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Qian R, Zhuang J, Xie J, Cheng H, Ou H, Lu X, Ouyang Z. Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis. Heliyon 2024; 10:e29603. [PMID: 38655348 PMCID: PMC11035062 DOI: 10.1016/j.heliyon.2024.e29603] [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: 09/18/2023] [Revised: 04/02/2024] [Accepted: 04/10/2024] [Indexed: 04/26/2024] Open
Abstract
Background Predicting the severity of acute pancreatitis (AP) early poses a challenge in clinical practice. While there are well-established clinical scoring tools, their actual predictive performance remains uncertain. Various studies have explored the application of machine-learning methods for early AP prediction. However, a more comprehensive evidence-based assessment is needed to determine their predictive accuracy. Hence, this systematic review and meta-analysis aimed to evaluate the predictive accuracy of machine learning in assessing the severity of AP. Methods PubMed, EMBASE, Cochrane Library, and Web of Science were systematically searched until December 5, 2023. The risk of bias in eligible studies was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analyses, based on different machine learning types, were performed. Additionally, the predictive accuracy of mainstream scoring tools was summarized. Results This systematic review ultimately included 33 original studies. The pooled c-index in both the training and validation sets was 0.87 (95 % CI: 0.84-0.89) and 0.88 (95 % CI: 0.86-0.90), respectively. The sensitivity in the training set was 0.81 (95 % CI: 0.77-0.84), and in the validation set, it was 0.79 (95 % CI: 0.71-0.85). The specificity in the training set was 0.84 (95 % CI: 0.78-0.89), and in the validation set, it was 0.90 (95 % CI: 0.86-0.93). The primary model incorporated was logistic regression; however, its predictive accuracy was found to be inferior to that of neural networks, random forests, and xgboost. The pooled c-index of the APACHE II, BISAP, and Ranson were 0.74 (95 % CI: 0.68-0.80), 0.77 (95 % CI: 0.70-0.85), and 0.74 (95 % CI: 0.68-0.79), respectively. Conclusions Machine learning demonstrates excellent accuracy in predicting the severity of AP, providing a reference for updating or developing a straightforward clinical prediction tool.
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Affiliation(s)
- Rui Qian
- Department of Gastroenterology, Shenzhen Bao'an Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518000, China
| | - Jiamei Zhuang
- The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, 518033, China
| | - Jianjun Xie
- Department of Gastroenterology, Shenzhen Bao'an Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518000, China
| | - Honghui Cheng
- Department of Gastroenterology, Shenzhen Bao'an Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518000, China
| | - Haiya Ou
- Department of Gastroenterology, Shenzhen Bao'an Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518000, China
| | - Xiang Lu
- Department of Plumonary and Critical Care Medicine, Shenzhen Bao'an Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518000, China
| | - Zichen Ouyang
- Department of Hepatology, Shenzhen Bao'an Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518000, China
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Rahmatinejad Z, Dehghani T, Hoseini B, Rahmatinejad F, Lotfata A, Reihani H, Eslami S. A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department. Sci Rep 2024; 14:3406. [PMID: 38337000 PMCID: PMC10858239 DOI: 10.1038/s41598-024-54038-4] [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: 09/14/2023] [Accepted: 02/07/2024] [Indexed: 02/12/2024] Open
Abstract
This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based on logistic regression (LR) models, have been proposed to indicate patient illness severity, this study aims to compare the predictive performance of ensemble learning (EL) models with LR for in-hospital mortality in the ED. A cross-sectional single-center study was conducted at the ED of Imam Reza Hospital in northeast Iran from March 2016 to March 2017. The study included adult patients with one to three levels of emergency severity index. EL models using Bagging, AdaBoost, random forests (RF), Stacking and extreme gradient boosting (XGB) algorithms, along with an LR model, were constructed. The training and validation visits from the ED were randomly divided into 80% and 20%, respectively. After training the proposed models using tenfold cross-validation, their predictive performance was evaluated. Model performance was compared using the Brier score (BS), The area under the receiver operating characteristics curve (AUROC), The area and precision-recall curve (AUCPR), Hosmer-Lemeshow (H-L) goodness-of-fit test, precision, sensitivity, accuracy, F1-score, and Matthews correlation coefficient (MCC). The study included 2025 unique patients admitted to the hospital's ED, with a total percentage of hospital deaths at approximately 19%. In the training group and the validation group, 274 of 1476 (18.6%) and 152 of 728 (20.8%) patients died during hospitalization, respectively. According to the evaluation of the presented framework, EL models, particularly Bagging, predicted in-hospital mortality with the highest AUROC (0.839, CI (0.802-0.875)) and AUCPR = 0.64 comparable in terms of discrimination power with LR (AUROC (0.826, CI (0.787-0.864)) and AUCPR = 0.61). XGB achieved the highest precision (0.83), sensitivity (0.831), accuracy (0.842), F1-score (0.833), and the highest MCC (0.48). Additionally, the most accurate models in the unbalanced dataset belonged to RF with the lowest BS (0.128). Although all studied models overestimate mortality risk and have insufficient calibration (P > 0.05), stacking demonstrated relatively good agreement between predicted and actual mortality. EL models are not superior to LR in predicting in-hospital mortality in the ED. Both EL and LR models can be considered as screening tools to identify patients at risk of mortality.
