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Sökning: onr:"swepub:oai:DiVA.org:su-223746" > The augmented value...

LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00004998naa a2200577 4500
001oai:DiVA.org:su-223746
003SwePub
008231117s2023 | |||||||||||000 ||eng|
009oai:prod.swepub.kib.ki.se:154046188
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-2237462 URI
0247 a https://doi.org/10.1186/s13756-023-01316-x2 DOI
0247 a http://kipublications.ki.se/Default.aspx?queryparsed=id:1540461882 URI
040 a (SwePub)sud (SwePub)ki
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Verberk, Janneke D. M.u University Medical Centre Utrecht, Utrecht, the Netherlands; National Institute for Public Health and the Environment, Bilthoven, the Netherlands4 aut
2451 0a The augmented value of using clinical notes in semi-automated surveillance of deep surgical site infections after colorectal surgery
264 1c 2023
338 a print2 rdacarrier
520 a BackgroundIn patients who underwent colorectal surgery, an existing semi-automated surveillance algorithm based on structured data achieves high sensitivity in detecting deep surgical site infections (SSI), however, generates a significant number of false positives. The inclusion of unstructured, clinical narratives to the algorithm may decrease the number of patients requiring manual chart review. The aim of this study was to investigate the performance of this semi-automated surveillance algorithm augmented with a natural language processing (NLP) component to improve positive predictive value (PPV) and thus workload reduction (WR).MethodsRetrospective, observational cohort study in patients who underwent colorectal surgery from January 1, 2015, through September 30, 2020. NLP was used to detect keyword counts in clinical notes. Several NLP-algorithms were developed with different count input types and classifiers, and added as component to the original semi-automated algorithm. Traditional manual surveillance was compared with the NLP-augmented surveillance algorithms and sensitivity, specificity, PPV and WR were calculated.ResultsFrom the NLP-augmented models, the decision tree models with discretized counts or binary counts had the best performance (sensitivity 95.1% (95%CI 83.5-99.4%), WR 60.9%) and improved PPV and WR by only 2.6% and 3.6%, respectively, compared to the original algorithm.ConclusionsThe addition of an NLP component to the existing algorithm had modest effect on WR (decrease of 1.4-12.5%), at the cost of sensitivity. For future implementation it will be a trade-off between optimal case-finding techniques versus practical considerations such as acceptability and availability of resources.
650 7a MEDICIN OCH HÄLSOVETENSKAPx Hälsovetenskap0 (SwePub)3032 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Health Sciences0 (SwePub)3032 hsv//eng
650 7a NATURVETENSKAPx Biologi0 (SwePub)1062 hsv//swe
650 7a NATURAL SCIENCESx Biological Sciences0 (SwePub)1062 hsv//eng
650 7a MEDICIN OCH HÄLSOVETENSKAPx Medicinska och farmaceutiska grundvetenskaper0 (SwePub)3012 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Basic Medicine0 (SwePub)3012 hsv//eng
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Infektionsmedicin0 (SwePub)302092 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Infectious Medicine0 (SwePub)302092 hsv//eng
653 a Automated surveillance
653 a Algorithm
653 a Colorectal surgery
653 a Healthcare-associated infections
653 a Natural language processing
653 a Surgical site infections
653 a data- och systemvetenskap
653 a Computer and Systems Sciences
7001 a van der Werff, Suzanne D.u Karolinska Institutet4 aut
7001 a Weegar, Rebecka,d 1982-u Stockholms universitet,Institutionen för data- och systemvetenskap4 aut0 (Swepub:su)rewe5142
7001 a Henriksson, Aron,d 1985-u Stockholms universitet,Institutionen för data- och systemvetenskap4 aut0 (Swepub:su)ahenr
7001 a Richir, Milan C.u University Medical Centre Utrecht, Utrecht, the Netherlands4 aut
7001 a Buchli, Christianu Karolinska Institutet4 aut
7001 a van Mourik, Maaike S. M.u University Medical Centre Utrecht, Utrecht, the Netherlands4 aut
7001 a Naucler, Pontusu Karolinska Institutet4 aut
7102 a Karolinska Institutetb University Medical Centre Utrecht, Utrecht, the Netherlands; National Institute for Public Health and the Environment, Bilthoven, the Netherlands4 org
7730 t Antimicrobial Resistance and Infection Controlg 12:1q 12:1x 2047-2994
8564 u https://doi.org/10.1186/s13756-023-01316-xy Fulltext
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-223746
8564 8u https://doi.org/10.1186/s13756-023-01316-x
8564 8u http://kipublications.ki.se/Default.aspx?queryparsed=id:154046188

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