Health officials are warning thousands of people who attended a national cheerleading competition in Texas last month to be on the lookout for signs and symptoms of mumps. A person with the virus attended the National Cheerleaders Association All-Star National Championship in Fort Worth, which drew crowds from 39 states, between Feb. 23 and Feb. 25. Anyone who was present during that time may have been exposed, the Texas Department of State Health Services (DSHS) said in a letter. No residents in Texas have been reported with the disease as of Tuesday, according to the Dallas News. Here's what you need to know.

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Explainable AI or XAI is a sub-category of AI where the decisions made by the model can be interpreted by humans, as opposed to "black box" models. As AI moves from correcting our spelling and targeting ads to driving our cars and diagnosing patients, the need to verify and justify the conclusions being reached is beginning to be prioritised. To begin to delve into the field, lets look at one simple XAI model: the decision tree. Decision trees can be easily read and even mimic a human approach to decision making by breaking the choice into many small sub-choices. A simple example is how one may evaluate local universities when the leave high school.

We study the problem of detecting change points (CPs) that are characterized by a subset of dimensions in a multi-dimensional sequence. A method for detecting those CPs can be formulated as a two-stage method: one for selecting relevant dimensions, and another for selecting CPs. It has been difficult to properly control the false detection probability of these CP detection methods because selection bias in each stage must be properly corrected. Our main contribution in this paper is to formulate a CP detection problem as a selective inference problem, and show that exact (non-asymptotic) inference is possible for a class of CP detection methods. We demonstrate the performances of the proposed selective inference framework through numerical simulations and its application to our motivating medical data analysis problem.

Chinese device maker Lepu Medical Technology Co. has received approval from the U.S. Food and Drug Administration for the registration of an electrocardiogram analysis and diagnosis system based on artificial intelligence. The Beijing-based firm's Carewell Healthcare subsidiary developed the product, named AI ECG Platform, which covers major cardiovascular diseases, Lepu said in a statement. AI ECG Platform's diagnostic accuracy for a variety of heart diseases is over 95 percent, a similar level to ECG medical experts and it is capable of outperforming specialists for the diagnosis of some complex cases, the firm added. The company has applied for more than a dozen Chinese and international patents related to the technology. Lepu aims to promote the use of the product in primary hospitals and clinics which lack professional cardiologists.

This article is by Jitesh Shah, a data & stats jockey in perpetual beta, located in Fremont, California. This article includes the data set and Python code. Wouldn't it be nice if defects and product failures can be predicted in advance. We've got the data on attributes and design features and manufacturing processes that come together and creates that product and we have defect and failure rate data so all we got to do is connect the two and use that to predict which set of features and attributes and processes in combination cause these defects. That was probably a non-trivial endeavor in the past but now with the ability to store and process vast amounts of data (no secret there), no big deal.

Radiation therapy is an integral part of many cancer treatments. Ideally, doses are focused on the observable tumor while leaving surrounding organs unaffected, but determining the figuration of tumors and organs-at-risk is done manually--a time consuming and, at times, imprecise task for radiologists. A team of Chinese researchers developed a machine learning technique--closed polygonal line and backpropagation neural network model (CPL-BNNM)--for accurately detecting smooth lung contours in 3D-CT scans that is more efficient than manually determining such information and superior to currently used algorithms. "The important information for organ diseases can be quantitatively provided by the clinical images, while quantification is often manually implemented in some clinics," wrote Tao Peng, with the School of Computer Science & Technology at Soochow University. "In order to speed up the manual task and reduce workload, combining computer-aided diagnosis with automatic detection method is becoming a research hotspot."

In the coming decades, diagnostic medicine will likely change dramatically. Perhaps the most conspicuous change will be the arrival of artificial intelligence (AI) for faster and better care. AI is not pitting man against machine. It is, in fact, a way to ease the physician's burden and expand the possibilities of treatment. The administrative aspect of practicing medicine can be overwhelming, to say the least, and AI offers the chance to sort through large amounts of information quickly and accurately.

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I did a series of blog posts on different machine learning techniques recently, which sparked a lot of interest. You can see part 1, part 2, and part 3 if you want to learn about classification, clustering, regression, and so on. In that series I was careful to differentiate between a general technique and a specific algorithm like decision trees. Classification, for example, is a general technique used to identify members of a known class like fraudulent transactions, bananas, or high value customers. Read this machine learning post if you need a refresher or are wondering quite what bananas have to do with machine learning.