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Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients

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Previous scoring models, such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score, do not adequately predict the mortality of patients receiving mechanical ventilation in the intensive care unit. Therefore, this study aimed to apply machine learning algorithms to improve the prediction accuracy for 30-day mortality of mechanically ventilated patients. The data of 16,940 mechanically ventilated patients were divided into the training-validation (83%, n = 13,988) and test (17%, n = 2952) sets. Machine learning algorithms including balanced random forest, light gradient boosting machine, extreme gradient boost, multilayer perceptron, and logistic regression were used. We compared the area under the receiver operating characteristic curves (AUCs) of machine learning algorithms with those of the APACHE II and ProVent score results. The extreme gradient boost model showed the highest AUC (0.79 (0.77–0.80)) for the 30-day mortality prediction, followed by the balanced random forest model (0.78 (0.76–0.80)). The AUCs of these machine learning models as achieved by APACHE II and ProVent scores were higher than 0.67 (0.65–0.69), and 0.69 (0.67–0.71)), respectively. The most important variables in developing each machine learning model were APACHE II score, Charlson comorbidity index, and norepinephrine. The machine learning models have a higher AUC than conventional scoring systems, and can thus better predict the 30-day mortality of mechanically ventilated patients.


How sparsification and quantization build leaner AI

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Artificial Intelligence (AI) and Machine Learning (ML) are rarely out of the news. Technology vendors are busy jostling for position in the AI-ML marketplace, all keen to explain how their approach to automation can speed everything from predictive maintenance for industrial machinery to knowing what day consumers are most likely to order vegan sausages in their online shopping orders. Much of the debate around AI itself concerns the resultant software tooling that tech vendors bring to market. We want to know more about how so-called'explainable' AI functions function and what those advancements can do for us. A key part of that explainability concentrates on AI bias and the need to ensure human unconscious (or perhaps semiconscious) thinking is not programmed into the systems we are creating.


Practical Use Cases of Artificial Intelligence in Marketing

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The use case for Artificial Intelligence (AI) in the workplace is there. Deloitte's Tech Trends 2021 found AI and machine learning technologies are helping financial services firm Morgan Stanley use decades of data to supplement human insight with accurate models for fraud detection and prevention, sales and marketing automation, and personalized wealth management, among others. For marketing and customer experience, in particular, organizations are using AI and machine learning to improve internal business processes and workflow, automating repetitive tasks and to improve customer journeys and touchpoints, among other use cases. The CMO Survey by Duke University reports a steady increase as far as the extent to which companies are reporting implementing AI or ML into their marketing toolkits. However, the majority of marketers know AI is very important or critical to their success this year, according to Paul Roetzer, founder and CEO of the Marketing AI Institute and PR 20/20.


Why inclusivity is studentship rather than leadership

ZDNet

Tom Peters, author of Excellence Now: Extreme Humanism speaks about the importance and value of diversity and inclusion for hiring and team building, performance management, and lifelong learning.