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Affiliation(s)
- Zahra Rahmatinejad
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Toktam Dehghani
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Toos Institute of Higher Education, Mashhad, Iran
| | - Benyamin Hoseini
- Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fatemeh Rahmatinejad
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Aynaz Lotfata
- Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, CA, USA
| | - Hamidreza Reihani
- Department of Emergency Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Saeid Eslami
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
- Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
- Department of Medical Informatics, Amsterdam UMC - Location AMC, University of Amsterdam, Amsterdam, The Netherlands.
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Chang CH, Chen CJ, Ma YS, Shen YT, Sung MI, Hsu CC, Lin HJ, Chen ZC, Huang CC, Liu CF. Real-time artificial intelligence predicts adverse outcomes in acute pancreatitis in the emergency department: Comparison with clinical decision rule. Acad Emerg Med 2024; 31:149-155. [PMID: 37885118 DOI: 10.1111/acem.14824] [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: 05/06/2023] [Revised: 10/10/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023]
Abstract
OBJECTIVE Artificial intelligence (AI) prediction is increasingly used for decision making in health care, but its application for adverse outcomes in emergency department (ED) patients with acute pancreatitis (AP) is not well understood. This study aimed to clarify this aspect. METHODS Data from 8274 ED patients with AP in three hospitals from 2009 to 2018 were analyzed. Demographic data, comorbidities, laboratory results, and adverse outcomes were included. Six algorithms were evaluated, and the one with the highest area under the curve (AUC) was implemented into the hospital information system (HIS) for real-time prediction. Predictive accuracy was compared between the AI model and Bedside Index for Severity in Acute Pancreatitis (BISAP). RESULTS The mean ± SD age was 56.1 ± 16.7 years, with 67.7% being male. The AI model was successfully implemented in the HIS, with Light Gradient Boosting Machine (LightGBM) showing the highest AUC for sepsis (AUC 0.961) and intensive care unit (ICU) admission (AUC 0.973), and eXtreme Gradient Boosting (XGBoost) showing the highest AUC for mortality (AUC 0.975). Compared to BISAP, the AI model had superior AUC for sepsis (BISAP 0.785), ICU admission (BISAP 0.778), and mortality (BISAP 0.817). CONCLUSIONS The first real-time AI prediction model implemented in the HIS for predicting adverse outcomes in ED patients with AP shows favorable initial results. However, further external validation is needed to ensure its reliability and accuracy.
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Affiliation(s)
- Ching-Hung Chang
- Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chia-Jung Chen
- Department of Information Systems, Chi Mei Medical Center, Tainan, Taiwan
| | - Yu-Shan Ma
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
| | - Yu-Ting Shen
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
| | - Mei-I Sung
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
| | - Chien-Chin Hsu
- Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan
| | - Hung-Jung Lin
- Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan
- Department of Emergency Medicine, Taipei Medical University, Taipei, Taiwan
| | - Zhih-Cherng Chen
- School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan
- Division of Cardiology, Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chien-Cheng Huang
- Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan
- Department of Emergency Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
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Váncsa S, Sipos Z, Váradi A, Nagy R, Ocskay K, Juhász FM, Márta K, Teutsch B, Mikó A, Hegyi PJ, Vincze Á, Izbéki F, Czakó L, Papp M, Hamvas J, Varga M, Török I, Mickevicius A, Erőss B, Párniczky A, Szentesi A, Pár G, Hegyi P. Metabolic-associated fatty liver disease is associated with acute pancreatitis with more severe course: Post hoc analysis of a prospectively collected international registry. United European Gastroenterol J 2023; 11:371-382. [PMID: 37062947 PMCID: PMC10165320 DOI: 10.1002/ueg2.12389] [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: 01/26/2023] [Accepted: 03/21/2023] [Indexed: 04/18/2023] Open
Abstract
