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Yao J, Chu LC, Patlas M. Applications of Artificial Intelligence in Acute Abdominal Imaging. Can Assoc Radiol J 2024:8465371241250197. [PMID: 38715249 DOI: 10.1177/08465371241250197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
Artificial intelligence (AI) is a rapidly growing field with significant implications for radiology. Acute abdominal pain is a common clinical presentation that can range from benign conditions to life-threatening emergencies. The critical nature of these situations renders emergent abdominal imaging an ideal candidate for AI applications. CT, radiographs, and ultrasound are the most common modalities for imaging evaluation of these patients. For each modality, numerous studies have assessed the performance of AI models for detecting common pathologies, such as appendicitis, bowel obstruction, and cholecystitis. The capabilities of these models range from simple classification to detailed severity assessment. This narrative review explores the evolution, trends, and challenges in AI applications for evaluating acute abdominal pathologies. We review implementations of AI for non-traumatic and traumatic abdominal pathologies, with discussion of potential clinical impact, challenges, and future directions for the technology.
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Affiliation(s)
- Jason Yao
- Department of Radiology, McMaster University, Hamilton, ON, Canada
| | - Linda C Chu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael Patlas
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
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Yu M, Wang S, He K, Teng F, Deng J, Guo S, Yin X, Lu Q, Gu W. Predicting the complexity and mortality of polytrauma patients with machine learning models. Sci Rep 2024; 14:8302. [PMID: 38594313 PMCID: PMC11004111 DOI: 10.1038/s41598-024-58830-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 04/03/2024] [Indexed: 04/11/2024] Open
Abstract
We aim to develop machine learning (ML) models for predicting the complexity and mortality of polytrauma patients using clinical features, including physician diagnoses and physiological data. We conducted a retrospective analysis of a cohort comprising 756 polytrauma patients admitted to the intensive care unit (ICU) at Pizhou People's Hospital Trauma Center, Jiangsu, China between 2020 and 2022. Clinical parameters encompassed demographics, vital signs, laboratory values, clinical scores and physician diagnoses. The two primary outcomes considered were mortality and complexity. We developed ML models to predict polytrauma mortality or complexity using four ML algorithms, including Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN) and eXtreme Gradient Boosting (XGBoost). We assessed the models' performance and compared the optimal ML model against three existing trauma evaluation scores, including Injury Severity Score (ISS), Trauma Index (TI) and Glasgow Coma Scale (GCS). In addition, we identified several important clinical predictors that made contributions to the prognostic models. The XGBoost-based polytrauma mortality prediction model demonstrated a predictive ability with an accuracy of 90% and an F-score of 88%, outperforming SVM, RF and ANN models. In comparison to conventional scoring systems, the XGBoost model had substantial improvements in predicting the mortality of polytrauma patients. External validation yielded strong stability and generalization with an accuracy of up to 91% and an AUC of 82%. To predict polytrauma complexity, the XGBoost model maintained its performance over other models and scoring systems with good calibration and discrimination abilities. Feature importance analysis highlighted several clinical predictors of polytrauma complexity and mortality, such as Intracranial hematoma (ICH). Leveraging ML algorithms in polytrauma care can enhance the prognostic estimation of polytrauma patients. This approach may have potential value in the management of polytrauma patients.
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Affiliation(s)
- Meiqi Yu
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China
- Jiangsu Province Engineering Research Center of TCM Intelligence Health Service, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China
| | - Shen Wang
- Department of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, 100044, China
| | - Kai He
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China
- Jiangsu Province Engineering Research Center of TCM Intelligence Health Service, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China
| | - Fei Teng
- Trauma Center, Pizhou People's Hospital, Xuzhou, 221300, Jiangsu, China
| | - Jin Deng
- Department of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, 100044, China
| | - Shuhang Guo
- Department of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, 100044, China
| | - Xiaofeng Yin
- Department of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, 100044, China.
- Key Laboratory of Trauma and Neural Regeneration (Peking University), Ministry of Education, 100044, Beijing, China.
