AI and Machine Learning in Healthcare

  

AI and Machine Learning in Healthcare

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Artificial intelligence and machine learning in healthcare will continue to get better and impact disease prevention and diagnosis, extract more meaning from data across various clinical trials, help develop customised drugs based on an individual's unique DNA and inform treatment options among other things.

What you'll learn

• Artificial intelligence, Machine Learning and Human Systems

Course content
9 sections • 11 lectures • 37m total length

Requirements

• No

Description

2020 No-Code Machine Learning Using IBM Watson AutoAI
Learn to Build and Deploy Machine Learning Models Without Any Coding (Zero-Code Approach to AI & ML) New

Evaluation of AI solutions in Healthcare.

To evaluate an AI solution beyond its predictive value, we need a framework that provides criteria to evaluate the utility, feasibility, and clinical impact of an AI solution.

Machine learning is a family of statistical and mathematical modelling techniques that uses a variety of approaches to automatically learn and improve the prediction of a target objective without explicit programming. These are the systems that improve their performance in a given task through exposure to experience or data.

Different neurotransmitters have different effects on the postsynaptic cell. There's a great number of categories of neurotransmitters. Some fall in the category of amino acids, like glutamate, aspartate, glycine and D-serine. Some of them are peptides, like somatostatin, vasopressin, oxytocin, and opioid peptides. And there are other categories, that includes serotonin or epinephrine, histamine, melatonin and others. In fact, overall there are over 50 neurotransmitters that have been identified, each having slightly different neurochemistry and each having slightly different functions

Who this course is for:

• All Levels

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