Search: onr:"swepub:oai:DiVA.org:su-223746" > The augmented value...
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000 | 04998naa a2200577 4500 | |
001 | oai:DiVA.org:su-223746 | |
003 | SwePub | |
008 | 231117s2023 | |||||||||||000 ||eng| | |
009 | oai:prod.swepub.kib.ki.se:154046188 | |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-2237462 URI |
024 | 7 | a https://doi.org/10.1186/s13756-023-01316-x2 DOI |
024 | 7 | 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 | 7 | a ref2 swepub-contenttype |
072 | 7 | a art2 swepub-publicationtype |
100 | 1 | 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 |
245 | 1 0 | a The augmented value of using clinical notes in semi-automated surveillance of deep surgical site infections after colorectal surgery |
264 | 1 | c 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 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Hälsovetenskap0 (SwePub)3032 hsv//swe |
650 | 7 | a MEDICAL AND HEALTH SCIENCESx Health Sciences0 (SwePub)3032 hsv//eng |
650 | 7 | a NATURVETENSKAPx Biologi0 (SwePub)1062 hsv//swe |
650 | 7 | a NATURAL SCIENCESx Biological Sciences0 (SwePub)1062 hsv//eng |
650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Medicinska och farmaceutiska grundvetenskaper0 (SwePub)3012 hsv//swe |
650 | 7 | a MEDICAL AND HEALTH SCIENCESx Basic Medicine0 (SwePub)3012 hsv//eng |
650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Infektionsmedicin0 (SwePub)302092 hsv//swe |
650 | 7 | a 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 | |
700 | 1 | a van der Werff, Suzanne D.u Karolinska Institutet4 aut |
700 | 1 | a Weegar, Rebecka,d 1982-u Stockholms universitet,Institutionen för data- och systemvetenskap4 aut0 (Swepub:su)rewe5142 |
700 | 1 | a Henriksson, Aron,d 1985-u Stockholms universitet,Institutionen för data- och systemvetenskap4 aut0 (Swepub:su)ahenr |
700 | 1 | a Richir, Milan C.u University Medical Centre Utrecht, Utrecht, the Netherlands4 aut |
700 | 1 | a Buchli, Christianu Karolinska Institutet4 aut |
700 | 1 | a van Mourik, Maaike S. M.u University Medical Centre Utrecht, Utrecht, the Netherlands4 aut |
700 | 1 | a Naucler, Pontusu Karolinska Institutet4 aut |
710 | 2 | a Karolinska Institutetb University Medical Centre Utrecht, Utrecht, the Netherlands; National Institute for Public Health and the Environment, Bilthoven, the Netherlands4 org |
773 | 0 | t Antimicrobial Resistance and Infection Controlg 12:1q 12:1x 2047-2994 |
856 | 4 | u https://doi.org/10.1186/s13756-023-01316-xy Fulltext |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-223746 |
856 | 4 8 | u https://doi.org/10.1186/s13756-023-01316-x |
856 | 4 8 | u http://kipublications.ki.se/Default.aspx?queryparsed=id:154046188 |
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