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References from Explainable Machine Learning Predictive Models for Surgical Site Infections: Scoping Review. Local targets link to admitted publications; unresolved targets remain external evidence.
10.1086/646436
10.1086/646436 · External reference
CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting
10.1016/j.ajic.2008.03.002 · External reference
The burden of health care utilization, cost, and mortality associated with select surgical site infections
10.1016/j.jcjq.2024.08.005 · External reference
Clinical and economic burden of surgical site infections in inpatient care in Germany: a retrospective, cross-sectional analysis from 79 hospitals
10.1371/journal.pone.0275970 · External reference
Long-term mortality following complications after elective surgery: a secondary analysis of pooled data from two prospective cohort studies
10.1016/j.bja.2022.06.019 · External reference
Surgical site infection and costs in low- and middle-income countries: a systematic review of the economic burden
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The prevalence of nosocomial infection and antibiotic use in German hospitals
10.3238/arztebl.2017.0851 · External reference
Global incidence of surgical site infection among patients: systematic review and meta-analysis
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Incidence and impact of surgical site infections on length of stay and cost of care for patients undergoing open procedures
10.1016/j.sopen.2022.10.004 · External reference
The incidence of surgical site infections in China
10.1016/j.jhin.2023.06.004 · External reference
Surgical site infection prevention: a review
10.1001/jama.2022.24075 · External reference
Worldwide incidence of surgical site infections in general surgical patients: a systematic review and meta-analysis of 488,594 patients
10.1016/j.ijsu.2021.106136 · External reference
Assessment of risk factors associated with surgical site infection following abdominal surgery: a systematic review
10.1136/bmjsit-2023-000182 · External reference
Impact of participation in a surgical site infection surveillance network: results from a large international cohort study
10.1016/j.jhin.2018.12.003 · External reference
Risk factors for surgical site infection (SSI) in patients undergoing hysterectomy: a systematic review and meta-analysis
10.1136/bmjopen-2024-093072 · External reference
A systematic review of risk factors associated with surgical site infections among surgical patients
10.1371/journal.pone.0083743 · External reference
What is machine learning? A primer for the epidemiologist
10.1093/aje/kwz189 · External reference
Reflection on modern methods: generalized linear models for prognosis and intervention-theory, practice and implications for machine learning
10.1093/ije/dyaa049 · External reference
Developing prediction models for clinical use using logistic regression: an overview
10.21037/jtd.2019.01.25 · External reference
Recent applications and perspectives of logistic regression modelling in healthcare
10.54254/2753-8818/36/20240614 · External reference
Evaluating predictive performance and generalizability of traditional and artificial intelligence models in predicting surgical site infections postspinal surgery: a systematic review
10.1016/j.spinee.2025.07.032 · External reference
A comprehensive survey on machine learning
10.54060/jmss/001.01.003 · External reference
Automated surveillance systems for healthcare-associated infections: results from a European survey and experiences from real-life utilization
10.1016/j.jhin.2021.12.021 · External reference
Validation of an algorithm for semiautomated surveillance to detect deep surgical site infections after primary total hip or knee arthroplasty-a multicenter study
10.1017/ice.2020.377 · External reference
Performance of simplified surgical site infection (SSI) surveillance case definitions for resource limited settings: comparison to SSI cases reported to the national healthcare safety network, 2013-2017
10.1017/ice.2020.36 · External reference
Risk prediction model for surgical site infection in patients with gastrointestinal cancer: a systematic review and meta-analysis
10.1186/s12957-025-03726-0 · External reference
Artificial intelligence for the detection of surgical site infection on wound images. A systematic review and meta-analysis
10.1093/bjs/znaf159.005 · External reference
Systematic evaluation of machine learning models for postoperative surgical site infection prediction
10.1371/journal.pone.0312968 · External reference
Predicting postoperative surgical site infection with administrative data: a random forests algorithm
10.1186/s12874-021-01369-9 · External reference
Preoperative prediction of postoperative infections using machine learning and electronic health record data
10.1097/sla.0000000000006106 · External reference
Assessing the utility of deep neural networks in detecting superficial surgical site infections from free text electronic health record data
10.3389/fdgth.2023.1249835 · External reference
Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study
10.1186/s13756-023-01294-0 · External reference
Machine learning approaches for the image-based identification of surgical wound infections: scoping review
