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Файл:Intelligent data analysis in medicine. Study aid
.pdf
M.V. Polyakov
INTELLIGENT DATA ANALYSIS
IN MEDICINE
Study aid
Translation from the Russian
Moscow
IPR Media
2024
М.В. Поляков
ИНТЕЛЛЕКТУАЛЬНЫЙ АНАЛИЗ
ДАННЫХ В МЕДИЦИНЕ
Учебное пособие
Москва
Ай Пи Ар Медиа
UDC 004.89
BBK 32.813.5
P 80
Author:
Polyakov M.V. — senior lecturer of the department information systems
and computer modeling of Volgograd State University
Reviewers:
Losev A.G. — Doctor of Physics and Mathematics, Professor, Director of Institute
of Mathematics and Information Technologies of Volgograd State University;
Zamechnik T.V. — Ph.D. of Medical Sciences, Associate Professor departments pathophysiology,
clinical pathophysiology of Volgograd State Medical University
Polyakov, Maxim Valentinovich.
P 80 Intelligent data analysis in medicine : study aid / M.V. Polyakov. — Moscow : IPR
Media, 2024. — 73 p. — Text: digital.
ISBN 978-5-4497-2556-1
The study aid covers the application of a number of end-to-end technologies given in
the National Program “Digital Economy of the Russian Federation”, such as: “Technologies
for storing and analyzing big data”, “Artificial intelligence”, “Machine learning technologies
and cognitive technologies”. The publication is dedicated to the use of machine learning
algorithms and data mining methods for processing the results of medical research. The main
tool is the high-level programming language Python, which is today the most powerful tool
for data processing and analysis.
The study aid is aimed at all the students, studying the following disciplines “Data
Mining”, “Intelligent Systems and Technologies”, “Methods for the Development and Design
of Expert Systems”, “Artificial Neural Networks”, “Artificial Intelligence Systems”.
Educational digital edition
ISBN 978-5-4497-2556-1 (en) © Поляков М.В., 2023
ISBN 978-5-4497-2104-4 (rus) © ООО Компания «Ай Пи Ар Медиа», 2023
© English edition, translation, design.
LLC “IPR Media”, 2024
Educational publication
Polyakov Maxim Valentinovich
Editor Yu.V. Semenova
Technical editors, computer layout E.O. Zueva, A.D. Talmayeva
Proofreaders E.V. Savenkova, Yu.V. Semenova
Cover by Y.A. Kirsanov, S.S. Siziumova, photo bank “Lori”
Signed for use on 30.11.2023. Data volume 7 Mb
LLC Company “IPR Media”
8 800 555 22 35 (toll-free within Russia)
E-mail: sale@iprmedia.ru
5
CONTENTS
INTRODUCTION ................................................................................................... 6
CHAPTER 1. ARTIFICIAL INTELLIGENCE
AND MACHINE LEARNING: APPLICATION AREAS ...................................... 8
CHAPTER 2. MEDICAL DATA AND THEIR FEATURES .............................. 15
CHAPTER 3. MACHINE LEARNING IN MEDICINE ...................................... 21
CHAPTER 4. MACHINE LEARNING ALGORITHMS ..................................... 31
CHAPTER 5. ARTIFICIAL INTELLIGENCE
ASSESSMENT METHODS ................................................................................... 51
CHAPTER 6. ETHICAL ISSUES IN APPLICATION
INTELLIGENT SYSTEMS IN MEDICINE .......................................................... 55
CHAPTER 7. IMPLEMENTATION OF INTELLIGENT SYSTEMS
INTO MEDICAL PRACTICE ................................................................................ 61
CHAPTER 8. EXAMPLES OF USE of INTELLIGENT SYSTEMS
IN MEDICINE ........................................................................................................ 69
REFERENCES ...................................................................................................... 72
6
INTRODUCTION
The idea of creating intelligent systems did not arise suddenly. It was developed
over many years. The evolution of computing technology has been significant over the
past few centuries, from the simplest abacus, the slide rule, to the analytical engine,
developed by Charles Babbage in the 1800s. In fact, once computers began to develop
with Babbage's invention of the Analytical Engine and the first computer program was
written by Ada Lovelace in 1842, people began to wonder and speculate whether
computers could actually become intelligent and begin solve problems autonomously
and completely independently. In fact, the English mathematician Alan Turing had a
huge influence on the development of modern theoretical computer science, algorithms
and formalized computer language already in the 1950s. He addressed concepts such
as “artificial intelligence” and “machine learning”. This brief overview of the evolution
of computing can give you an idea that artificial intelligence tasks have evolved
gradually and have reached their peak in modern times.
With faster computers, better processing methods, high computing power and
more memory, we are able to process significant amounts of experimental data,
including medical data [1]. Every day we are faced with managing big data and creating
intelligent systems using concepts and methodologies from data science, artificial
intelligence, data mining and machine learning. Of course, most of you have heard of
these terms. The main challenge that businesses and scientific organizations have faced
in the last decade is to use approaches to try to process and interpret all the data they
have, and apply valuable information and conclusions based on it to make better
decisions.
Indeed, with great advances in technology, including the availability of
computing resources, hardware (including GPUs) and storage, we are seeing the rise
of an ecosystem built around areas such as artificial intelligence, machine learning,
including deep learning [2]. Researchers, developers, data scientists and engineers are
working to research and create tools, frameworks, algorithms, methods and
methodologies to implement intelligent models and systems that can predict events in
various fields, automate performing complex tasks, detecting anomalies, interacting
with a person and even complementing him [3].
