New algorithm will improve bowel-cancer patient care
Date:
March 30, 2022
Source:
University of Portsmouth
Summary:
An algorithm which can predict how long a patient might spend in
hospital if they're diagnosed with bowel cancer could save the
money and help patients feel better prepared.
FULL STORY ==========================================================================
An algorithm which can predict how long a patient might spend in hospital
if they're diagnosed with bowel cancer could save the NHS millions of
pounds and help patients feel better prepared.
========================================================================== Experts from the University of Portsmouth and the Portsmouth Hospitals University NHS Trust have used artificial intelligence and data analytics
to predict the length of hospital stay for bowel cancer patients,
whether they will be readmitted after surgery, and their likelihood of
death over a one or three-month period.
The intelligent model will allow healthcare providers to design the best patient care and prioritise resources.
Bowel cancer is one of the most common types of cancer diagnosed in the
UK, with more than 42,000 people diagnosed every year.
Professor of Intelligent Systems, Adrian Hopgood, from the University
of Portsmouth, is one of the lead authors on the new paper. He said:
"It is estimated that by 2035 there will be around 2.4 million new cases
of bowel cancer annually worldwide. This is a staggering figure and one
that can't be ignored. We need to act now to improve patient outcomes.
"This technology can give patients insight into what they're likely to experience. They can not only be given a good indication of what their
longer- term prognosis is, but also what to expect in the shorter term.
==========================================================================
"If a patient isn't expecting to find themselves in hospital for two
weeks and suddenly they are, that can be quite distressing. However,
if they have a predicted length of stay, they have useful information
to help them prepare.
"Or indeed if a patient is given a prognosis that isn't good or they
have other illnesses, they might decide they don't want a surgical
option resulting in a long stay in hospital." Bowel cancer (also known a colorectal cancer) affects the large bowel, which is made up of the colon
and rectum. The cost of diagnosing and treating patients is significant
and the economic impact on healthcare systems is immense.
The study used data taken from a database of over 4,000 bowel cancer
patients who underwent surgery between 2003 and 2019. It looked at 47
different variables including age, weight, fitness, surgical approaches,
and mortality.
The insights of consultant surgeon Jim Khan and his colleagues Samuel
Stefan and Karen Flashman were complemented by the analytical expertise
of Dr Shamsul Masum, under Professor Hopgood's direction.
Professor Hopgood said: "We used a full set of data that included
the 47 variables, but also predicted outcomes with just some of
the most significant ones and found the two approaches showed very
little difference. This is useful in itself because it shows that the
algorithm is just as effective using a streamlined set of variables."
The technology could be rolled out straightaway in principle, but would
need to be approved for use in a clinical setting. However, Professor
Hopgood is keen to work with an even bigger dataset to improve the
accuracy of predictions, which is already above 80 per cent.
"If we could attract funding, we would love to get together with other
bowel cancer centres so we have access to even bigger datasets. With
machine learning, the simple rule is the more data the better," he said.
"Everyone I've spoken to in the health domain thinks that artificial intelligence will help them do a better job and we hope this research
will do exactly that -- by providing more accurate predictions, the
health service can allocate the best resources to each patient and
improve patient care."
========================================================================== Story Source: Materials provided by University_of_Portsmouth. Note:
Content may be edited for style and length.
========================================================================== Journal Reference:
1. Shamsul Masum, Adrian Hopgood, Samuel Stefan, Karen Flashman,
Jim Khan.
Data analytics and artificial intelligence in predicting length
of stay, readmission, and mortality: a population-based study of
surgical management of colorectal cancer. Discover Oncology, 2022;
13 (1) DOI: 10.1007/s12672-022-00472-7 ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2022/03/220330103218.htm
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