10.2196/52880 · External reference
The use of mobile thermal imaging and machine learning technology for the detection of early surgical site infections
10.1016/j.amjsurg.2023.04.011 · External reference
Performance of machine learning algorithms for surgical site infection case detection and prediction: a systematic review and meta-analysis
10.1016/j.amsu.2022.104956 · External reference
The best machine learning algorithm for building surgical site infection predictive models: a systematic review and network meta-analysis
10.1016/j.compbiomed.2025.110286 · External reference
Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review
10.3390/app11115088 · External reference
FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare
10.1136/bmj.r340 · External reference
Using artificial intelligence to reduce orthopedic surgical site infection surveillance workload: Algorithm design, validation, and implementation in 4 Spanish hospitals
10.1016/j.ajic.2023.04.165 · External reference
Surgical site infection surveillance in knee and hip arthroplasty: optimizing an algorithm to detect high-risk patients based on electronic health records
10.1186/s13756-024-01445-x · External reference
How explainable artificial intelligence can increase or decrease clinicians’ trust in AI applications in health care: systematic review
10.2196/53207 · External reference
Measuring the impact of AI in the diagnosis of hospitalized patients: a randomized clinical vignette survey study
10.1001/jama.2023.22295 · External reference
Interpreting black-box models: a review on explainable artificial intelligence
10.1007/s12559-023-10179-8 · External reference
Unresolved reference
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Bayesian networks for interpretable machine learning and optimization
10.1016/j.neucom.2021.01.138 · External reference
Unresolved reference
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
10.1038/s42256-019-0048-x · External reference
Benchmarking and survey of explanation methods for black box models
10.1007/s10618-023-00933-9 · External reference
Shapley value: from cooperative game to explainable artificial intelligence
10.1007/s43684-023-00060-8 · External reference
Permutation importance: a corrected feature importance measure
10.1093/bioinformatics/btq134 · External reference
Classifying XAI methods to resolve conceptual ambiguity
10.3390/technologies13090390 · External reference
Notions of explainability and evaluation approaches for explainable artificial intelligence
10.1016/j.inffus.2021.05.009 · External reference
A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences
10.1007/s10462-022-10256-8 · External reference
The enlightening role of explainable artificial intelligence in medical & healthcare domains: a systematic literature review
10.1016/j.compbiomed.2023.107555 · External reference
Designing interpretable ML system to enhance trust in healthcare: a systematic review to proposed responsible clinician-AI-collaboration framework
10.1016/j.inffus.2024.102412 · External reference
Interpretability of clinical decision support systems based on artificial intelligence from technological and medical perspective: a systematic review
10.1155/2023/9919269 · External reference
PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation
10.7326/m18-0850 · External reference
10.46658/jbimes-24-01
10.46658/jbimes-24-01 · External reference
PRISMA-S: an extension to the PRISMA statement for reporting literature searches in systematic reviews
10.1186/s13643-020-01542-z · External reference
Prediction model for urinary tract infection in pediatric urological surgery patients
10.3389/fpubh.2022.888089 · External reference
A novel risk calculator predicting surgical site infection after spinal surgery in patients with cerebral palsy
10.1111/dmcn.15193 · External reference
Systematic review of risk factors for surgical site infection in pediatric scoliosis surgery
10.1016/j.spinee.2015.03.005 · External reference
Theory and practice of integrating machine learning and conventional statistics in medical data analysis
10.3390/diagnostics12102526 · External reference
Beyond comparing machine learning and logistic regression in clinical prediction modelling: shifting from model debate to data quality
10.2196/77721 · External reference
High-dimensional LASSO-based computational regression models: regularization, shrinkage, and selection
10.3390/make1010021 · External reference
Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist
10.1371/journal.pmed.1001744 · External reference
Unresolved reference
External reference
Centers for disease control and prevention guideline for the prevention of surgical site infection, 2017
10.1001/jamasurg.2017.0904 · External reference
Clinical prediction models: diagnosis versus prognosis
10.1016/j.jclinepi.2021.01.009 · External reference
Unresolved reference
External reference
Systematic review and meta-analysis of the risk factors of surgical site infection in patients with colorectal cancer
10.21037/tcr-22-627 · External reference
Multiple preoperative biomarkers are associated with incidence of surgical site infection following surgeries of ankle fractures
10.1111/iwj.13351 · External reference
