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Implementation of an Automated Pediatric Malnutrition Screen Using Anthropometric Measurements in the #EHR | Salud Publica | Scoop.it

From jandonline.org - October 21, 2018 10:31 PM

Nutrition evaluation and intervention in hospitalized pediatric patients is critical, because undernutrition negatively impacts physical and cognitive development, wound healing, immune function, mortality, and quality of life.

Multiple, validated pediatric nutrition screening tools are available, yet no consensus on the ideal tool exists.

Generally, the aims of the nutrition screening process are identification of current nutrition status and determination of a need for further nutrition assessment and intervention.

Children’s Hospital of Philadelphia has developed what it says is the first automated pediatric malnutrition screening tool using EHR data.

In this study the tool was used to analyze anthropometric measurements in the hospital’s Epic EHR—including body mass index, height, length and weight—for inpatients in the pediatric oncology unit at CHOP for a little more than a year, representing about 2,100 hospitalizations.

Researchers used software to take note of changes in the anthropometric measurements to assess each hospitalized patient’s risk of malnutrition. For those pediatric cancer patients determined to be at risk, the automated program categorized their risk as either mild, moderate or severe.

In the study, 47 percent were classified as at mild risk, 24 percent as moderate risk and 29 percent as severe—consistent with clinical experience and other research. In addition, the overall prevalence of malnutrition was determined to be 42 percent for the study period, which was also consistent with previous studies.

“This test study demonstrates the feasibility of using EHR data to create an automated screening tool for malnutrition in pediatric inpatients, Further research is needed to formally assess this screening tool, but it has the potential to identify at-risk patients in the early stages of malnutrition, so we can intervene quickly. In addition, this tool could be implemented to screen all pediatric patients for malnutrition, because it uses data common to all electronic medical records.”

ref: https://www.healthdatamanagement.com/news/chop-uses-ehr-data-to-identify-cancer-patients-for-malnutrition

study: https://jandonline.org/article/S2212-2672(18)30974-2/abstract

Via nrip

DeepMind furthering AI cancer research program with new partnership in Japan to refine breast cancer detection algorithms | Salud Publica | Scoop.it

From venturebeat.com - October 5, 2018 10:37 PM

Deep mind will use data available to it via a new partnership with Jikei University Hospital in Japan to refine its artificially intelligent (AI) breast cancer detection algorithms.

Google AI subsidiary DeepMind has partnered with Jikei University Hospital in Japan to analyze mammagrophy scans from 30,000 women.

DeepMind is furthering its cancer research efforts with a newly announced partnership.

The London-based Google subsidiary said it has been given access to mammograms from roughly 30,000 women that were taken at Jikei University Hospital in Tokyo, Japan between 2007 and 2018.

Deep mind will use that data to refine its artificially intelligent (AI) breast cancer detection algorithms.

Over the course of the next five years, DeepMind researchers will review the 30,000 images, along with 3,500 images from magnetic resonance imaging (MRI) scans and historical mammograms provided by the U.K.’s Optimam (an image database of over 80,000 scans extracted from the NHS’ National Breast Screening System), to investigate whether its AI systems can accurately spot signs of cancerous tissue.

read the original and complete story at https://venturebeat.com/2018/10/04/deepmind-expands-ai-cancer-research-program-to-japan/

Via nrip

nrip's curator insight, October 5, 2018 1:25 AM

Healthcare data is increasingly being analyzed and complex algorithms created to help various aspects of the healthcare ecosystem.

A big problem is the availability of huge data sets, and where available, the prevention of their misuse. Its great that Deepmind is able to source data sets , (being a sub of Google, am sure plays a role), and hopefully they will put their deep mind ;) to good use and be able to improve detection algorithms.

I have written previously on this, and it will be useful for patients and if the data sets do help create both faster as well as more accurate detection algorithms in the future.

Implementation of an Automated Pediatric Malnutrition Screen Using Anthropometric Measurements in the #EHR

DeepMind furthering AI cancer research program with new partnership in Japan to refine breast cancer detection algorithms