- National Center for Trauma Medicine, 100044, Beijing, China.
| | - Qingguo Lu
- Trauma Center, Pizhou People's Hospital, Xuzhou, 221300, Jiangsu, China.
| | - Wanjun Gu
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China.
- Jiangsu Province Engineering Research Center of TCM Intelligence Health Service, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China.
- Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu, China.
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Liu CW, Chacon M, Crawford L, Polydore H, Ting T, Wilson NA. Machine Learning Improves the Accuracy of Trauma Team Activation Level Assignments in Pediatric Patients. J Pediatr Surg 2024; 59:74-79. [PMID: 37865573 PMCID: PMC10843072 DOI: 10.1016/j.jpedsurg.2023.09.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 09/06/2023] [Indexed: 10/23/2023]
Abstract
BACKGROUND The assignment of trauma team activation levels can be conceptualized as a classification task. Machine learning models can be used to optimize classification predictions. Our purpose was to demonstrate proof-of-concept for a machine learning tool for predicting trauma team activation levels in pediatric patients with traumatic injuries. METHODS Following IRB approval, we retrospectively collected data from the institutional trauma registry and electronic medical record at our Pediatric Trauma Center for all patients (age <18 y) who triggered a trauma team activation (1/2014-12/2021), including: demographics, mechanisms of injury, comorbidities, pre-hospital interventions, numeric variables, and the six "Need for Trauma Intervention (NFTI)" criteria. Three machine learning models (Logistic Regression, Random Forest, Support Vector Machine) were tested 1000 times in separate trials using the union of the Cribari and NFTI metrics as ground-truth (Injury Severity Score >15 or positive for any of 6 NFTI criteria = full activation). Model performance was quantified and compared to emergency department (ED) staff. RESULTS ED staff had 75% accuracy, an area under the curve (AUC) of 0.73 ± 0.04, and an F1 score of 0.49. The best performing of all machine learning models, the support vector machine, had 80% accuracy, AUC 0.81 ± 4.1e-5, F1 Score 0.80, with less variance compared to other models and ED staff. CONCLUSIONS All machine learning models outperformed ED staff in all performance metrics. These results suggest that data-driven methods can optimize trauma team activations in the ED, with potential improvements in both patient safety and hospital resource utilization. TYPE OF STUDY Economic/Decision Analysis or Modeling Studies. LEVEL OF EVIDENCE II.
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Affiliation(s)
- Catherine W Liu
- School of Medicine, University of Rochester, 601 Elmwood Avenue, Box 601A, Rochester NY, 14642, USA
| | - Miranda Chacon
- Department of Surgery, University of Rochester Medical Center, 601 Elmwood Ave, Box SURG, Rochester, NY 14642, USA
| | - Loralai Crawford
- Department of Biomedical Engineering, University of Rochester, 601 Elmwood Ave, Box SURG, Rochester, NY 14642, USA
| | - Hadassah Polydore
- Division of Pediatric Surgery, Department of Surgery, University of Rochester Medical Center, 601 Elmwood Ave, Box SURG, Rochester, NY 14642, USA
| | - Tiffany Ting
- School of Medicine, University of Rochester, 601 Elmwood Avenue, Box 601A, Rochester NY, 14642, USA
| | - Nicole A Wilson
- School of Medicine, University of Rochester, 601 Elmwood Avenue, Box 601A, Rochester NY, 14642, USA; Department of Surgery, University of Rochester Medical Center, 601 Elmwood Ave, Box SURG, Rochester, NY 14642, USA; Department of Biomedical Engineering, University of Rochester, 601 Elmwood Ave, Box SURG, Rochester, NY 14642, USA; Division of Pediatric Surgery, Department of Surgery, University of Rochester Medical Center, 601 Elmwood Ave, Box SURG, Rochester, NY 14642, USA.
