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Abstract
OBJECTIVE To summarize the current research progress of machine learning and venous thromboembolism. METHODS The literature on risk factors, diagnosis, prevention and prognosis of machine learning and venous thromboembolism in recent years was reviewed. RESULTS Machine learning is the future of biomedical research, personalized medicine, and computer-aided diagnosis, and will significantly promote the development of biomedical research and healthcare. However, many medical professionals are not familiar with it. In this review, we will introduce several commonly used machine learning algorithms in medicine, discuss the application of machine learning in venous thromboembolism, and reveal the challenges and opportunities of machine learning in medicine. CONCLUSION The incidence of venous thromboembolism is high, the diagnostic measures are diverse, and it is necessary to classify and treat machine learning, and machine learning as a research tool, it is more necessary to strengthen the special research of venous thromboembolism and machine learning.
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Affiliation(s)
- Shirong Zou
- West China Hospital of Medicine, West China Hospital Operation Room /West China School of Nursing, Sichuan University, Chengdu, China
| | - Zhoupeng Wu
- Department of vascular surgery, West China Hospital, Sichuan University, Chengdu, China
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Zhang J, Shao Y, Zhou H, Li R, Xu J, Xiao Z, Lu L, Cai L. Prediction model of deep vein thrombosis risk after lower extremity orthopedic surgery. Heliyon 2024; 10:e29517. [PMID: 38720714 PMCID: PMC11076659 DOI: 10.1016/j.heliyon.2024.e29517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 05/12/2024] Open
Abstract
Purpose This investigation was conceived to engineer and appraise a pioneering clinical nomogram, crafted to bridge the extant chasm in literature regarding the postoperative risk stratification for deep vein thrombosis (DVT) in the aftermath of lower extremity orthopedic procedures. This novel tool offers a sophisticated and discerning algorithm for risk prediction, heretofore unmet by existing methodologies. Methods In this retrospective observational study, clinical records of hospitalized patients who underwent lower extremity orthopedic surgery were collected at the Wuxi TCM Hospital Affiliated to the Nanjing University of Chinese Medicine between Jan 2017 and Oct 2019. The univariate and multivariate analysis with the backward stepwise method was applied to select features for the predictive nomogram. The performance of the nomogram was evaluated with respect to its discriminant capability, calibration ability, and clinical utility. Result A total of 5773 in-hospital patients were eligible for the study, with the incidence of deep vein thrombosis being approximately 1 % in this population. Among 31 variables included, 5 of them were identified to be the predictive features in the nomogram, including age, mean corpuscular hemoglobin concentration (MCHC), D-dimer, platelet distribution width (PDW), and thrombin time (TT). The area under the receiver operating characteristic (ROC) curve in the training and validation cohort was 85.9 % (95%CI: 79.96 %-90.04 %) and 85.7 % (95%CI: 78.96 %-90.69 %), respectively. Both the calibration curves and decision curve analysis demonstrated the overall satisfactory performance of the model. Conclusion Our groundbreaking nomogram is distinguished by its unparalleled accuracy in discriminative and calibrating functions, complemented by its tangible clinical applicability. This innovative instrument is set to empower clinicians with a robust framework for the accurate forecasting of postoperative DVT, thus facilitating the crafting of bespoke and prompt therapeutic strategies, aligning with the rigorous standards upheld by the most esteemed biomedical journals.
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Affiliation(s)
- Jiannan Zhang
- Department of Anesthesiology, Wuxi TCM Hospital, Wuxi, 214071, PR China
| | - Yang Shao
- Department of Anesthesiology, Wuxi TCM Hospital, Wuxi, 214071, PR China
| | - Hongmei Zhou
- Department of Anesthesiology, Wuxi TCM Hospital, Wuxi, 214071, PR China
| | - Ronghua Li
- Department of Anesthesiology, Wuxi TCM Hospital, Wuxi, 214071, PR China
| | - Jie Xu
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, PR China
- Université de Montpellier, Montpellier, Languedoc-Roussillon, France
| | - Zhongzhou Xiao
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, PR China
| | - Lu Lu
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, PR China
| | - Liangyu Cai
- Department of Anesthesiology, Wuxi TCM Hospital, Wuxi, 214071, PR China
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Danilatou V, Dimopoulos D, Kostoulas T, Douketis J. Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review. Thromb Haemost 2024. [PMID: 38574756 DOI: 10.1055/a-2299-4758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
BACKGROUND Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment. METHODS Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included. RESULTS Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison. CONCLUSION ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.
