Foundations of mining non-structured medical data
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Course Summary
This course explores the process of mining medical data to extract valuable insights and information that can help improve patient outcomes and healthcare delivery. Students will learn how to use various data mining techniques to analyze electronic health records and other medical data sources.Key Learning Points
- Understand the fundamentals of data mining and how it can be applied in the healthcare industry
- Explore different data sources and learn how to extract, clean, and transform data for analysis
- Learn how to use machine learning algorithms to uncover patterns and insights in medical data
Job Positions & Salaries of people who have taken this course might have
- Healthcare Data Analyst
- USA: $67,000
- India: ₹5,50,000
- Spain: €35,000
- Healthcare Data Scientist
- USA: $112,000
- India: ₹10,50,000
- Spain: €45,000
- Healthcare Business Intelligence Analyst
- USA: $85,000
- India: ₹7,50,000
- Spain: €40,000
Related Topics for further study
- Healthcare Analytics
- Data Mining Techniques
- Electronic Health Records
- Machine Learning Algorithms
- Healthcare Business Intelligence
Learning Outcomes
- Ability to extract and transform medical data for analysis
- Understanding of data mining techniques and machine learning algorithms
- Experience in applying data mining to healthcare data
Prerequisites or good to have knowledge before taking this course
- Familiarity with basic statistics and programming concepts
- Access to software such as R or Python
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
- Video lectures
Similar Courses
- Data Science in Healthcare
- Healthcare Informatics
Related Education Paths
- Certified Health Data Analyst (CHDA)
- Certified Healthcare Information Systems Security Practitioner (CHISSP)
Notable People in This Field
- Surgeon, Writer, and Public Health Researcher
- Cardiologist and Digital Medicine Researcher
Related Books
Description
The goal of this course is to understand the foundations of Big Data and the data that is being generated in the health domain and how the use of technology would help to integrate and exploit all those data to extract meaningful information that can be later used in different sectors of the health domain from physicians to management, from patients to care givers, etc. The course will offer to the student a high-level perspective of the importance of the medical context within the European context, the types of data that are managed in the health (clinical) context, the challenges to be addressed in the mining of unstructured medical data (text and image) as well as the opportunities from the analytical point of view with an introduction to the basics of data analytics field.
Outline
- Introduction
- Introduction
- Big Data (I)
- Big Data (II)
- Importance of medical domain in the European cost context
- Big Data in medical domain: opportunities and challenges
- Data generated in the health domain
- Introduction: evaluation test
- Challenges in unstructured data in health domain
- Challenges and problems in biomedical texts
- Challenges and problems in medical images
- Challenges in unstructured data in health: evaluation test
- NLP in medical domain
- Introduction to NLP pipeline and tasks
- Tools and frameworks: general purpose
- Tools and frameworks: medical domain
- Vocabularies and ontologies (I)
- Vocabularies and ontologies (II)
- EHR analysis: structure, content and challenges
- NLP in medical domain: evaluation test
- Medical Image Analysis
- Introduction of digital image basic concepts applied to medical image (I)
- Introduction of digital image basic concepts applied to medical image (II)
- Structuring image information
- Case of use: Digital pathology. An example for breast cancer histological images
- Medical image analysis: evaluation test
- Data Analysis of structured information
- Data mining problems and techniques
- Data mining basics (I)
- Data mining basics (II)
- Classification (I)
- Classification (II)
- Clustering
- Association
- Validation
- Data analysis: evaluation test
Summary of User Reviews
Explore the world of mining medical data with this comprehensive course on Coursera. Users have found this course informative and engaging, with a particular focus on practical applications. Overall, the course has received positive reviews from learners.Key Aspect Users Liked About This Course
The practical application of the course material.Pros from User Reviews
- Clear and engaging course material
- Well-structured and informative
- Practical application of concepts
- Expert instructors
- Great for beginners and intermediate learners
Cons from User Reviews
- Some users may find the course too basic
- Lacks advanced topics and case studies
- Course content may be too technical for some learners
- Some users have reported technical issues with the platform
- Limited interaction with other learners and instructors
Alejandro RodrÃguez González
4 Raiting
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Learn Data Science, Deep Learning, Machine Learning, Natural Language Processing, R and Python Language with libraries || DATA SCIENCE || What Does a Data Scientist Do?...
Introduction to Clinical Data
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This course introduces you to a framework for successful and ethical medical data mining. We will explore the variety of clinical data collected during the delivery of healthcare....
Foundations of mining non-structured medical data
- 4
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Introduction Introduction Big Data (I) Big Data (II) Importance of medical domain in the European cost context Big Data in medical domain: opportunities and challenges...
