Introduction

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Из курса от партнера The University of Edinburgh

Data Science in Stratified Healthcare and Precision Medicine

18 оценки

The University of Edinburgh

Data Science in Stratified Healthcare and Precision Medicine

18 оценки

An increasing volume of data is becoming available in biomedicine and healthcare, from genomic data, to electronic patient records and data collected by wearable devices. Recent advances in data science are transforming the life sciences, leading to precision medicine and stratified healthcare. In this course, you will learn about some of the different types of data and computational methods involved in stratified healthcare and precision medicine. You will have a hands-on experience of working with such data. And you will learn from leaders in the field about successful case studies. Topics include: (i) Sequence Processing, (ii) Image Analysis, (iii) Network Modelling, (iv) Probabilistic Modelling, (v) Machine Learning, (vi) Natural Language Processing, (vii) Process Modelling and (viii) Graph Data. Watch the course promo video here: http://edin.ac/2pn350P

Из урока

WELCOME TO WEEK 4

This week you will discover how clinical notes and other free-form text can be analysed with the use of Natural Language Processing techniques. You will also find out how Process Modelling can help us understand, stratify and improve healthcare processes.

Introduction2:48

Computational Methods4:10

Познакомьтесь с преподавателями

Dr Areti Manataki
Teaching and Research Fellow
Centre for Medical Informatics
Dr Frances Wong
Data Science MOOC Project Lead
College of Science and Engineering

Personal medical records contain important information on an individual's health.

For example, medications, immunization records,

lab results, and screening due dates.

Many medical professionals are interested

in extracting such information from clinical text.

However, a great deal of

such information is not structured in a table with rules and columns,

but instead can be classed as free form text.

Unfortunately, processing text in an automated fashion and extracting meaning

from it is very challenging, mainly due to the ambiguity of natural language.

Also, it is a complex task that requires several other tasks to be carried out beforehand.

However, natural language processing methods can be used to process, analyze,

and interpret clinical text, allowing us to

answer important questions regarding an individual's health.

A specific example of clinical text can be found in pathology reports.

A pathology report is a document that contains the diagnosis

determined by examining cells and tissues under a microscope.

And may also contain a description of the size,

shape, and appearance of a specimen as seen by the naked eye.

Pathologists examine samples for

abnormalities but quite often these tests are done to confirm that everything is okay.

For instance, these tests are used for preventative screenings.

Preventative screenings are important as they can help with

the early detection of potentially fatal conditions.

An individual's screening results are recorded in a pathology report.

Pathology reports also play an important role

in cancer diagnosis and the staging of cancers.

The staging of a tumor determines the extent of

cancer within the body and whether it has spread.

This allows for a more informed decision to be made when considering treatment options.

How can the retrieval of clinical text information be improved?

Well, natural language processing methods enable

relevant information from clinical text, to be retrieved in a timely manner,

and this is an important step towards precision medicine.

In the next few videos,

we are going to talk about natural language processing tasks

relating them to clinical text,

such as a pathology report, and computational methods used in natural language processing.

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