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Affiliation(s)
- Vasiliki Danilatou
- School of Medicine, European University of Cyprus, Nicosia, Cyprus
- Healthcare Division, Sphynx Technology Solutions, Nicosia, Cyprus
| | - Dimitrios Dimopoulos
- School of Engineering, Department of Information and Communication Systems Engineering, University of the Aegean, North Aegean, Greece
| | - Theodoros Kostoulas
- School of Engineering, Department of Information and Communication Systems Engineering, University of the Aegean, North Aegean, Greece
| | - James Douketis
- Department of Medicine, McMaster University, Hamilton, Canada
- Department of Medicine, St. Joseph's Healthcare Hamilton, Ontario, Canada
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Wei C, Wang J, Yu P, Li A, Xiong Z, Yuan Z, Yu L, Luo J. Comparison of different machine learning classification models for predicting deep vein thrombosis in lower extremity fractures. Sci Rep 2024; 14:6901. [PMID: 38519523 PMCID: PMC10960026 DOI: 10.1038/s41598-024-57711-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 03/21/2024] [Indexed: 03/25/2024] Open
Abstract
Deep vein thrombosis (DVT) is a common complication in patients with lower extremity fractures. Once it occurs, it will seriously affect the quality of life and postoperative recovery of patients. Therefore, early prediction and prevention of DVT can effectively improve the prognosis of patients. This study constructed different machine learning models to explore their effectiveness in predicting DVT. Five prediction models were applied to the study, including Extreme Gradient Boosting (XGBoost) model, Logistic Regression (LR) model, RandomForest (RF) model, Multilayer Perceptron (MLP) model, and Support Vector Machine (SVM) model. Afterwards, the performance of the obtained prediction models was evaluated by area under the curve (AUC), accuracy, sensitivity, specificity, F1 score, and Kappa. The prediction performances of the models based on machine learning are as follows: XGBoost model (AUC = 0.979, accuracy = 0.931), LR model (AUC = 0.821, accuracy = 0.758), RF model (AUC = 0.970, accuracy = 0.921), MLP model (AUC = 0.830, accuracy = 0.756), SVM model (AUC = 0.713, accuracy = 0.661). On our data set, the XGBoost model has the best performance. However, the model still needs external verification research before clinical application.
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Affiliation(s)
- Conghui Wei
- Department of Rehabilitation Medicine, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Jialiang Wang
- Department of Rehabilitation Medicine, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Pengfei Yu
- Department of Rehabilitation Medicine, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Ang Li
- Department of Rehabilitation Medicine, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Ziying Xiong
- Department of Rehabilitation Medicine, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Zhen Yuan
- Department of Rehabilitation Medicine, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Lingling Yu
- Department of Rehabilitation Medicine, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China.
| | - Jun Luo
- Department of Rehabilitation Medicine, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China.
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Xi L, Kang H, Deng M, Xu W, Xu F, Gao Q, Xie W, Zhang R, Liu M, Zhai Z, Wang C. A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score, revised Geneva score, and Years algorithm. Chin Med J (Engl) 2024; 137:676-682. [PMID: 37828028 PMCID: PMC10950185 DOI: 10.1097/cm9.0000000000002837] [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: 05/09/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Acute pulmonary embolism (APE) is a fatal cardiovascular disease, yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs. A simple, objective technique will help clinicians make a quick and precise diagnosis. In population studies, machine learning (ML) plays a critical role in characterizing cardiovascular risks, predicting outcomes, and identifying biomarkers. This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models. METHODS This is a single-center retrospective study. Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets. A total of 8 ML models, including random forest (RF), Naïve Bayes, decision tree, K-nearest neighbors, logistic regression, multi-layer perceptron, support vector machine, and gradient boosting decision tree were developed based on the training set to diagnose APE. Thereafter, the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies, including the Wells score, revised Geneva score, and Years algorithm. Eventually, the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic (ROC) analysis. RESULTS The ML models were constructed using eight clinical features, including D-dimer, cardiac troponin T (cTNT), arterial oxygen saturation, heart rate, chest pain, lower limb pain, hemoptysis, and chronic heart failure. Among eight ML models, the RF model achieved the best performance with the highest area under the curve (AUC) (AUC = 0.774). Compared to the current clinical assessment strategies, the RF model outperformed the Wells score ( P = 0.030) and was not inferior to any other clinical probability assessment strategy. The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726. CONCLUSIONS Based on RF algorithm, a novel prediction model was finally constructed for APE diagnosis. When compared to the current clinical assessment strategies, the RF model achieved better diagnostic efficacy and accuracy. Therefore, the ML algorithm can be a useful tool in assisting with the diagnosis of APE.
