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Markov Models

Analyzing Patient Trajectories With Artificial Intelligence


For example, electronic health records store the history of a patient's diagnoses, medications, laboratory values, and treatment plans [1-3]. Wearables collect granular sensor measurements of various neurophysiological body functions over time [4-6]. Intensive care units (ICUs) monitor disease progression via continuous physiological measurements (eg, electrocardiograms) [7-10]. As a result, patient data in digital medicine are regularly of longitudinal form (ie, consisting of health events from multiple time points) and thus form patient trajectories. Analyzing patient trajectories provides opportunities for more effective care in digital medicine [2,7,11]. Patient trajectories encode rich information on the history of health states that are also predictive of the future course of a disease (eg, individualized differences in disease progression or responsiveness to medications) [9,10,12]. As such, it is possible to construct patient trajectories that capture the entire disease course and characterize the many possible disease progression patterns, such as recurrent, stable, or rapidly deteriorating disease states (Figure 1). Hence, modeling the patient trajectories allows one to build robust models of diseases that capture disease dynamics seen in patient trajectories. Here, we replace disease models with data from only a single or a small number of time points by disease models that account for the longitudinal nature of patient trajectories, thus offering vast potential for digital medicine. Several studies have previously introduced artificial intelligence (AI) in medicine for practitioners [13,14].

Unsupervised Machine Learning Hidden Markov Models in Python


Created by Lazy Programmer Inc. English [Auto-generated], Portuguese [Auto-generated] Preview this Udemy Course - GET COUPON CODE Description The Hidden Markov Model or HMM is all about learning sequences. A lot of the data that would be very useful for us to model is in sequences. Stock prices are sequences of prices. Language is a sequence of words. Credit scoring involves sequences of borrowing and repaying money, and we can use those sequences to predict whether or not you're going to default.

Discrete Markov chains


The discrete case, generally known as a Markov chain, is discussed on this page. The Markov approach can be applied to the random behaviour of systems that vary discretely or continuously with respect to time and space. This discrete or continuous random variable is known as a stochastic process. Not all stochastic processes can be modelled using the basic Markov approach although there are techniques available for modelling some additional stochastic processes using extensions of this basic method. In order for the basic Markov approach to be applicable, the behaviour of the system must be characterized by a lack of memory, that is, the future states of a system are independent of all past states' except the immediately preceding one.

PRISM: A Hierarchical Intrusion Detection Architecture for Large-Scale Cyber Networks


The increase in scale of cyber networks and the rise in sophistication of cyber-attacks have introduced several challenges in intrusion detection. The primary challenge is the requirement to detect complex multi-stage attacks in realtime by processing the immense amount of traffic produced by present-day networks. In this paper we present PRISM, a hierarchical intrusion detection architecture that uses a novel attacker behavior model-based sampling technique to minimize the realtime traffic processing overhead. PRISM has a unique multi-layered architecture that monitors network traffic distributedly to provide efficiency in processing and modularity in design. PRISM employs a Hidden Markov Model-based prediction mechanism to identify multi-stage attacks and ascertain the attack progression for a proactive response. Furthermore, PRISM introduces a stream management procedure that rectifies the issue of alert reordering when collected from distributed alert reporting systems. To evaluate the performance of PRISM, multiple metrics has been proposed, and various experiments have been conducted on a multi-stage attack dataset. The results exhibit up to 7.5x improvement in processing overhead as compared to a standard centralized IDS without the loss of prediction accuracy while demonstrating the ability to predict different attack stages promptly.

The Complete Neural Networks Bootcamp: Theory, Applications


Including NLP and Transformers Students also bought Recommender Systems and Deep Learning in Python Machine Learning A-Z: Become Kaggle Master Unsupervised Deep Learning in Python Deep Learning: Recurrent Neural Networks in Python Unsupervised Machine Learning Hidden Markov Models in Python Deep Learning: Convolutional Neural Networks in Python Preview this Udemy Course GET COUPON CODE Description This course is a comprehensive guide to Deep Learning and Neural Networks. The theories are explained in depth and in a friendly manner. After that, we'll have the hands-on session, where we will be learning how to code Neural Networks in PyTorch, a very advanced and powerful deep learning framework! We will walk through an example and do the calculations step-by-step. We will also discuss the activation functions used in Neural Networks, with their advantages and disadvantages!

Self Learning AI-Agents Part I: Markov Decision Processes


A Markov Decision Processes (MDP) is a discrete time stochastic control process. MDP is the best approach we have so far to model the complex environment of an AI agent. Every problem that the agent aims to solve can be considered as a sequence of states S1, S2, S3, … Sn (A state may be for example a Go/chess board configuration). The agent takes actions and moves from one state to an other. In the following you will learn the mathematics that determine which action the agent must take in any given situation.

Machine Learning for Genomic Data


This report explores the application of machine learning techniques on short timeseries gene expression data. Although standard machine learning algorithms work well on longer time-series', they often fail to find meaningful insights from fewer timepoints. In this report, we explore model-based clustering techniques. We combine popular unsupervised learning techniques like K-Means, Gaussian Mixture Models, Bayesian Networks, Hidden Markov Models with the well-known Expectation Maximization algorithm. K-Means and Gaussian Mixture Models are fairly standard, while Hidden Markov Model and Bayesian Networks clustering are more novel ideas that suit time-series gene expression data.

Unsupervised Deep Learning in Python


Free Coupon Discount - Theano / Tensorflow: Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA Created by Lazy Programmer Inc. Students also bought Artificial Intelligence: Reinforcement Learning in Python Advanced AI: Deep Reinforcement Learning in Python Machine Learning A-Z: Hands-On Python & R In Data Science Learn Python Programming Masterclass Complete Python Developer in 2020: Zero to Mastery Preview this Udemy Course GET COUPON CODE Description This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? In these course we'll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding). Next, we'll look at a special type of unsupervised neural network called the autoencoder.

Deep Learning: Recurrent Neural Networks in Python


The Recurrent Neural Network (RNN) has been used to obtain state-of-the-art results in sequence modeling. This includes time series analysis, forecasting and natural language processing (NLP). Learn about why RNNs beat old-school machine learning algorithms like Hidden Markov Models. The basics of machine learning and neurons (just a review to get you warmed up!) Neural networks for classification and regression (just a review to get you warmed up!) How to predict stock prices and stock returns with LSTMs in Tensorflow 2 (hint: it's not what you think!) All of the materials required for this course can be downloaded and installed for FREE.