How Machines Work in Healthcare.pdf

Libria

271

7页

0次

2021-02-22

50墨值下载



How macHineS learn in HealtHcare

Machine learning is transforming every facet of healthcare, as computer

systems are being taught how to use Big Data to derive insights and support

decision making. In this respect, teaching a computer, no less than teaching

a child, is to “shape the future.” Educating a computer is a surprisingly labor-

intensive process, requiring massive amounts of data, a nuanced understanding

of every data element from every data source, years of trial and error, and

extensive domain expertise. The key differentiator in machine learning is not the

specic technology and science applied; it is in the volume and quality of the

instructional material and the knowledge of the instructor.

ADVANCES IN DATA SCIENCE

ANDREI STOICA, PHD







astoica@iqvia.com

Today, we’re surrounded by computing systems that

can learn from experience and handle new situations.



music curation, and virtual assistants, computers

are studying away, becoming “smarter” with each

interaction we have with them.

Machine learning is destined to accelerate the pace

of healthcare transformation, as it allows us to extract

meaning from otherwise insurmountable volumes

of data. It is proving valuable in supporting research



improving diagnostics, providing clinical decision





much the same way as humans. The ingredients







together in the right way, machines are able to perform

high-volume automation, recognize patterns, spot





outcomes with great reliability.





data than we could effectively move over the internet



data. Today, we are in a similar position with

machine learning. The term has been over-hyped by

vendors that have limited experience with healthcare









the entire process from data processing to analytics

and the intrinsic interdependencies between the

various stages underpinning the quality of results.

This article provides additional details about data

processing as the foundation for analytics.

GOOD DATA HYGIENE



calls for data of great breadth. Other times, for data

of great depth. But in most cases, and especially for

business critical decisions, the data must be clean.

That’s why most data mining systems that claim they



cleansing step prior to data processing. It’s worth

reviewing the three basic steps involved in data

cleansing and processing: bridging, coding and



they are the foundation for quality machine learning

in processing and analytics stages.



to entities (such as diagnoses, products, physicians,







hundreds of attributes, including details on the

procedures, imaging, notes, etc.

In some cases, there are standard codes by which

these entities can be referenced, such as the National





as this exist, they must be assigned to the entity in

the data record and subsequently validated. This

assignment is called bridging. If the entity does not

have a standard code, a unique one must be created

as a reference in a process called coding.

To prepare data for bridging and coding, simple



example, one basic rule might be to remove all extra



Machine learning requires human, healthcare knowledge

Machine learning is destined to accelerate the pace of healthcare transformation,

as it allows us to extract meaning from otherwise insurmountable volumes of data

continued on next page