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Affiliation(s)
- Linfeng Xi
- Capital Medical University, Beijing 100069, China
- National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China
| | - Han Kang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing 100025, China
| | - Mei Deng
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China
- Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Wenqing Xu
- Department of Radiology, Peking University China-Japan Friendship School of Clinical Medicine, Beijing 100191, China
| | - Feiya Xu
- Capital Medical University, Beijing 100069, China
- National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China
| | - Qian Gao
- National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China
| | - Wanmu Xie
- National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China
| | - Rongguo Zhang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing 100025, China
| | - Min Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China
| | - Zhenguo Zhai
- National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China
| | - Chen Wang
- Capital Medical University, Beijing 100069, China
- National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China
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Puchades R, Tung-Chen Y, Salgueiro G, Lorenzo A, Sancho T, Fernández Capitán C. Artificial intelligence for predicting pulmonary embolism: A review of machine learning approaches and performance evaluation. Thromb Res 2024; 234:9-11. [PMID: 38113607 DOI: 10.1016/j.thromres.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 12/02/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023]
Affiliation(s)
- Ramón Puchades
- Internal Medicine Service, Thromboembolic Disease Unit, La Paz University Hospital, Madrid, Spain.
| | - Yale Tung-Chen
- Internal Medicine Service, Thromboembolic Disease Unit, La Paz University Hospital, Madrid, Spain
| | - Giorgina Salgueiro
- Internal Medicine Service, Thromboembolic Disease Unit, La Paz University Hospital, Madrid, Spain
| | - Alicia Lorenzo
- Internal Medicine Service, Thromboembolic Disease Unit, La Paz University Hospital, Madrid, Spain
| | - Teresa Sancho
- Internal Medicine Service, Thromboembolic Disease Unit, La Paz University Hospital, Madrid, Spain
| | - Carmen Fernández Capitán
- Internal Medicine Service, Thromboembolic Disease Unit, La Paz University Hospital, Madrid, Spain
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Nassour N, Akhbari B, Ranganathan N, Shin D, Ghaednia H, Ashkani-Esfahani S, DiGiovanni CW, Guss D. Using machine learning in the prediction of symptomatic venous thromboembolism following ankle fracture. Foot Ankle Surg 2024; 30:110-116. [PMID: 38193887 DOI: 10.1016/j.fas.2023.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/31/2023] [Accepted: 10/13/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND Venous thromboembolism (VTE) is a major cause of morbidity and mortality in the trauma setting, and both prediction and prevention of VTE have long been a concern for healthcare providers in orthopedic surgery. The purpose of this study was to evaluate the use of novel statistical analysis and machine-learning in predicting the risk of VTE and the usefulness of prophylaxis following ankle fractures. METHODS The medical profiles of 16,421 patients with ankle fractures were screened retrospectively for symptomatic VTE. In total, 238 patients sustaining either surgical or nonsurgical treatment for ankle fracture with subsequently confirmed VTE within 180 days following the injury were placed in the case group. Alternatively, 937 patients who sustained ankle fractures managed similarly but had no documented evidence of VTE were randomly chosen as the control group. Individuals from both the case and control populations were also divided into those who had received VTE prophylaxis and those who had not. Over 110 variables were included. Conventional statistics and machine learning methods were used for data analysis. RESULTS Patients who had a motor vehicle accident, surgical treatment, increased hospital stay, and were on warfarin were shown to have a higher incidence of VTE, whereas patients who were on statins had a lower incidence of VTE. The highest Area Under the Receiver Operating Characteristic Curves (AUROC) showing the performance of our machine learning approach was 0.88 with 0.94 sensitivity and 0.36 specificity. The most balanced performance was seen in a model that was trained using selected variables with 0.86 AUROC, 0.75 sensitivity, and 0.85 specificity. CONCLUSION By using machine learning, this study successfully pinpointed several predictive factors linked to the occurrence or absence of VTE in patients who experienced an ankle fracture. Training these algorithms using larger, more granular, and multicentric data will further increase their validity and reliability and should be considered the standard for the development of such algorithms. LEVEL OF EVIDENCE Case-Control study - 3.