Distinction of risk factors for superficial vs organ-space surgical site infections after pancreatic surgery
10.1001/jamasurg.2017.2155 · External reference
Interpretable and explainable machine learning: a methods‐centric overview with concrete examples
10.1002/widm.1493 · External reference
PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods
10.1136/bmj-2024-082505 · External reference
The measurement of observer agreement for categorical data
10.2307/2529310 · External reference
Interpretable machine learning models for pre- and postoperative prediction of early intra-abdominal infections after liver transplantation: a multicenter retrospective cohort study
10.1177/20499361261453152 · External reference
Prediction models of surgical site infection after gastrointestinal surgery: a nationwide prospective cohort study
10.1097/js9.0000000000000808 · External reference
Impact of the ileal microbiota on surgical site infections in Crohn’s disease: a nationwide prospective cohort
10.1093/ecco-jcc/jjac026 · External reference
Prognostics of surgical site infections using dynamic health data
10.1016/j.jbi.2016.10.021 · External reference
Quantifying surgical complexity with machine learning: looking beyond patient factors to improve surgical models
10.1016/j.surg.2014.04.034 · External reference
Artificial intelligence-based multimodal risk assessment model for surgical site infection (AMRAMS): development and validation study
10.2196/18186 · External reference
Leveraging electronic health records for predictive modeling of post-surgical complications
10.1177/0962280217696115 · External reference
An explainable long short-term memory network for surgical site infection identification
10.1016/j.surg.2024.03.006 · External reference
The augmented value of using clinical notes in semi-automated surveillance of deep surgical site infections after colorectal surgery
10.1186/s13756-023-01316-x · External reference
Semi-automated surveillance of surgical site infections using machine learning and rule-based classification models
10.1038/s41746-025-01989-1 · External reference
Machine learning-enhanced surveillance for surgical site infections in patients undergoing colon surgery: model development and evaluation study
10.2196/75121 · External reference
Explainable machine learning models for prediction of surgical site infection after posterior lumbar fusion surgery based on Shapley additive explanations
10.1016/j.wneu.2025.123942 · External reference
Predicting common maternal postpartum complications: leveraging health administrative data and machine learning
10.1111/1471-0528.15607 · External reference
Using artificial intelligence (AI) to predict postoperative surgical site infection: a retrospective cohort of 4046 posterior spinal fusions
10.1016/j.clineuro.2020.105718 · External reference
Logistic regression and neural network prediction of neurosurgical site infections in a grade A tertiary hospital
10.11816/cn.ni.2018-180362 · External reference
A risk prediction model for postoperative incision infection in patients with open tibia and fibula fractures was established based on decision tree
10.12102/j.issn.2095-8668.2025.16.029 · External reference
Application of AI in predicting postoperative infections using routine blood parameters
10.6026/973206300214271 · External reference
Maximizing interpretability and cost-effectiveness of surgical site infection (SSI) predictive models using feature-specific regularized logistic regression on preoperative temporal data
10.1155/2019/2059851 · External reference
Prophylactic antibiotic bundle compliance and surgical site infections: an artificial neural network analysis
10.1186/s13037-019-0222-4 · External reference
Pathogenetic characteristics and related risk factors of incisional infection after surgery for acute intestinal obstruction and construction of prediction model
10.1186/s40001-025-02652-x · External reference
ML-predicted surgical site infections: an epidemiological study utilizing machine learning on routinely collected healthcare data to predict infection risk
10.1016/j.smhl.2025.100596 · External reference
Performance of a machine learning algorithm in predicting outcomes of aortic valve replacement
10.1016/j.athoracsur.2020.05.107 · External reference
Development and validation of models for detection of postoperative infections using structured electronic health records data and machine learning
10.1016/j.surg.2022.10.026 · External reference
Applying machine learning across sites: external validation of a surgical site infection detection algorithm
10.1016/j.jamcollsurg.2021.03.026 · External reference
Identification of surgical site infections using electronic health record data
10.1016/j.ajic.2018.05.011 · External reference
Risk factors and predictive model for early surgical site infection following single-level PLIF in diabetic patients
10.3389/fsurg.2025.1709831 · External reference
Validating administrative data to identify complex surgical site infections following cardiac implantable electronic device implantation: a comparison of traditional methods and machine learning
10.1186/s13756-022-01174-z · External reference
Is it possible to identify cases of coronary artery bypass graft postoperative surgical site infection accurately from claims data?
