regression


Machine learning model closely predicts patient waiting times for CT, MRI

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"We noticed that most patients who were dissatisfied with the displayed waiting times were delayed for longer than predicted, so the need for more accurate models became imminent," Curtis et al. said. "We also wanted to predict not only waiting times for walk-in facilities, but also delays for the scheduled facilities." Stepping outside of existing research, Curtis and her co-authors zoned in on machine learning, an artificial intelligence modality that can reflect sophisticated trends otherwise difficult to capture with traditional regression approaches. Machine learning models can also resist noise, adapt to changing environments and run without human supervision, the researchers wrote, which fit the needs of a waiting room to a T. The team considered CT exams, MRI, ultrasound and radiography--only the last of which offered walk-in appointments--for the study. They evaluated 10 machine learning algorithms, including neural network, random forest, support vector machine, elastic net, multivariate adaptive regression splines, k-th nearest neighbor and linear regression, to find the algorithm that most closely predicted waiting times.


Machine learning model closely predicts patient waiting times for CT, MRI

#artificialintelligence

"We noticed that most patients who were dissatisfied with the displayed waiting times were delayed for longer than predicted, so the need for more accurate models became imminent," Curtis et al. said. "We also wanted to predict not only waiting times for walk-in facilities, but also delays for the scheduled facilities." Stepping outside of existing research, Curtis and her co-authors zoned in on machine learning, an artificial intelligence modality that can reflect sophisticated trends otherwise difficult to capture with traditional regression approaches. Machine learning models can also resist noise, adapt to changing environments and run without human supervision, the researchers wrote, which fit the needs of a waiting room to a T. The team considered CT exams, MRI, ultrasound and radiography--only the last of which offered walk-in appointments--for the study. They evaluated 10 machine learning algorithms, including neural network, random forest, support vector machine, elastic net, multivariate adaptive regression splines, k-th nearest neighbor and linear regression, to find the algorithm that most closely predicted waiting times.



Some Essential Hacks and Tricks for Machine Learning with Python

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It's never been easier to get started with machine learning. In addition to structured MOOCs, there is also a huge number of incredible, free resources available around the web. Familiarity and moderate expertise in at least one high-level programming language is useful for beginners in machine learning. Unless you are a Ph.D. researcher working on a purely theoretical proof of some complex algorithm, you are expected to mostly use the existing machine learning algorithms and apply them in solving novel problems. This requires you to put on a programming hat.


Learning Each Function with Machine Learning

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Machine Learning is a subset of Artificial Intelligence (AI) that provides computers with the ability to learn without being explicitly programmed and to take intelligent decisions. It also enables machines to grow and improve with experiences. There are 3 types of learning that are associated with Machine Learning & these are: supervised, unsupervised and semi-supervised learning. Supervised: It works with the labeled data and the algorithms in it learn to predict the output from the input data itself. Unsupervised: It works with the unlabeled data and the algorithms learn to inherent structure from the input data.


Data Science – The New Monetization Model for Analytics Industry

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"Data Scientist is the sexiest job of the 21st century" – Harvard Business Review "Expect a shortage of over 100,000 data scientists by 2020" – Gartner Unarguably, in today's hyper-competitive marketplace, Data Science plays an indispensable role for organizations to personalize experiences and create value out of their data. Analyzing large data sets without preset defined rules or scope for analysis to uncover insights, a sublime concept till a few years ago, will form the key basis of competition in the future to significantly unlock business value, unleashing new waves of productivity for businesses, enabling a culture of innovation, and reinvigorating internal processes, as long as the right ecosystem and enablers are put in place. Numerous articles today are buzzing with this glamourous new word in the Analytics world i.e. So what exactly is Data Science or this hype around Data Scientist? Frankly speaking, multiple definitions, roles, job descriptions exist making it harder for businesses to understand what truly is the role about and the ROI out of making any additional investments.


The 10 Statistical Techniques Data Scientists Need to Master

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Regardless of where you stand on the matter of Data Science sexiness, it's simply impossible to ignore the continuing importance of data, and our ability to analyze, organize, and contextualize it. Drawing on their vast stores of employment data and employee feedback, Glassdoor ranked Data Scientist #1 in their 25 Best Jobs in America list. So the role is here to stay, but unquestionably, the specifics of what a Data Scientist does will evolve. With technologies like Machine Learning becoming ever-more common place, and emerging fields like Deep Learning gaining significant traction amongst researchers and engineers -- and the companies that hire them -- Data Scientists continue to ride the crest of an incredible wave of innovation and technological progress. While having a strong coding ability is important, data science isn't all about software engineering (in fact, have a good familiarity with Python and you're good to go).


10 machine learning algorithms Every Data Scientist should know in 2018

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A data scientist is a person hired to analyze and interpret complicated digital records, together with the utilization statistics of a website; particularly so that it will help an enterprise in its decision-making. An analytical model is a mathematical model that is designed to carry out a particular task or to find out the probability of a selected event i.e. the solution to the equations used to describe modifications in a system can be expressed as a mathematical analytic function. According to Layman, an analytical model is simply a mathematical presentation of an enterprise problem. A simple equation y a bx may be termed as a model with a group of predefined input data and desired output. Scalable and efficient analytical modeling is severely consequential to enable the business to use those techniques to ever-more sizably voluminous data sets for reducing the time taken to carry out these analyses.



Machine Learning - The Hitchhiker's Guide to Python

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Machine learning is undoubtedly on the rise, slowly climbing into'buzzword' territory. This is in large part due to misuse and simple misunderstanding of the topics that come with the term. Take a quick glance at the chart below and you'll see this illustrated quite clearly thanks to Google Trends' analysis of interest in the term over the last few years. However, the goal of this article is not to simply reflect on the popularity of machine learning. It is rather to explain and implement relevant machine learning algorithms in a clear and concise way.