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Affiliation(s)
- Nour Nassour
- Foot & Ankle Research and Innovation Laboratory (FARIL), Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
| | - Bardiya Akhbari
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Noopur Ranganathan
- Foot & Ankle Research and Innovation Laboratory (FARIL), Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - David Shin
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Hamid Ghaednia
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Soheil Ashkani-Esfahani
- Foot & Ankle Research and Innovation Laboratory (FARIL), Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Foot and Ankle Division, Department of Orthopaedic Surgery, Massachusetts General Hospital, Newton Wellesley Hospital, Harvard Medical School, Boston, MA, USA
| | - Christopher W DiGiovanni
- Foot & Ankle Research and Innovation Laboratory (FARIL), Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Foot and Ankle Division, Department of Orthopaedic Surgery, Massachusetts General Hospital, Newton Wellesley Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel Guss
- Foot & Ankle Research and Innovation Laboratory (FARIL), Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Foot and Ankle Division, Department of Orthopaedic Surgery, Massachusetts General Hospital, Newton Wellesley Hospital, Harvard Medical School, Boston, MA, USA
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Jiao Y, Mu X. Coagulation parameters correlate to venous thromboembolism occurrence during the perioperative period in patients with spinal fractures. J Orthop Surg Res 2023; 18:928. [PMID: 38057818 DOI: 10.1186/s13018-023-04407-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 11/25/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Venous thromboembolism (VTE) is one of the leading causes of mortality in hospitalized patients. However, whether the coagulation-related parameters of the hospitalized patients could be used to predict the occurrence of VTE in patients with spinal injury surgery remained unclear. METHOD The patients with spinal fractures who met the inclusion and exclusion criteria were enrolled to be analyzed using a retrospective analysis approach. The association of risk factors of enrolled patients and operations to VTE occurrence were analyzed. The activated partial thromboplastin time, prothrombin time, thrombin time, D-dimer (D-D), fibrinogen (FIB) and fibrinogen degradation products (FDP) were detected. ROC and HR analysis were applied to evaluate the correlation of coagulation-related parameters and other parameters to VTE occurrence. RESULT The indicators of D-D, FIB and FDP were significantly elevated in VTE patients compared to non-VTE patients. The multivariate analysis of OR showed that six risk factors, including age ≥ 60, spinal cord injury, postoperative bedtime over 5 days, plasma D-dimer ≥ 0.54 mg/L, plasma fibrinogen ≥ 3.75 g/L and plasma FDP ≥ 5.19 mg/L, were positively correlated to VTE. CONCLUSION The six risk factors, including D-D, FIB, FDP, age ≥ 60, spinal cord injury, and postoperative bedtime over 5 days, could be used to predict the occurrence of VTE.
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Affiliation(s)
- Yong Jiao
- Department of Orthopedics, Dongzhimen Hospital, Beijing University of Traditional Chinese Medicine, No. 5 Haihai Warehouse, Dongzhimen, Beijing, 100000, China
| | - Xiaohong Mu
- Department of Orthopedics, Dongzhimen Hospital, Beijing University of Traditional Chinese Medicine, No. 5 Haihai Warehouse, Dongzhimen, Beijing, 100000, China.
