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Two other related articles: CSDN machine learning notes eleven k-nearest neighbor algorithm CSDN machine learning notes twelve k-nearest neighbor algorithm to achieve handwriting recognition system. The proximity algorithm, or K-Nearest Neighbor kNN, k-NearestNeighbor classification algorithm is one of the simplest methods in data mining classification technology. The so-called K nearest neighbors means k nearest neighbors, which means that each sample can be represented by its nearest k neighbors. This method only determines the category of the sample to be classified based on the category of the nearest one or several samples in determining the classification decision. The kNN method is only related to a very small number of adjacent samples when making category decisions. A Note On Detection Algorithm.

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TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Train a neural network to classify images of clothing, like sneakers and shirts, in this fast-paced overview of a complete TensorFlow program. Start with building and training a retrieval model to predict a set of movies that a user is likely to watch, and then use a ranking model to create recommendations. Train a generative adversarial network to generate images of handwritten digits, using the Keras Subclassing API.

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What is this page? Visual acuity is the primary measure used in ophthalmology to determine how well a patient can see.

Visual acuity for a single eye may be recorded in multiple ways for a single patient visit eg, Snellen vs. Capturing the best documented visual acuity BDVA of each eye in an individual patient visit is an important step for making electronic ophthalmology clinical notes useful in research. Currently, there is limited methodology for capturing BDVA in an efficient and accurate manner from electronic health record EHR notes.

We developed an algorithm to detect BDVA for right and left eyes from defined A Note On Detection Algorithm within electronic ophthalmology clinical notes. We designed an algorithm to detect the BDVA from defined fields within click here, ophthalmology clinical notes with visual acuity data present. About unique responses were identified and an algorithm was developed to map all of the unique responses to a structured list of Snellen visual acuities. Visual acuity was captured from a total ofophthalmology clinical notes during the study dates. The algorithm identified all visual acuities in the defined visual acuity section for each eye and returned a single BDVA for each eye.

Our algorithm successfully captures best documented Snellen distance visual acuity from ophthalmology clinical notes and transforms a variety of inputs into a structured Snellen equivalent list. Our work, to the best of Algoeithm knowledge, represents the first attempt at capturing visual acuity accurately from large numbers of electronic ophthalmology notes.

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Use of this algorithm can benefit research groups interested in assessing visual acuity for patient centered outcome. This page is provided by Altmetric. Altmetric Badge. Mentioned by twitter 2 tweeters facebook 1 Facebook page.

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Readers on mendeley 13 Mendeley. Summary Twitter Facebook Dimensions citations. You are seeing a free-to-access but limited selection of the activity Altmetric has collected about link research output. Click here to find out more. View on publisher site Alert me about new mentions. Twitter Demographics The Algorihm shown below were collected from the profiles of 2 tweeters who shared this research output.

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