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Chen T, Liu C, Zhang Z, Liang T, Zhu J, Zhou C, Wu S, Yao Y, Huang C, Zhang B, Feng S, Wang Z, Huang S, Sun X, Chen L, Zhan X. Using Machine Learning to Predict Surgical Site Infection After Lumbar Spine Surgery. Infect Drug Resist 2023; 16:5197-5207. [PMID: 37581167 PMCID: PMC10423613 DOI: 10.2147/idr.s417431] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 07/26/2023] [Indexed: 08/16/2023] Open
Abstract
Objective The objective of this study was to utilize machine learning techniques to analyze perioperative factors and identify blood glucose levels that can predict the occurrence of surgical site infection following posterior lumbar spinal surgery. Methods A total of 4019 patients receiving lumbar internal fixation surgery from an institute were enrolled between June 2012 and February 2021. First, the filtered data were randomized into the test and verification groups. Second, in the test group, specific variables were screened using logistic regression analysis, Lasso regression analysis, support vector machine, and random forest. Specific variables obtained using the four methods were intersected, and a dynamic model was constructed. ROC and calibration curves were constructed to assess model performance. Finally, internal model performance was verified in the verification group using ROC and calibration curves. Results The data from 4019 patients were collected. In total, 1327 eligible cases were selected. By combining logistic regression analysis with three machine learning algorithms, this study identified four predictors associated with SSI, namely Modic changes, sebum thickness, hemoglobin, and glucose. Using this information, a prediction model was developed and visually represented. Then, we constructed ROC and calibration curves using the test group; the area under the ROC curve was 0.988. Further, calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index of our model was 0.986 (95% CI 0.981-0.994). Finally, we used the validation group to validate the model internally; the AUC was 0.987. Calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index was 0.982 (95% CI 0.974-0.999). Conclusion Logistic regression analysis and machine learning were employed to select four risk factors: Modic changes, sebum thickness, hemoglobin, and glucose. Then, a dynamic prediction model was constructed to help clinicians simplify the monitoring and prevention of SSI.
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Affiliation(s)
- Tianyou Chen
- Department of Spine and Osteopathy Ward, the First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Chong Liu
- Department of Spine and Osteopathy Ward, the First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Zide Zhang
- Spine Ward, Liuzhou People’s Hospital, Liuzhou, People’s Republic of China
| | - Tuo Liang
- Spine Ward, Liuzhou People’s Hospital, Liuzhou, People’s Republic of China
| | - Jichong Zhu
- Department of Spine and Osteopathy Ward, the First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Chenxing Zhou
- Department of Spine and Osteopathy Ward, the First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Shaofeng Wu
- Department of Spine and Osteopathy Ward, the First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Yuanlin Yao
- Department of Spine and Osteopathy Ward, the First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Chengqian Huang
- Department of Spine and Osteopathy Ward, the First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Bin Zhang
- Department of Spine and Osteopathy Ward, the First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Sitan Feng
- Department of Spine and Osteopathy Ward, the First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Zequn Wang
- Department of Spine and Osteopathy Ward, the First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Shengsheng Huang
- Department of Spine and Osteopathy Ward, the First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Xuhua Sun
- Department of Spine and Osteopathy Ward, the First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Liyi Chen
- Department of Spine and Osteopathy Ward, the First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Xinli Zhan
- Department of Spine and Osteopathy Ward, the First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
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Hunter OF, Perry F, Salehi M, Bandurski H, Hubbard A, Ball CG, Morad Hameed S. Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care. World J Emerg Surg 2023; 18:16. [PMID: 36879293 PMCID: PMC9987401 DOI: 10.1186/s13017-022-00469-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 12/12/2022] [Indexed: 03/08/2023] Open
Abstract
Artificial intelligence (AI) and machine learning describe a broad range of algorithm types that can be trained based on datasets to make predictions. The increasing sophistication of AI has created new opportunities to apply these algorithms within within trauma care. Our paper overviews the current uses of AI along the continuum of trauma care, including injury prediction, triage, emergency department volume, assessment, and outcomes. Starting at the point of injury, algorithms are being used to predict severity of motor vehicle crashes, which can help inform emergency responses. Once on the scene, AI can be used to help emergency services triage patients remotely in order to inform transfer location and urgency. For the receiving hospital, these tools can be used to predict trauma volumes in the emergency department to help allocate appropriate staffing. After patient arrival to hospital, these algorithms not only can help to predict injury severity, which can inform decision-making, but also predict patient outcomes to help trauma teams anticipate patient trajectory. Overall, these tools have the capability to transform trauma care. AI is still nascent within the trauma surgery sphere, but this body of the literature shows that this technology has vast potential. AI-based predictive tools in trauma need to be explored further through prospective trials and clinical validation of algorithms.