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Meng L, Wei T, Fan R, Su H, Liu J, Wang L, Huang X, Qi Y, Li X. Development and validation of a machine learning model to predict venous thromboembolism among hospitalized cancer patients. Asia Pac J Oncol Nurs 2022; 9:100128. [PMID: 36276886 PMCID: PMC9583033 DOI: 10.1016/j.apjon.2022.100128] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 07/30/2022] [Indexed: 11/02/2022] Open
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Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis. MATHEMATICAL AND COMPUTATIONAL APPLICATIONS 2022. [DOI: 10.3390/mca27020024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Deep venous thrombosis (DVT) is a disease that must be diagnosed quickly, as it can trigger the death of patients. Nowadays, one can find different ways to determine it, including clinical scoring, D-dimer, ultrasonography, etc. Recently, scientists have focused efforts on using machine learning (ML) and neural networks for disease diagnosis, progressively increasing the accuracy and efficacy. Patients with suspected DVT have no apparent symptoms. Using pattern recognition techniques, aiding good timely diagnosis, as well as well-trained ML models help to make good decisions and validation. The aim of this paper is to propose several ML models for a more efficient and reliable DVT diagnosis through its implementation on an edge device for the development of instruments that are smart, portable, reliable, and cost-effective. The dataset was obtained from a state-of-the-art article. It is divided into 85% for training and cross-validation and 15% for testing. The input data in this study are the Wells criteria, the patient’s age, and the patient’s gender. The output data correspond to the patient’s diagnosis. This study includes the evaluation of several classifiers such as Decision Trees (DT), Extra Trees (ET), K-Nearest Neighbor (KNN), Multi-Layer Perceptron Neural Network (MLP-NN), Random Forest (RF), and Support Vector Machine (SVM). Finally, the implementation of these ML models on a high-performance embedded system is proposed to develop an intelligent system for early DVT diagnosis. It is reliable, portable, open source, and low cost. The performance of different ML algorithms was evaluated, where KNN achieved the highest accuracy of 90.4% and specificity of 80.66% implemented on personal computer (PC) and Raspberry Pi 4 (RPi4). The accuracy of all trained models on PC and Raspberry Pi 4 is greater than 85%, while the area under the curve (AUC) values are between 0.81 and 0.86. In conclusion, as compared to traditional methods, the best ML classifiers are effective at predicting DVT in an early and efficient manner.
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Chelazzi C, Villa G, Manno A, Ranfagni V, Gemmi E, Romagnoli S. The new SUMPOT to predict postoperative complications using an Artificial Neural Network. Sci Rep 2021; 11:22692. [PMID: 34811383 PMCID: PMC8608915 DOI: 10.1038/s41598-021-01913-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 10/28/2021] [Indexed: 12/24/2022] Open
Abstract
An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality in high-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial Neural Network technology to identify patients at risk for postoperative complications. We developed the new SUMPOT based on risk factors previously used in other scoring systems and tested it in a cohort of 560 surgical patients undergoing elective or emergency procedures and subsequently admitted to intensive care units, high-dependency units or standard wards. The whole dataset was divided into a training set, to train the predictive model, and a testing set, to assess generalization performance. The effectiveness of the Artificial Neural Network is a measure of the accuracy in detecting those patients who will develop postoperative complications. A total of 560 surgical patients entered the analysis. Among them, 77 patients (13.7%) suffered from one or more postoperative complications (PoCs), while 483 patients (86.3%) did not. The trained Artificial Neural Network returned an average classification accuracy of 90% in the testing set. Specifically, classification accuracy was 90.2% in the control group (46 patients out of 51 were correctly classified) and 88.9% in the PoC group (8 patients out of 9 were correctly classified). The Artificial Neural Network showed good performance in predicting presence/absence of postoperative complications, suggesting its potential value for perioperative management of surgical patients. Further clinical studies are required to confirm its applicability in routine clinical practice.