TechDay - Top 5 Machine Learning Libraries Today

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With the use of Machine Learning (ML) on the rise, it is more important than ever to take a look at the leading five ML libraries being used today. But before we get into that, let’s look at what is an ML library? A Machine Learning library, or a Machine Learning framework, is a set of routines and functions that are written in a given programming language. Essentially, they are interfaces, libraries or tools helping developers to easily and quickly build machine learning models, going past the specific basic details of the underlying algorithms. So they basically help developers carry out complex tasks without having to rewrite many lines of code. Now let us look at the five best ML libraries out there for developers today: 1. TensorFlow Created by the Google Brain team, TensorFlow is a free and open-source software library used for research and production. Allowing easy and effective implementation of machine learning algorithms, it is an efficient math library and is also used for machine learning applications such as neural networks. The emergence of high-level APIs (Application Programming Interfaces) like Keras and Theano has made TensorFlow more effective in improving the capability of computers to predict solutions with a greater degree of accuracy. Bear in mind that TensorFlow offers stable APIs for Python and C. Providing parallel processing, it is easily trainable on CPU as well as GPU (Graphics Processing Unit) for distributed computing and enjoys a large community support with additional advantages including better computational graph visualizations, quick updates, frequent new releases with new features, good debugging methods and scalability. 2. Keras Several back-end neural network computation engines are supported by Keras, an open-source neural network library written in Python. It can run on top of frameworks such as TensorFlow, Microsoft Cognitive Toolkit, Theano. Keras has many impressive features. First is modularity, where a model can be understood as a sequence or a graph alone, next is minimalism so that the library shares just enough to get an outcome and there’s also the element of maximizing readability and extensibility which allows researchers to do more trials. Its advantages include its support for a wide range of production deployment options and integration with back-end engines/frameworks; it also helps that everything in Keras is native Python. Kids and teens interested in learning TensorFlow and Keras can join the YoungWonks afterschool coding program. Here, they will first get to learn the basics of Python and work their way up to learn about the two ML libraries in live online classroom sessions focusing on project-based and self-paced learning. 3. Scikit-learn Scikit-learn is a free machine learning library for Python built on SciPy. An effective tool for data mining and data analysis, it is used today for model selection, clustering, preprocessing, and more. Its popularity can be traced to the fact that it boasts a clean API, is easy to use, fast, comprehensive and enjoys good documentation and the support of an active developer community. It also scores well on the simplicity and accessibility front. 4. Theano Also a Python ML library, Theano is an open-source project developed by Montreal Institute for Learning Algorithms (MILA) at the Université de Montréal. It allows developers to define, optimize and evaluate mathematical expressions that include multi-dimensional arrays. It provides features such as good integration with NumPy, transparent use of a GPU, extensive unit-testing, and self-verification. 5. PyTorch Developed by Facebook’s AI Research lab (FAIR), PyTorch is used for applications like computer vision and natural language processing. Also a free and open-source software, it has a polished Python interface along with a C++ interface. PyTorch offers Tensor computing (like NumPy) with strong acceleration via graphics processing units (GPU) and today many deep learning softwares have been/ are being built on top of PyTorch.


Coding a deep learning model using TensorFlow.js

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In the previous tutorial "An introduction to AI in Node.js", we explained two basic approaches for embedding a deep learning model in your Node.js application. In this tutorial, we go a step further and show you how to build and train a simple deep learning model from scratch. Therefore, unlike the previous tutorial, you need a more in-depth understanding of how deep learning models work to get the most benefit from this tutorial. We start with the programming concepts for deep learning and cover two different programming APIs: the high-level Layers API and the low-level Core API. You'll code a simple model to classify clothing items, train it with a small data set, and evaluate the model's accuracy. Then, to illustrate a common practice in deep learning, you'll take your trained model and apply transfer learning to teach the model to classify new items. We also describe how to take a pre-trained model from other sources such as Python and convert it to a format that can be used in JavaScript. So far, we have seen that the actual deep learning model can be hidden in an npm package, loaded from a binary format, or served through a REST API. In these cases, we are simply running an inference on the model, and we don't care how the model was implemented.


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Understanding dimensionality reduction in machine learning models

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Machine learning algorithms have gained fame for being able to ferret out relevant information from datasets with many features, such as tables with dozens of rows and images with millions of pixels. Thanks to advances in cloud computing, you can often run very large machine learning models without noticing how much computational power works behind the scenes. But every new feature that you add to your problem adds to its complexity, making it harder to solve it with machine learning algorithms. Data scientists use dimensionality reduction, a set of techniques that remove excessive and irrelevant features from their machine learning models. Dimensionality reduction slashes the costs of machine learning and sometimes makes it possible to solve complicated problems with simpler models. Machine learning models map features to outcomes.


Artificial Intelligence

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Learn to write programs using the foundational AI algorithms powering everything from NASA's Mars Rover to DeepMind's AlphaGo Zero. Learn to write AI programs using the algorithms powering everything from NASA's Mars Rover to DeepMind's AlphaGo Zero.


How Can I Tell If My Machine Learning Model Is Working For Me?

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How can I tell if my machine learning model is working for me? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. Businesses must eventually sustain themselves beyond external funding sources by turning profits. What's talked about less than ML itself is how one can leverage machine learned models to generate profits. I've got a whole sub-section of the book that shows how to account for the revenues and costs of building a machine learning system, using traditional accounting concepts. Essentially, data learning loops bring about profit and create investment opportunities for the AI-First vendor: better predictions can lead to more automation, which lowers operating costs, which in turn means more gross profit that can be invested in research and development (models and data), leading to better predictions, and so on.