eyes flagged as positive by the system had diabetic retinopathy or another eye disease.
The US Food and Drug Administration (FDA) has acknowledged the potential impact that artificial intelligence (AI) and machine learning (ML) can have on healthcare. The FDA has been hard at work on the cutting edge of how to regulate transformative AI to ensure patients have access to safe technology that saves lives. Vast amounts of health data are collected every day during routine medical procedures. The development of any form of machine learning relies upon high-quality pools of data to build the necessary algorithms. With so much data available to build algorithms from, the healthcare industry is an accessible field for AI to make a positive impact.
The effect that food has on blood glucose levels in people with Type 1 diabetes is well established. Less clear, however, is the role that stress, time of day, activity levels and other factors play in regulating blood glucose. To better understand these dynamics, UB researchers have launched a project that combines artificial intelligence (AI) with data gathered by continuous glucose monitoring tools. Ultimately, the goal is to better understand the relationship between insulin and blood glucosen, empowering people with Type 1 diabetes to better manage the condition and improve their quality of life. "We're developing new tools -- combining data collected from diabetes-monitoring tools with AI systems, as well as traditional time-series modeling approaches -- that could greatly improve how people manage their Type 1 diabetes," says the project's leader, Tarunraj Singh, professor of mechanical and aerospace engineering, School of Engineering and Applied Sciences.
We live in a world in which machine learning is at the core of the fourth industrial revolution. Linear regression is one of the simplest and most widely used machine learning techniques. There are a plethora of practical applications of linear regression. For example, obesity can be used to predict the chances of developing type 2 diabetes. Or, a student's GPA can be predicted based on the number of hours he/she spends studying.
Today, the diabetic patient himself calculates his insulin quantity, errors in the calculation lead to health damage and high consequential costs. Optimal management of blood glucose levels in people with diabetes (lack or insufficient insulin production for blood glucose control) is complicated. Diabetes patients have so far learned how to calculate insulin requirements. Often, however, this self-calculation is faulty and leads to severe health problems. The insulin quantity calculation could not be developed successfully so far.
Deep learning, a recently described AI machine learning technique, when applied to image analysis, allows the algorithm to analyze data using multiple processing layers to extract different image features,1x1LeCun, Y., Bengio, Y., and Hinton, G. Deep learning. In ophthalmology, many groups have reported exceptional diagnostic performance using deep learning algorithms to detect various ocular conditions based on anterior segment topography (e.g., keratoconus),5x5Hwang, E.S., Perez-Straziota, C.E., Kim, S.W. et al. Distinguishing highly asymmetric keratoconus eyes using combined Scheimpflug and spectral-domain OCT analysis. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning.