Title |
Beyond playing games: nephrologist vs machine in pediatric dialysis prescribing
|
---|---|
Published in |
Pediatric Nephrology, July 2018
|
DOI | 10.1007/s00467-018-4021-4 |
Pubmed ID | |
Authors |
Wesley Hayes, Marco Allinovi |
Abstract |
In a recent article in Pediatric Nephrology, Olivier Niel and colleagues applied an artificial intelligence algorithm to a clinical problem that continues to challenge experienced pediatric nephrologists: optimizing the target weight of children on dialysis. They compared blood pressure, antihypertensive medication and intradialytic symptoms in children whose target weight was prescribed firstly by a nephrologist, then subsequently using a machine learning algorithm. Improvements in all outcome measures are reported. Their innovative approach to tackling this important clinical problem appears promising. In this editorial, we discuss the strengths and weaknesses of their study and consider to what extent machine learning strategies are suited to optimizing pediatric dialysis outcomes. |
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Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 10% |
Russia | 1 | 10% |
Mexico | 1 | 10% |
Canada | 1 | 10% |
Unknown | 6 | 60% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 6 | 60% |
Practitioners (doctors, other healthcare professionals) | 2 | 20% |
Scientists | 1 | 10% |
Science communicators (journalists, bloggers, editors) | 1 | 10% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 36 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 8 | 22% |
Student > Ph. D. Student | 5 | 14% |
Student > Doctoral Student | 3 | 8% |
Student > Bachelor | 3 | 8% |
Student > Master | 3 | 8% |
Other | 5 | 14% |
Unknown | 9 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 10 | 28% |
Nursing and Health Professions | 4 | 11% |
Engineering | 2 | 6% |
Business, Management and Accounting | 1 | 3% |
Agricultural and Biological Sciences | 1 | 3% |
Other | 5 | 14% |
Unknown | 13 | 36% |