Predicting the occurrence of surgical site infections using text mining and machine learning

Silva, Daniel A. da; Caten, Carla S. ten; Santos, Rodrigo Pires dos; Fogliatto, Flávio Sanson; Hsuan, Juliana

doi:10.1371/journal.pone.0226272

Cited by 19 publications

(11 citation statements)

References 35 publications

(35 reference statements)

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“…As far as we know, machine learning models have been used only in few previous CRS studies, to classify osteomeatal complex inflammation on computed tomography 50 and olfactory recovery after ESS 51 . In surgery research, machine learning models have been used to predict surgical site infections 52 , postoperative outcome of degenerative cervical myelopathy 39 , revision surgery after knee replacement 53 , prolonged opioid prescription after surgery for lumbar disc herniation 54 , and blood transfusion after adult spinal deformity surgery 55 .…”

Section: Discussionmentioning

confidence: 99%

Using machine learning for personalized prediction of revision paranasal sinus surgery

Nuutinen

Haukka

Virkkula

et al. 2021

Preprint

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Background: Uncontrolled chronic rhinosinusitis (CRS) needing consideration of surgery is a growing health problem yet its risk factors at individual level are not known. Our aim was to examine risk factors of revision endoscopic sinus surgery (ESS) at the individual level by using artificial intelligence. Methods: Demographic and visit variables were collected from electronic health records (EHR) of 790 operated CRS patients. The effect of variables on the prediction accuracy of revision ESS was examined at the individual level via machine learning models. Results: Revision ESS was performed to 114 (14.7%) CRS patients. The logistic regression, gradient boosting and random forest classifiers had similar performance (AUC values .746, .745 and .747, respectively) for predicting revision ESS. The best performance was yielded by using logistic regression and long predictor data retrieval time (AUC .809, precision 36%, sensitivity 70%) as compared with data collection time from baseline visit until 0, 3 and 6 months after the baseline ESS (AUC values .668, .717 and .746, respectively). The number of visits, number of days from the baseline visit to the baseline ESS, age, CRS with nasal polyps (CRSwNP), asthma, NERD and immunodeficiency or its suspicion were associated with revision ESS. Age and the number of visits before baseline ESS had non-linear effects for the predictions. Conclusions: Intelligent data analysis found important predictors of revision ESS at the individual level, such as visit frequency, age, Type 2 high diseases and immunodeficiency or its suspicion.

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Section: Discussionmentioning

confidence: 99%

Using machine learning for personalized prediction of revision paranasal sinus surgery

Nuutinen

Haukka

Virkkula

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Machine learning, however, has previously been used in allergology and related research [45], including in the prediction of persistent early childhood asthma [46], eosinophilic esophagitis [47], eosinophilic CRS [48] or osteomeatal complex inflammation [49]. In addition, machine learning has found applications in predicting postoperative outcomes for degenerative cervical myelopathy [50], revision surgery following knee replacement [51], prolonged opioid prescription following surgery for lumbar disc herniation [52], blood transfusion following adult spinal deformity surgery [53], surgical infections [54] and olfactory recovery after ESS [55]. None of these previous studies, however, have presented models designed to predict revision ESS at the individual level.…”

Section: Plos Onementioning

confidence: 99%

Using machine learning for the personalised prediction of revision endoscopic sinus surgery

et al. 2022

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Background Revision endoscopic sinus surgery (ESS) is often considered for chronic rhinosinusitis (CRS) if maximal conservative treatment and baseline ESS prove insufficient. Emerging research outlines the risk factors of revision ESS. However, accurately predicting revision ESS at the individual level remains uncertain. This study aims to examine the prediction accuracy of revision ESS and to identify the effects of risk factors at the individual level. Methods We collected demographic and clinical variables from the electronic health records of 767 surgical CRS patients ≥16 years of age. Revision ESS was performed on 111 (14.5%) patients. The prediction accuracy of revision ESS was examined by training and validating different machine learning models, while the effects of variables were analysed using the Shapley values and partial dependence plots. Results The logistic regression, gradient boosting and random forest classifiers performed similarly in predicting revision ESS. Area under the receiving operating characteristic curve (AUROC) values were 0.744, 0.741 and 0.730, respectively, using data collected from the baseline visit until six months after baseline ESS. The length of time during which data were collected improved the prediction performance. For data collection times of 0, 3, 6 and 12 months after baseline ESS, AUROC values for the logistic regression were 0.682, 0.715, 0.744 and 0.784, respectively. The number of visits before or after baseline ESS, the number of days from the baseline visit to the baseline ESS, patient age, CRS with nasal polyps (CRSwNP), asthma, non-steroidal anti-inflammatory drug exacerbated respiratory disease and immunodeficiency or suspicion of it all associated with revision ESS. Patient age and number of visits before baseline ESS carried non-linear effects for predictions. Conclusions Intelligent data analysis identified important predictors of revision ESS at the individual level, such as the frequency of clinical visits, patient age, Type 2 high diseases and immunodeficiency or a suspicion of it.

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“…Finally, purely data-driven approaches on unstructured data are becoming ubiquitous thanks to the development of advanced machine learning techniques [27], [28], [29]. Extracting information from text using natural language processing algorithms is especially difficult as the models need to capture different semantics within the words [30]. Karhade et al [31] analyzed free-text notes of patients to report a postsurgical infection automatically.…”

Section: A Related Workmentioning

confidence: 99%

Clinically Relevant Features for Predicting the Severity of Surgical Site Infections

Boubekki

Myhre

Luppino

et al. 2022

IEEE J. Biomed. Health Inform.

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Surgical site infections are hospital-acquired infections resulting in severe risk for patients and significantly increased costs for healthcare providers. In this work, we show how to leverage irregularly sampled preoperative blood tests to predict, on the day of surgery, a future surgical site infection and its severity. Our dataset is extracted from the electronic health records of patients who underwent gastrointestinal surgery and developed either deep, shallow or no infection. We represent the patients using the concentrations of fourteen common blood components collected over the four weeks preceding the surgery partitioned into six time windows. A gradient boosting based classifier trained on our new set of features reports an AUROC of 0.991 for predicting a postoperative infection and and AUROC of 0.937 for classifying the severity of the infection. Further analyses support the clinical relevance of our approach as the most important features describe the nutritional status and the liver function over the two weeks prior to surgery.

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Predicting the occurrence of surgical site infections using text mining and machine learning

Cited by 19 publications

References 35 publications

Using machine learning for personalized prediction of revision paranasal sinus surgery

Using machine learning for personalized prediction of revision paranasal sinus surgery

Using machine learning for the personalised prediction of revision endoscopic sinus surgery

Clinically Relevant Features for Predicting the Severity of Surgical Site Infections

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