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Affiliation(s)
- Olivia F Hunter
- Department of Surgery, University of British Columbia, Vancouver, Canada
| | - Frances Perry
- Department of Surgery, University of British Columbia, Vancouver, Canada
| | - Mina Salehi
- Department of Surgery, University of British Columbia, Vancouver, Canada
| | | | - Alan Hubbard
- University of California, Berkeley School of Public Health, Berkeley, USA
| | - Chad G Ball
- Department of Surgery, University of Calgary, Calgary, Canada
| | - S Morad Hameed
- Department of Surgery, University of British Columbia, Vancouver, Canada. .,T6 Health Systems, Boston, USA.
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Zou H, Yang W, Wang M, Zhu Q, Liang H, Wu H, Tang L. Predicting length of stay ranges by using novel deep neural networks. Heliyon 2023; 9:e13573. [PMID: 36852025 PMCID: PMC9958433 DOI: 10.1016/j.heliyon.2023.e13573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/11/2023] Open
Abstract
Background and aims Accurately predicting length of stay (LOS) is considered a challenging task for health care systems globally. In previous studies on LOS range prediction, researchers commonly pre-classified the LOS ranges, which were the same for all patients in the same classification, and then utilized a classifier for prediction. In this study, we innovatively aimed to predict the specific LOS range for each patient (the LOS range was different for each patient). Methods In the modified deep neural network (DNN), the overall sample error (root mean square error (RMSE) method), the estimated sample error (ERRpred method), the probability distribution with different loss functions (Dispred_Loss1, Dispred_Loss2, and Dispred_Loss3 method), and the generative adversarial networks (WGAN-GP for LOS method) are used for LOS range prediction. The Medical Information Mart for Intensive Care III (MIMIC-III) database is used to validate these methods. Results The RMSE method is convenient for LOS range prediction, but the predicted ranges are all consistent in the same batch of samples. The ERRpred method can achieve better prediction results in samples with low errors. However, the prediction effect is worse in samples with larger errors. The Dispred_Loss1 method encounters a training instability problem. The Dispred_Loss2 and Dispred_Loss3 methods perform well in making predictions. Although WGAN-GP for LOS method does not show a substantial advantage over other methods, this method might have the potential to improve the predictive performance. Conclusion The results show that it is possible to achieve an acceptable accurate LOS range prediction through a reasonable model design, which may help physicians in the clinic.