10.1186/1472-6947-14-42 · External reference
SurgiCut-AI: an AI-driven tool for predicting surgical site infections following ventral hernia repair using pre- and intra-operative parameters
10.1007/s10029-025-03498-2 · External reference
-A machine learning model to predict surgical site infection after surgery of lower extremity fractures
10.1007/s00264-024-06194-5 · External reference
Construct validation of machine learning in the prediction of short-term postoperative complications following total shoulder arthroplasty
10.1016/j.jse.2019.05.017 · External reference
Influencing factors for surgical site infection after gastrointestinal perforation repair surgery: analysis based on decision tree and logistic regression model
10.12138/j.issn.1671-9638.20245130 · External reference
Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models?
10.1177/03000605241293696 · External reference
Construct validation of machine learning for accurately predicting the risk of postoperative surgical site infection following spine surgery
10.1016/j.jhin.2023.09.024 · External reference
Risk stratification for superficial surgical site infection after emergency trauma laparotomy
10.1089/sur.2020.242 · External reference
Accurate risk stratification for development of organ/space surgical site infections after emergent trauma laparotomy
10.1097/ta.0000000000002143 · External reference
Machine learning based peri-surgical risk calculator for abdominal related emergency general surgery: a multicenter retrospective study
10.1097/js9.0000000000001276 · External reference
Preliminary evaluation of a novel artificial intelligence-based prediction model for surgical site infection in colon cancer
10.21873/cdp.10161 · External reference
Machine learning models accurately predict surgical site infection after emergent trauma laparotomy
10.1016/j.jss.2025.10.043 · External reference
Predicting surgical site infection after lumbar laminectomy and discectomy: a cutting-edge algorithmic approach by incorporating ensembled stacking into the current state-of-the-art for automated machine learning
10.1007/s10143-025-03766-w · External reference
Machine-learning models for predicting surgical site infections using patient pre-operative risk and surgical procedure factors
10.1016/j.ajic.2022.08.013 · External reference
Assessing the utility of deep neural networks in predicting postoperative surgical complications: a retrospective study
10.1016/s2589-7500(21)00084-4 · External reference
Assessing the value of deep neural networks for postoperative complication prediction in pancreaticoduodenectomy patients
10.1371/journal.pone.0316402 · External reference
Challenges of modeling outcomes for surgical infections: a word of caution
10.1089/sur.2020.208 · External reference
Improved prediction of surgical-site infection after colorectal surgery using machine learning
10.1097/dcr.0000000000002559 · External reference
Machine learning to predict periprosthetic joint infections following primary total hip arthroplasty using a national database
10.1007/s00402-025-05757-4 · External reference
Development and internal validation of supervised machine learning algorithms for predicting the risk of surgical site infection following minimally invasive transforaminal lumbar interbody fusion
10.3389/fmed.2021.771608 · External reference
Construct and validate a predictive model for surgical site infection after posterior lumbar interbody fusion based on machine learning algorithm
10.1155/2022/2697841 · External reference
Machine learning models for mitral valve replacement: a comparative analysis with the Society of Thoracic Surgeons risk score
10.1111/jocs.16072 · External reference
Development and external validation of the “Global Surgical-Site Infection” (GloSSI) predictive model in adult patients undergoing gastrointestinal surgery
10.1093/bjs/znae163.082 · External reference
Prediction of early periprosthetic joint infection after total hip arthroplasty
10.2147/clep.s347968 · External reference
Artificial intelligence-based prediction model for surgical site infection in metastatic spinal disease: a multicenter development and validation study
10.1097/js9.0000000000002806 · External reference
Machine learning model predicting factors for incisional infection following right hemicolectomy for colon cancer
10.1186/s12893-024-02543-8 · External reference
Using preoperative and intraoperative factors to predict the risk of surgical site infections after lumbar spinal surgery: a machine learning-based study
10.1016/j.wneu.2022.03.060 · External reference
The use of artificial neural networks for the prediction of surgical site infection following TKA
10.1055/s-0041-1741396 · External reference
Using multiple indicators to predict the risk of surgical site infection after ORIF of tibia fractures: a machine learning based study