7
As a result of mastering the discipline, the student must:
1) know:
– natural science and general engineering knowledge, methods of mathematical
analysis and modeling, theoretical and experimental research in professional activities;
– principles and methods of collecting, selecting and summarizing information;
2) be able to:
– apply natural science and general engineering knowledge, methods of
mathematical analysis and modeling, theoretical and experimental research in
professional activities;
– analyze the problem, highlighting the basic components;
– search for information, critically analyze the information necessary to solve a
problem;
– propose possible options for solving the problem, assessing their advantages
and disadvantages;
3) have the skills of:
– working with information sources;
– scientific research;
– argumentation of the obtained conclusions and one’s own point of view.
8
CHAPTER 1. ARTIFICIAL INTELLIGENCE
AND MACHINE LEARNING APPLICATION AREAS
Modern concepts of artificial intelligence
Artificial intelligence is a field of computer science that deals with the
development of intelligent computer systems, i.e. systems that have capabilities that
we traditionally associate with the activities of the human mind [4].
Artificial intelligence (AI) is considered one of the most significant scientific
and technological achievements of our time. Virtual assistants can determine our
musical tastes with amazing accuracy, cars now can move without human intervention,
and mobile apps can diagnose diseases that were once considered difficult to diagnose
even with specialized medical equipment. Nevertheless, from the point of view of the
mathematical apparatus, there is nothing new in AI. AI technologies have been around
for decades. In fact, it is currently experiencing a renaissance, and is driven by the
availability of data and computing resources.
Researchers estimate that in the next decade there will be 150 billion networked
measurement sensors, which is 20 times the population of the Earth [5]. The
exponential growth of data allows us to develop more and more smart devices.
Smartphones, smart car washes, smart homes, cities and communities are increasingly
developing these days. With this data comes a plethora of learning opportunities and
hence the focus has now shifted to learning from available data and developing
intelligent systems. The more data a system is given, the greater its learning ability,
allowing it to become more accurate.
The use of AI and machine learning in enterprises is still relatively new,
especially in healthcare. Machine learning applications in healthcare appear to be
relatively new, interesting and innovative. Today, with more mobile devices than
people, the future of healthcare depends on data from the patient, the environment, and
the physician. As a result, there is a growing opportunity to optimize the healthcare
system using AI and machine learning. The implementation of AI and machine
learning in healthcare is nothing short of welcome in the current ecosystem as
healthcare costs are on the rise across the world. Costs should generally be reduced
without negatively impacting patients and their care.
9
The basic concepts of AI include agents developing independently, acquiring
knowledge, constructing reasoning, and finding solutions to problems. In particular,
AI can be considered to include:
– bringing the system to rational thinking. The methods contain automated
reasoning, construction of proofs, search for limitations and reasoning based on
precedents;
– creation of a program for training and further prediction. The methods include
machine learning, data mining and the search for new scientific knowledge.
Machine learning
Machine learning is a class of AI methods, the characteristic feature of which
is not the direct solution of a problem, but learning through the application of solutions
to many similar problems [6].
Machine learning is a term coined by Arthur Samuel (IBM), who in 1959
suggested that computers could be “taught” to recognize natural language, images and
other objects in the world around them, and also formulate tasks for themselves
independently. Machine learning can be understood as a particular application of AI.
It originated from the problem of pattern recognition and the theory that computers can
be trained to perform specific tasks based on specialized computer programs. Such
software is based on algorithms such as Bayesian methods, neural networks, inductive
logic programming, interpretation-based natural language processing, decision trees
and reinforcement learning.
Systems with hard-coded knowledge bases typically have difficulty deploying
in new environments. Some difficulties can be overcome by a system that is capable of
acquiring its own knowledge. This capability is known as machine learning (Fig. 1).
All this requires algorithms for acquiring knowledge, logical inference, updating and
clarifying the knowledge base, defining heuristics, etc.
All AI tasks use some form of data representation. Data science is an emerging
discipline that covers everything related to preparing, processing, extracting and
analyzing data. Data science is an umbrella term for a range of techniques used when
trying to extract insights and information from data, i.e. an attempt to find patterns in
the source data. The term “data science” was coined by William Cleveland in 2001 to
describe the academic discipline that combines statistics and computer science.
10
Fig. 1. Relationship between machine learning algorithms and artificial intelligence
with data science and the IT industry in general
Teams working on AI projects will undoubtedly interact with data, no matter
how large it is. In the case of big data obtained in real time, real-time analysis of this
data is usually required. In most business cases, a data scientist or data engineer has to
perform a variety of technical roles related to data manipulation, including searching,
interpreting and managing data, ensuring data consistency, building mathematical
models using data, presenting and communicating data, output of information to
interested parties. However, it is not always necessary to deal with big data, as a result
of which data analysis becomes a more accessible procedure. Because data mining
requires the use of statistical analysis techniques, many scientists do not recognize the
difference between data science and statistics. A team of data scientists (even if it's a
team of one) is fundamental to a successful project deployment.
Before the advent of smartphones and low-cost computers, data sets remained
limited in size, reliability, and representation. Today we have real-time data that can
be accessed instantly and tools that allow rapid analysis. The technologies used today
are flexible enough to access these huge data sets to rapidly develop machine learning
applications.
Artificial intelligence and machine learning in medicine
Both patients and healthcare providers are a source of enormous amounts of data.
Phones aggregate metrics such as blood pressure, geographic location, steps taken,
food diaries and other semi-structured data such as conversations, reactions and
images. It's not just digital or clinical metrics. Data extraction methods can be applied
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