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Analyzing Patient Trajectories With Artificial Intelligence

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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].


10 Mathematics for Data Science Free Courses You Must Know in 2022

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Knowledge of Mathematics is essential to understand the data science basics. So if you want to learn Mathematics for Data Science, this article is for you. In this article, you will find the 10 Best Mathematics for Data Science Free Courses. For these courses, You don't need to pay a single buck. Now, without any further ado, let's get started- This is a completely FREE course for beginners and covers data visualization, probability, and many elementary statistics concepts like regression, hypothesis testing, and more.


Self Learning AI-Agents Part I: Markov Decision Processes

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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.


Steven Pinker Has His Reasons - Issue 108: Change

Nautilus

A few years ago, at the Princeton Club in Manhattan, I chanced on a memorable chat with the Harvard psychologist Steven Pinker. His spouse, the philosopher Rebecca Goldstein, with whom he was tagging along, had been invited onto a panel to discuss the conflict between religion and science and Einstein's so-called "God letter," which was being auctioned at Christie's. Pinker had recently published Enlightenment Now: The Case for Reason, Science, Humanism, and Progress. I was eager to pepper him with questions, mainly on religion, rationality, and evolutionary psychology. I remember I wanted Pinker's take on something Harvey Whitehouse, one of the founders of the cognitive science of religion, told me in an interview--that my own little enlightenment, of becoming an atheist in college, was probably mostly a product of merely changing my social milieu. I wasn't so much moved by rational arguments against the ethics and existence of God but by being distanced from my old life and meeting new, non-religious friends. I recall Pinker almost pouncing on that argument, defending reason's power to change our minds. He noted that people especially high in "intellectance," a personality trait now more commonly called "openness to experience," tend to be more curious, intelligent, and willing to entertain new ideas. I still think that Pinker's way of seeing things made more sense of my experience in those heady days. I really was, for the first time, trying my best to think things through, and it was exhilarating. We talked until the event staff shelved the wine, and parted ways at a chilly midtown intersection.


Making RL tractable by learning more informative reward functions: example-based control, meta-learning, and normalized maximum likelihood

AIHub

After the user provides a few examples of desired outcomes, MURAL automatically infers a reward function that takes into account these examples and the agent's uncertainty for each state. Although reinforcement learning has shown success in domains such as robotics, chip placement and playing video games, it is usually intractable in its most general form. In particular, deciding when and how to visit new states in the hopes of learning more about the environment can be challenging, especially when the reward signal is uninformative. These questions of reward specification and exploration are closely connected -- the more directed and "well shaped" a reward function is, the easier the problem of exploration becomes. The answer to the question of how to explore most effectively is likely to be closely informed by the particular choice of how we specify rewards.


Naive Bayes Algorithm

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This formula was devised and penned by respected Thomas Bayes, renowned statistician.It is an arithmetical formula for determining conditional probability. Conditional probability is the likelihood of an outcome occurring, based on a previous outcome occurring. This might be a bit brain-teasing as you are working backwards. Bayes' theorem may be derived from the definition of conditional probability, P(Do not launch Stock price increases) 0.4 0.30 0.12 Thus, there is a 57% probability that the company's share price will increase. Bayes' Theorem has several forms.


BEGINNERS' GLOSSERY OF AI

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My old account got hacked and it can't be accessed now. Machine Learning (ML) is a convenient way to describe classes of algorithms that are used to gain insight into data in a way that allows a certain amount self-instruction which, if properly designed & trained, achieves a robustness to changes in initial conditions that are lacking in other types of analytic methods. Regression is a general term describing a model that explicitly defines a relationship between features of interest and a target. The term is most often used when the target is a continuous numeric dependent variable. Deep learning is a subset of ML approaches.


Probabilistic Deep Learning for Wind Turbines

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Model speed can be a deal breaker on large datasets. Leveraging an empirical study, we will look at two dimension reduction techniques and how they can be applied to a Gaussian Processes. Regarding implementation of the method, anyone familiar with the basics of conditional probability can develop a Gaussian Process model. However, to fully leverage the capabilities of the framework, a fair amount of in-depth knowledge is required. Gaussian processes also are not very computationally efficient, but their flexibility is makes them a common choice for niche regression problems.


Making RL tractable by learning more informative reward functions: example-based control, meta-learning, and normalized maximum likelihood

Robohub

After the user provides a few examples of desired outcomes, MURAL automatically infers a reward function that takes into account these examples and the agent's uncertainty for each state. Although reinforcement learning has shown success in domains such as robotics, chip placement and playing video games, it is usually intractable in its most general form. In particular, deciding when and how to visit new states in the hopes of learning more about the environment can be challenging, especially when the reward signal is uninformative. These questions of reward specification and exploration are closely connected -- the more directed and "well shaped" a reward function is, the easier the problem of exploration becomes. The answer to the question of how to explore most effectively is likely to be closely informed by the particular choice of how we specify rewards.


8 Terms You Should Know about Bayesian Neural Network

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From the previous article, we know that Bayesian Neural Network would treat the model weights and outputs as variables. Instead of finding a set of optimal estimates, we are fitting the probability distributions for them. But the problem is "How can we know what their distributions look like?" To answer this, you have to learn what prior, posterior, and Bayes' theorem are. In the following, we will use an example for illustration.