10.3389/fcimb.2023.1206393 · External reference
The predictive value of column chart model and random forest model based on preoperative systemic inflammatory factors for postoperative infection in patients with open tibiofibular fractures
10.3969/j.issn.2095-252x.2025.09.007 · External reference
Research on risk prediction model of postoperative wound infection in patients with colorectal cancer undergoing radical resection surgery
10.15932/j.0253-9713.2025.01.009 · External reference
Meta-lasso: new insight on infection prediction after minimally invasive surgery
10.1007/s11517-024-03027-w · External reference
Establishment of nomogram model predicting risk of surgical site infection after posterior lumbar surgery
10.12089/jca.2023.05.002 · External reference
Explainable model using shapley additive explanations approach on wound infection after wide soft tissue sarcoma resection: “Big Data” analysis based on health insurance review and assessment service hub
10.3390/medicina60020327 · External reference
Systemic immune-inflammatory biomarkers combined with the CRP-albumin-lymphocyte index predict surgical site infection following posterior lumbar spinal fusion: a retrospective study using machine learning
10.3389/fmed.2025.1590248 · External reference
Machine learning prediction model for surgical site infections after major abdominal surgery
10.1186/s13037-026-00481-3 · External reference
Performance of a novel risk model for deep sternal wound infection after coronary artery bypass grafting
10.1038/s41598-022-19473-1 · External reference
Development and validation of a predictive model for surgical site infection in open hand injuries
10.2147/idr.s589797 · External reference
A comprehensive feature importance analysis of surgical site infection following colorectal cancer surgery
10.1038/s41598-025-23722-4 · External reference
Multiclass classification of infections after cervical spine surgery in the elderly: a machine learning approach based on preoperative and perioperative data
10.1007/s00586-026-09761-z · External reference
Construct validation of machine learning models for predicting surgical site infection risk following ankle fracture surgery
10.1097/js9.0000000000003239 · External reference
Machine learning approach to predict surgical site infection in head and neck squamous cell carcinoma patients after free flap reconstruction
10.1016/j.jcms.2025.09.010 · External reference
Real-time risk prediction of colorectal surgery-related post-surgical complications using GRU-D model
10.1016/j.jbi.2022.104202 · External reference
Machine learning enhanced prediction of deep sternal wound infection after surgical myocardial revascularization
10.1016/j.carrev.2025.07.016 · External reference
Establishment of predictive model for surgical site infection following colorectal surgery based on machine learning
10.7507/1002-0179.202002027 · External reference
Modernizing Surgical Quality: A Novel Approach to Improving Detection of Surgical Site Infections in the Veteran Population
10.1089/sur.2024.013 · External reference
Explainable AI modeling of postoperative surgical site infection risk in autologous breast reconstruction
10.1016/j.bjps.2025.12.029 · External reference
Application of machine learning to prediction of surgical site infection
10.1109/embc.2019.8857942 · External reference
Establishment and validation of a nomogram model for surgical site infections after posterior lumbar interbody fusion: a retrospective observational study
10.1007/s10143-025-03645-4 · External reference
Prognostic value of C-reactive protein and neutrophil-to-lymphocyte ratio in predicting postoperative infections after gastrointestinal surgery: a meta-analysis
10.7759/cureus.91123 · External reference
Time of developing surgical site infections and its association with patient and procedure characteristics
10.1016/j.jiph.2025.102734 · External reference
Time to development of surgical site infection and its predictors among general surgery patients admitted at specialized hospitals in Amhara region, northwest Ethiopia: a prospective follow-up study
10.1186/s12879-023-08301-0 · External reference
Risk factors and prediction model for inpatient surgical site infection after major abdominal surgery
10.1016/j.jss.2017.05.018 · External reference
Interleukin-6 as a critical inflammatory marker for early diagnosis of surgical site infection after spine surgery
10.1007/s15010-024-02271-4 · External reference
A prognostic model of surgical site infection using daily clinical wound assessment
10.1016/j.jamcollsurg.2016.04.046 · External reference
Factors influencing microbial colonies in the air of operating rooms
10.1186/s12879-017-2928-1 · External reference
Traffic in the operating room: a review of factors influencing air flow and surgical wound contamination
10.2174/1871526511313030002 · External reference
