Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge
Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine learning and artificial intelligence applications that can harness this `data deluge'. This broad nontechnical overvie...
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creator | Marcinkevičs, Ričards Ozkan, Ece Vogt, Julia E |
description | Many modern research fields increasingly rely on collecting and analysing
massive, often unstructured, and unwieldy datasets. Consequently, there is
growing interest in machine learning and artificial intelligence applications
that can harness this `data deluge'. This broad nontechnical overview provides
a gentle introduction to machine learning with a specific focus on medical and
biological applications. We explain the common types of machine learning
algorithms and typical tasks that can be solved, illustrating the basics with
concrete examples from healthcare. Lastly, we provide an outlook on open
challenges, limitations, and potential impacts of machine-learning-powered
medicine. |
doi_str_mv | 10.48550/arxiv.2212.12303 |
format | Article |
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massive, often unstructured, and unwieldy datasets. Consequently, there is
growing interest in machine learning and artificial intelligence applications
that can harness this `data deluge'. This broad nontechnical overview provides
a gentle introduction to machine learning with a specific focus on medical and
biological applications. We explain the common types of machine learning
algorithms and typical tasks that can be solved, illustrating the basics with
concrete examples from healthcare. Lastly, we provide an outlook on open
challenges, limitations, and potential impacts of machine-learning-powered
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massive, often unstructured, and unwieldy datasets. Consequently, there is
growing interest in machine learning and artificial intelligence applications
that can harness this `data deluge'. This broad nontechnical overview provides
a gentle introduction to machine learning with a specific focus on medical and
biological applications. We explain the common types of machine learning
algorithms and typical tasks that can be solved, illustrating the basics with
concrete examples from healthcare. Lastly, we provide an outlook on open
challenges, limitations, and potential impacts of machine-learning-powered
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massive, often unstructured, and unwieldy datasets. Consequently, there is
growing interest in machine learning and artificial intelligence applications
that can harness this `data deluge'. This broad nontechnical overview provides
a gentle introduction to machine learning with a specific focus on medical and
biological applications. We explain the common types of machine learning
algorithms and typical tasks that can be solved, illustrating the basics with
concrete examples from healthcare. Lastly, we provide an outlook on open
challenges, limitations, and potential impacts of machine-learning-powered
medicine.</abstract><doi>10.48550/arxiv.2212.12303</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Learning Statistics - Machine Learning |
title | Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge |
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