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Affiliation(s)
- Hong Zou
- Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China.,Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu 610044, Sichuan Province, China
| | - Wei Yang
- Department of Urology, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Meng Wang
- Department of Traditional Chinese Medicine, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Qiao Zhu
- Department of Obstetrics and Gynecology, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Hongyin Liang
- Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Hong Wu
- Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu 610044, Sichuan Province, China
| | - Lijun Tang
- Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
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Ossai CI, Rankin D, Wickramasinghe N. Preadmission assessment of extended length of hospital stay with RFECV-ETC and hospital-specific data. Eur J Med Res 2022; 27:128. [PMID: 35879803 PMCID: PMC9310419 DOI: 10.1186/s40001-022-00754-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 06/21/2022] [Indexed: 12/22/2022] Open
Abstract
Background Patients who exceed their expected length of stay in the hospital come at a cost to stakeholders in the healthcare sector as bed spaces are limited for new patients, nosocomial infections increase and the outcome for many patients is hampered due to multimorbidity after hospitalization. Objectives This paper develops a technique for predicting Extended Length of Hospital Stay (ELOHS) at preadmission and their risk factors using hospital data. Methods A total of 91,468 records of patient’s hospital information from a private acute teaching hospital were used for developing a machine learning algorithm relaying on Recursive Feature Elimination with Cross-Validation and Extra Tree Classifier (RFECV-ETC). The study implemented Synthetic Minority Oversampling Technique (SMOTE) and tenfold cross-validation to determine the optimal features for predicting ELOHS while relying on multivariate Logistic Regression (LR) for computing the risk factors and the Relative Risk (RR) of ELOHS at a 95% confidence level. Results An estimated 11.54% of the patients have ELOHS, which increases with patient age as patients < 18 years, 18–40 years, 40–65 years and ≥ 65 years, respectively, have 2.57%, 4.33%, 8.1%, and 15.18% ELOHS rates. The RFECV-ETC algorithm predicted preadmission ELOHS to an accuracy of 89.3%. Age is a predominant risk factors of ELOHS with patients who are > 90 years—PAG (> 90) {RR: 1.85 (1.34–2.56), P: < 0.001} having 6.23% and 23.3%, respectively, higher likelihood of ELOHS than patient 80–90 years old—PAG (80–90) {RR: 1.74 (1.34–2.38), P: < 0.001} and those 70–80 years old—PAG (70–80) {RR: 1.5 (1.1–2.05), P: 0.011}. Those from admission category—ADC (US1) {RR: 3.64 (3.09–4.28, P: < 0.001} are 14.8% and 70.5%, respectively, more prone to ELOHS compared to ADC (UC1) {RR: 3.17 (2.82–3.55), P: < 0.001} and ADC (EMG) {RR: 2.11 (1.93–2.31), P: < 0.001}. Patients from SES (low) {RR: 1.45 (1.24–1.71), P: < 0.001)} are 13.3% and 45% more susceptible to those from SES (middle) and SES (high). Admission type (ADT) such as AS2, M2, NEWS, S2 and others {RR: 1.37–2.77 (1.25–6.19), P: < 0.001} also have a high likelihood of contributing to ELOHS while the distance to hospital (DTH) {RR: 0.64–0.75 (0.56–0.82), P: < 0.001}, Charlson Score (CCI) {RR: 0.31–0.68 (0.22–0.99), P: < 0.001–0.043} and some VMO specialties {RR: 0.08–0.69 (0.03–0.98), P: < 0.001–0.035} have limited influence on ELOHS. Conclusions Relying on the preadmission assessment of ELOHS helps identify those patients who are susceptible to exceeding their expected length of stay on admission, thus, making it possible to improve patients’ management and outcomes.
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Multilayer dynamic ensemble model for intensive care unit mortality prediction of neonate patients. J Biomed Inform 2022; 135:104216. [DOI: 10.1016/j.jbi.2022.104216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 09/25/2022] [Accepted: 09/28/2022] [Indexed: 12/26/2022]
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Machine Learning in the Prediction of Trauma Outcomes: A Systematic Review. Ann Emerg Med 2022; 80:440-455. [PMID: 35842343 DOI: 10.1016/j.annemergmed.2022.05.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 03/20/2022] [Accepted: 05/04/2022] [Indexed: 11/23/2022]
Abstract
STUDY OBJECTIVE Machine learning models carry unique potential as decision-making aids and prediction tools for improving patient care. Traumatically injured patients provide a uniquely heterogeneous population with severe injuries that can be difficult to predict. Given the relative infancy of machine learning applications in medicine, this systematic review aimed to better understand the current state of machine learning development and implementation to help create a basis for future research. METHODS We conducted a systematic review from inception to May 2021, using Embase, MEDLINE through Ovid, Web of Science, Google Scholar, and relevant gray literature, for uses of machine learning in predicting the outcomes of trauma patients. The screening and data extraction were performed by 2 independent reviewers. RESULTS Of the 14,694 identified articles screened, 67 were included for data extraction. Artificial neural networks comprised the most commonly used model, and mortality was the most prevalent outcome of interest. In terms of machine learning model development, there was a lack of studies that employed external validation, feature selection methods, and performed formal calibration testing. Significant heterogeneity in reporting was also observed between the machine learning models employed, patient populations, performance metrics, and features employed. CONCLUSION This review highlights the heterogeneity in the development and reporting of machine learning models for the prediction of trauma outcomes. While these models present an area of opportunity as an ancillary to clinical decision-making, we recommend more standardization and rigorous guidelines for the development of future models.