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Affiliation(s)
- Cosimo Chelazzi
- Department of Anesthesia and Intensive Care, Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Gianluca Villa
- Department of Anesthesia and Intensive Care, Azienda Ospedaliero Universitaria Careggi, Florence, Italy
- Department of Health Sciences, Section of Anesthesiology, Intensive Care and Pain Medicine, University of Florence, Florence, Italy
| | - Andrea Manno
- Center of Excellence Dews, Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila, Italy.
| | - Viola Ranfagni
- Department of Health Sciences, Section of Anesthesiology, Intensive Care and Pain Medicine, University of Florence, Florence, Italy
| | - Eleonora Gemmi
- Department of Health Sciences, Section of Anesthesiology, Intensive Care and Pain Medicine, University of Florence, Florence, Italy
| | - Stefano Romagnoli
- Department of Anesthesia and Intensive Care, Azienda Ospedaliero Universitaria Careggi, Florence, Italy
- Department of Health Sciences, Section of Anesthesiology, Intensive Care and Pain Medicine, University of Florence, Florence, Italy
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Villacorta H, Pickering JW, Horiuchi Y, Olim M, Coyne C, Maisel AS, Than MP. Machine learning with D-dimer in the risk stratification for pulmonary embolism: a derivation and internal validation study. EUROPEAN HEART JOURNAL-ACUTE CARDIOVASCULAR CARE 2021; 11:13-19. [PMID: 34697635 DOI: 10.1093/ehjacc/zuab089] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/21/2021] [Accepted: 09/27/2021] [Indexed: 11/13/2022]
Abstract
AIM To develop a machine learning model to predict the diagnosis of pulmonary embolism (PE). METHODS AND RESULTS We undertook a derivation and internal validation study to develop a risk prediction model for use in patients being investigated for possible PE. The machine learning technique, generalized logistic regression using elastic net, was chosen following an assessment of seven machine learning techniques and on the basis that it optimized the area under the receiver operator characteristic curve (AUC) and Brier score. Models were developed both with and without the addition of D-dimer. A total of 3347 patients were included in the study of whom, 219 (6.5%) had PE. Four clinical variables (O2 saturation, previous deep venous thrombosis or PE, immobilization or surgery, and alternative diagnosis equal or more likely than PE) plus D-dimer contributed to the machine learning models. The addition of D-dimer improved the AUC by 0.16 (95% confidence interval 0.13-0.19), from 0.73 to 0.89 (0.87-0.91) and decreased the Brier score by 14% (10-18%). More could be ruled out with a higher positive likelihood ratio than by the Wells score combined with D-dimer, revised Geneva score combined with D-dimer, or the Pulmonary Embolism Rule-out Criteria score. Machine learning with D-dimer maintained a low-false-negative rate at a true-negative rate of nearly 53%, which was better performance than any of the other alternatives. CONCLUSION A machine learning model outperformed traditional risk scores for the risk stratification of PE in the emergency department. However, external validation is needed.
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Affiliation(s)
- Humberto Villacorta
- Division of Cardiology, Department of Clinical Medicine, Fluminense Federal University, Rua Marquês do Paraná 303, Niterói, Rio de Janeiro CEP 24033-900, Brazil
| | - John W Pickering
- Emergency Department, Christchurch Hospital, Riccarton Avenue, Christchurch 8011, New Zealand.,Department of Medicine, University of Otago, Christchurch, 2 Riccarton Road, Christchurch 8011, New Zealand
| | - Yu Horiuchi
- Division of Cardiology, Department of Medicine, Mitsui Memorial Hospital, Kanda-Izumicho 1, Chiyoda-ku, Tokyo, 101-8643, Japan
| | - Moshe Olim
- Brainstorm Medical, Inc., 2235 Montgomery Ave Cardiff By The Sea, San Diego, CA, 92007-1913, USA
| | - Christopher Coyne
- Emergency Medicine, Department of Medicine, University of California San Diego, 200 W. Arbor Drive 8676, San Diego, CA, 92103, USA
| | - Alan S Maisel
- Brainstorm Medical, Inc., 2235 Montgomery Ave Cardiff By The Sea, San Diego, CA, 92007-1913, USA.,Division of Cardiovascular Medicine, Department of Medicine, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92037-7411
| | - Martin P Than
- Emergency Department, Christchurch Hospital, Riccarton Avenue, Christchurch 8011, New Zealand
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