Artificial intelligence tools quickly detect signs of injection drug use in patients' health records

Artificial intelligence tools quickly detect signs of injection drug use in patients' health records

2 years ago
Anonymous $CIOZ56dzxX

https://www.sciencedaily.com/releases/2022/09/220921210130.htm

Currently, people who inject drugs are identified through International Classification of Diseases (ICD) codes that are specified in patients' electronic health records by the healthcare providers or extracted from those notes by trained human coders who review them for billing purposes. But there is no specific ICD code for injection drug use, so providers and coders must rely on a combination of non-specific codes as proxies to identify PWIDs -- a slow approach that can lead to inaccuracies.

The researchers manually reviewed 1,000 records from 2003-2014 of people admitted to Veterans Administration hospitals with Staphylococcus aureus bacteremia, a common infection that develops when the bacteria enters openings in the skin, such as those at injection sites. They then developed and trained algorithms using natural language processing and machine learning and compared them with 11 proxy combinations of ICD codes to identify PWIDs.

Artificial intelligence tools quickly detect signs of injection drug use in patients' health records

Sep 22, 2022, 5:42pm UTC
https://www.sciencedaily.com/releases/2022/09/220921210130.htm > Currently, people who inject drugs are identified through International Classification of Diseases (ICD) codes that are specified in patients' electronic health records by the healthcare providers or extracted from those notes by trained human coders who review them for billing purposes. But there is no specific ICD code for injection drug use, so providers and coders must rely on a combination of non-specific codes as proxies to identify PWIDs -- a slow approach that can lead to inaccuracies. > The researchers manually reviewed 1,000 records from 2003-2014 of people admitted to Veterans Administration hospitals with Staphylococcus aureus bacteremia, a common infection that develops when the bacteria enters openings in the skin, such as those at injection sites. They then developed and trained algorithms using natural language processing and machine learning and compared them with 11 proxy combinations of ICD codes to identify PWIDs.