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Fan G, Yang S, Liu H, Xu N, Chen Y, He J, Su X, Pang M, Liu B, Han L, Rong L. Machine Learning-based Prediction of Prolonged Intensive Care Unit Stay for Critical Patients with Spinal Cord Injury. Spine (Phila Pa 1976) 2022; 47:E390-E398. [PMID: 34690328 DOI: 10.1097/brs.0000000000004267] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
STUDY DESIGN A retrospective cohort study. OBJECTIVE The objective of the study was to develop machine-learning (ML) classifiers for predicting prolonged intensive care unit (ICU)-stay and prolonged hospital-stay for critical patients with spinal cord injury (SCI). SUMMARY OF BACKGROUND DATA Critical patients with SCI in ICU need more attention. SCI patients with prolonged stay in ICU usually occupy vast medical resources and hinder the rehabilitation deployment. METHODS A total of 1599 critical patients with SCI were included in the study and labeled with prolonged stay or normal stay. All data were extracted from the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care III-IV Database. The extracted data were randomly divided into training, validation and testing (6:2:2) subdatasets. A total of 91 initial ML classifiers were developed, and the top three initial classifiers with the best performance were further stacked into an ensemble classifier with logistic regressor. The area under the curve (AUC) was the main indicator to assess the prediction performance of all classifiers. The primary predicting outcome was prolonged ICU-stay, while the secondary predicting outcome was prolonged hospital-stay. RESULTS In predicting prolonged ICU-stay, the AUC of the ensemble classifier was 0.864 ± 0.021 in the three-time five-fold cross-validation and 0.802 in the independent testing. In predicting prolonged hospital-stay, the AUC of the ensemble classifier was 0.815 ± 0.037 in the three-time five-fold cross-validation and 0.799 in the independent testing. Decision curve analysis showed the merits of the ensemble classifiers, as the curves of the top three initial classifiers varied a lot in either predicting prolonged ICU-stay or discriminating prolonged hospital-stay. CONCLUSION The ensemble classifiers successfully predict the prolonged ICU-stay and the prolonged hospital-stay, which showed a high potential of assisting physicians in managing SCI patients in ICU and make full use of medical resources.Level of Evidence: 3.
