One approach is to design more robust algorithms where the testing error is consistent with the training error, or the performance is stable after adding noise to the dataset. For example, using "r" as a measure of similarity in the registration of low-contrast images can produce cases where "close to unity" means 0.998 and "far from unity" means 0.98, and there's no way to compute a p-value due to the extremely non-Gaussian distributions of pixel values involved. Robust statistics are also called nonparametric precisely because the underlying data can have almost any distribution and they will still produce a number that can be associated with a p-value. So while losing signal information can reduce the statistical power of a method, degrading gracefully in the presence of noise is an extremely nice feature to have, particularly when it comes time to deploy a method into production.
That is, each study participant would see as many varied product offerings (or product profiles) as the experiment required, and would evaluate each. For instance, eight features or attributes, each varied in three ways, would require 18 runs in the experiment--or 18 product profiles. This was overcome with the use of a machine learning method, Hierarchical Bayesian Analysis. This method provides highly accurate estimates by learning from the data where an individual's responses are spotty or missing.
Artificial intelligence software combined with a robotic harness could help spinal injury and stroke patients walk again. Rehabilitation programs for spinal cord injuries or strokes usually have patients walk on treadmills at a steady pace while harnesses support their weight to varying degrees. As part of a clinical trial of this "neurorobotic platform," the researchers experimented with 26 volunteers recovering from spinal cord injuries or strokes, whose disability ranged from being able to walk without assistance to being able to neither stand nor walk independently. After the volunteers walked roughly 20 meters using the neurorobotic platform to familiarize themselves with the apparatus, three patients with spinal cord injuries who previously could not stand independently could, immediately after such practice, walk with or without assistance.
New statistics or fake data science textbooks are published every week but with the exact same technical content: KNN clustering, logistic regression, naive Bayes, decision and boosted trees, SVM, Bayesian statistics, centroid clustering, linear discrimination - as in the early eighties, applied to tiny data such as Fisher's iris data set. If you compare traffic statistics (Alexa rank) from top traditional statistics websites, with data science websites, the contrast is surprising. These numbers are based on Alexa rankings, which are notoriously inaccurate, though over time, they have improved their statistical science to measure and filter Internet traffic, and the numbers that I quote here have been stable recently, showing the same trend for months, and subject to a small 30% error rate (compared to 100% error rate a few years ago, based on comparing Alexa variances over time for multiple websites that we own and for which we know exact traffic stats after filtering out robots). Modern statistical data science techniques are far more robust than traditional statistics, and designed for big data.
The format is intentionally not dependent on every member regularly attending--shit happens, people get busy. In our case, questions from every skill level are great, but a beginner monopolizing the discussion ruins it for everyone else and makes it much harder to keep experts engaged. The format is intentionally not dependent on every member regularly attending--shit happens, people get busy. In our case, questions from every skill level are great, but a beginner monopolizing the discussion ruins it for everyone else and makes it much harder to keep experts engaged.
I am self-learning Machine Learning/Data Science and thought it would be very cool to have a group of individuals with whom to discuss what we just learned and talk about parts that were hard to understand. Also, perhaps doing a few projects together would help to get used to working on ML in a team. Another thing is motivation, having deadlines might force you to finally finish reading that chapter instead of browsing Reddit. If you have any suggestions on this list, feel free to make them.
Google is developing tools to analyze large volumes of electronic health records (EHRs) and identify patient groups at risk of cardiac arrest, illness relapse, or other events, therefore reducing the likelihood of emergency hospital visits and inpatient stays. In Paris, a group of public hospitals is applying data analytics and machine learning to predict times of high patient volumes, allowing facilities to adjust resources in response to admission trends. Other market players are exploring AI algorithms and analytics for health applications including genomic-based precision medicine, cancer treatment protocols, wearable health device monitoring, and clinical trial enrollment. IBM's Watson Health analyzed cancer center data to identify potential treatments previously not considered by doctors; another Watson technology used at a neurological institute helped identify five new genes associated with ALS.
Analyzing and understanding social media and patient discussions at forums provide additional leading indicators of potential study issues. This functionality will enable us to predict the likelihood of meeting the required recruitment number at sites and the most optimal match to make the study a success in terms of time and budget. This learning model can predict the success probability of a specific site for the target study. A recent McKinsey Global Institute (MGI) report,6 "The Age of Analytics: Competing in a Data-Driven World," explains the role of analytics for enhanced decision making, disruptive business models, and organizational challenges.
London's Royal Free hospital failed to comply with the Data Protection Act when it handed over personal data of 1.6 million patients to DeepMind, a Google subsidiary, according to the Information Commissioner's Office. The ICO ruled that testing the app with real patient data went beyond Royal Free's authority, particularly given how broad the scope of the data transfer was. The ruling does not directly criticise DeepMind, a London-based AI company purchased by Google in 2013, since the ICO views the Royal Free as the "data controller" responsible for upholding the data protection act throughout its partnership with Streams, with DeepMind acting as a data processor on behalf of the trust. Streams has since been rolled out to other British hospitals, and DeepMind has also branched out into other clinical trials, including a project aimed at using machine-learning techniques to improve diagnosis of diabetic retinopathy, and another aimed at using similar techniques to better prepare radiotherapists for treating head and neck cancers.