Diagnostic framework to validate clinical machine learning models locally on temporally stamped data
10.1038/s43856-025-00965-w · External reference
A framework for understanding label leakage in machine learning for health care
10.1093/jamia/ocad178 · External reference
Postoperative laboratory markers as predictors of early spinal surgical site infections: a retrospective cohort study
10.1016/j.cjtee.2025.08.002 · External reference
Artificial intelligence to predict billing code levels of emergency department encounters
10.1016/j.annemergmed.2024.07.011 · External reference
Enhancing patient outcome prediction through deep learning with sequential diagnosis codes from structured electronic health record data: systematic review
10.2196/57358 · External reference
Diagnostic codes in AI prediction models and label leakage of same-admission clinical outcomes
10.1001/jamanetworkopen.2025.50454 · External reference
The mythos of model interpretability
10.1145/3233231 · External reference
Algorithms to estimate Shapley value feature attributions
10.1038/s42256-023-00657-x · External reference
Using permutation-based feature importance for improved machine learning model performance at reduced costs
10.1109/access.2025.3544625 · External reference
From local explanations to global understanding with explainable AI for trees
10.1038/s42256-019-0138-9 · External reference
Unresolved reference
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10.1007/978-3-030-53352-6_24
10.1007/978-3-030-53352-6_24 · External reference
Explainable AI in clinical decision support systems: a meta-analysis of methods, applications, and usability challenges
10.3390/healthcare13172154 · External reference
Comparison of SHAP and clinician friendly explanations reveals effects on clinical decision behaviour
10.1038/s41746-025-01958-8 · External reference
Co-design of human-centered, explainable AI for clinical decision support
10.1145/3587271 · External reference
Evaluating the impact of explainable AI on clinicians’ decision-making: a study on ICU length of stay prediction
10.1016/j.ijmedinf.2025.105943 · External reference
Tell me a story! narrative-driven XAI with large language models
10.1016/j.dss.2025.114402 · External reference
10.1145/3746175.3746195
10.1145/3746175.3746195 · External reference
Guidelines and evaluation of clinical explainable AI in medical image analysis
10.1016/j.media.2022.102684 · External reference
Evaluating generalized feature importance via performance assessment of machine learning models for predicting elastic properties of materials
10.1016/j.commatsci.2024.112847 · External reference
Surgical site infections in colorectal cancer surgeries: a systematic review and meta-analysis of the impact of surgical approach and associated risk factors
10.3390/life14070850 · External reference
Risk factors for surgical site infection in patients with gastric cancer: a meta-analysis
10.1111/iwj.14264 · External reference
Predictors for different types of surgical site infection in patients with gastric cancer: a systematic review and meta-analysis
10.1111/iwj.14549 · External reference
Prediction or causality? A scoping review of their conflation within current observational research
10.1007/s10654-021-00794-w · External reference
Causal machine learning for predicting treatment outcomes
10.1038/s41591-024-02902-1 · External reference
The false hope of current approaches to explainable artificial intelligence in health care
10.1016/s2589-7500(21)00208-9 · External reference
Beyond predictive accuracy: statistical validation of feature importance in biomedical machine learning
10.1016/j.cmpb.2025.109085 · External reference
Causal scientific explanations from machine learning
10.1007/s11229-023-04429-3 · External reference
Counterfactual clinical prediction models could help to infer individualized treatment effects in randomized controlled trials-an illustration with the international stroke trial
10.1016/j.jclinepi.2020.05.022 · External reference
A machine learning explainability tutorial for atmospheric sciences
10.1175/aies-d-23-0018.1 · External reference
Unresolved reference
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Clinically relevant features for predicting the severity of surgical site infections
10.1109/jbhi.2021.3121038 · External reference
Time-to-death longitudinal characterization of clinical variables and longitudinal prediction of mortality in COVID-19 patients: a two-center study
10.3389/fmed.2021.661940 · External reference
Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records
10.1016/s2589-7500(20)30018-2 · External reference
Time-dependent predictive performance of inflammatory markers for 30-day all-cause mortality in patients with suspected infection in a Japanese emergency department: a retrospective cohort study
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