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Affiliation(s)
- Guoxin Fan
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yatsen University, Guangzhou, China
- Intelligent and Digital Surgery Innovation Center, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, China
| | - Sheng Yang
- Department of Orthopedics, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Huaqing Liu
- Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China
| | - Ningze Xu
- Tongji University School of Medicine, Shanghai, P. R. China
| | - Yuyong Chen
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yatsen University, Guangzhou, China
- Intelligent and Digital Surgery Innovation Center, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, China
| | - Jie He
- Intelligent and Digital Surgery Innovation Center, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, China
| | - Xiuyun Su
- Intelligent and Digital Surgery Innovation Center, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, China
| | - Mao Pang
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yatsen University, Guangzhou, China
| | - Bin Liu
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yatsen University, Guangzhou, China
| | - Lanqing Han
- Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China
| | - Limin Rong
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yatsen University, Guangzhou, China
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Machine Learning and Antibiotic Management. Antibiotics (Basel) 2022; 11:antibiotics11030304. [PMID: 35326768 PMCID: PMC8944459 DOI: 10.3390/antibiotics11030304] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 02/07/2022] [Accepted: 02/18/2022] [Indexed: 11/17/2022] Open
Abstract
Machine learning and cluster analysis applied to the clinical setting of an intensive care unit can be a valuable aid for clinical management, especially with the increasing complexity of clinical monitoring. Providing a method to measure clinical experience, a proxy for that automatic gestalt evaluation that an experienced clinician sometimes effortlessly, but often only after long, hard consideration and consultation with colleagues, relies upon for decision making, is what we wanted to achieve with the application of machine learning to antibiotic therapy and clinical monitoring in the present work. This is a single-center retrospective analysis proposing methods for evaluation of vitals and antimicrobial therapy in intensive care patients. For each patient included in the present study, duration of antibiotic therapy, consecutive days of treatment and type and combination of antimicrobial agents have been assessed and considered as single unique daily record for analysis. Each parameter, composing a record was normalized using a fuzzy logic approach and assigned to five descriptive categories (fuzzy domain sub-sets ranging from “very low” to “very high”). Clustering of these normalized therapy records was performed, and each patient/day was considered to be a pertaining cluster. The same methodology was used for hourly bed-side monitoring. Changes in patient conditions (monitoring) can lead to a shift of clusters. This can provide an additional tool for assessing progress of complex patients. We used Fuzzy logic normalization to descriptive categories of parameters as a form nearer to human language than raw numbers.
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Su L, Xu Z, Chang F, Ma Y, Liu S, Jiang H, Wang H, Li D, Chen H, Zhou X, Hong N, Zhu W, Long Y. Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models. Front Med (Lausanne) 2021; 8:664966. [PMID: 34291058 PMCID: PMC8288021 DOI: 10.3389/fmed.2021.664966] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 05/20/2021] [Indexed: 12/20/2022] Open
Abstract
Background: Early prediction of the clinical outcome of patients with sepsis is of great significance and can guide treatment and reduce the mortality of patients. However, it is clinically difficult for clinicians. Methods: A total of 2,224 patients with sepsis were involved over a 3-year period (2016-2018) in the intensive care unit (ICU) of Peking Union Medical College Hospital. With all the key medical data from the first 6 h in the ICU, three machine learning models, logistic regression, random forest, and XGBoost, were used to predict mortality, severity (sepsis/septic shock), and length of ICU stay (LOS) (>6 days, ≤ 6 days). Missing data imputation and oversampling were completed on the dataset before introduction into the models. Results: Compared to the mortality and LOS predictions, the severity prediction achieved the best classification results, based on the area under the operating receiver characteristics (AUC), with the random forest classifier (sensitivity = 0.65, specificity = 0.73, F1 score = 0.72, AUC = 0.79). The random forest model also showed the best overall performance (mortality prediction: sensitivity = 0.50, specificity = 0.84, F1 score = 0.66, AUC = 0.74; LOS prediction: sensitivity = 0.79, specificity = 0.66, F1 score = 0.69, AUC = 0.76) among the three models. The predictive ability of the SOFA score itself was inferior to that of the above three models. Conclusions: Using the random forest classifier in the first 6 h of ICU admission can provide a comprehensive early warning of sepsis, which will contribute to the formulation and management of clinical decisions and the allocation and management of resources.
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Affiliation(s)
- Longxiang Su
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Zheng Xu
- Digital Health China Technologies Co., Ltd., Beijing, China
| | | | - Yingying Ma
- Digital Health China Technologies Co., Ltd., Beijing, China
| | - Shengjun Liu
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Huizhen Jiang
- Department of Information Center, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Hao Wang
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Dongkai Li
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Huan Chen
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Xiang Zhou
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Na Hong
- Digital Health China Technologies Co., Ltd., Beijing, China
| | - Weiguo Zhu
- Department of Information Center, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.,Department of Primary Care and Family Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yun Long
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
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