INTRODUCTION Non-alcoholic fatty liver disease (NAFLD) is a proven risk factor for acute pancreatitis (AP). However, NAFLD has recently been redefined as metabolic-associated fatty liver disease (MAFLD). In this post hoc analysis, we quantified the effect of MAFLD on the outcomes of AP. METHODS We identified our patients from the multicentric, prospective International Acute Pancreatitis Registry of the Hungarian Pancreatic Study Group. Next, we compared AP patients with and without MAFLD and the individual components of MAFLD regarding in-hospital mortality and AP severity based on the revised Atlanta classification. Lastly, we calculated odds ratios (ORs) with 95% confidence intervals (CIs) using multivariate logistic regression analysis. RESULTS MAFLD had a high prevalence in AP, 39% (801/2053). MAFLD increased the odds of moderate-to-severe AP (OR = 1.43, CI: 1.09-1.89). However, the odds of in-hospital mortality (OR = 0.89, CI: 0.42-1.89) and severe AP (OR = 1.70, CI: 0.97-3.01) were not higher in the MAFLD group. Out of the three diagnostic criteria of MAFLD, the highest odds of severe AP was in the group based on metabolic risk abnormalities (OR = 2.68, CI: 1.39-5.09). In addition, the presence of one, two, and three diagnostic criteria dose-dependently increased the odds of moderate-to-severe AP (OR = 1.23, CI: 0.88-1.70, OR = 1.38, CI: 0.93-2.04, and OR = 3.04, CI: 1.63-5.70, respectively) and severe AP (OR = 1.13, CI: 0.54-2.27, OR = 2.08, CI: 0.97-4.35, and OR = 4.76, CI: 1.50-15.4, respectively). Furthermore, in patients with alcohol abuse and aged ≥60 years, the effect of MAFLD became insignificant. CONCLUSIONS MAFLD is associated with AP severity, which varies based on the components of its diagnostic criteria. Furthermore, MAFLD shows a dose-dependent effect on the outcomes of AP.
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Early prediction of the severe course, survival, and ICU requirements in acute pancreatitis by artificial intelligence. Pancreatology 2023; 23:176-186. [PMID: 36610872 DOI: 10.1016/j.pan.2022.12.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 11/20/2022] [Accepted: 12/12/2022] [Indexed: 01/09/2023]
Abstract
OBJECTIVE To evaluate the success of artificial intelligence for early prediction of severe course, survival, and intensive care unit(ICU) requirement in patients with acute pancreatitis(AP). METHODS Retrospectively, 1334 patients were included the study. Severity is determined according to the Revised Atlanta Classification(RAC). The success of machine learning(ML) method was evaluated by 13 simple demographic, clinical, etiologic, and laboratory features obtained on ER admission. Additionally, it was evaluated whether Balthazar-computerized tomography severity index(CTSI) at 48-h contributed to success. The dataset was split into two parts, 90% for ML(of which 70% for learning and 30% for testing) and 10% for validation and 5-fold stratified sampling has been utilized. Variable Importance was used in the selection of features during training phase of machine. The Gradient Boost Algorithm trained the machine by KNIME analytics platform. SMOTE has been applied to increase the minority classes for training. The combined effects of the measured features were examined by multivariate logistic regression analysis and reciever operating curve curves of the prediction and confidence of the target variables were obtained. RESULTS Accuracy values for the early estimation of Atlanta severity score, ICU requirement, and survival were found as 88.20%, 98.25%, and 92.77% respectively. When Balthazar-CTSI score is used, results were found as 91.02%, 92.25%, and 98% respectively. CONCLUSIONS The ML method we used successfully predicted the severe course, ICU requirement and survival, with promising accuracy values of over 88%. If 48-h Balthazar-CTSI is included in the calculation, the severity score and survival rates increase even more.
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Chen Z, Wang Y, Zhang H, Yin H, Hu C, Huang Z, Tan Q, Song B, Deng L, Xia Q. Deep Learning Models for Severity Prediction of Acute Pancreatitis in the Early Phase From Abdominal Nonenhanced Computed Tomography Images. Pancreas 2023; 52:e45-e53. [PMID: 37378899 DOI: 10.1097/mpa.0000000000002216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
OBJECTIVES To develop and validate deep learning (DL) models for predicting the severity of acute pancreatitis (AP) by using abdominal nonenhanced computed tomography (CT) images. METHODS The study included 978 AP patients admitted within 72 hours after onset and performed abdominal CT on admission. The image DL model was built by the convolutional neural networks. The combined model was developed by integrating CT images and clinical markers. The performance of the models was evaluated by using the area under the receiver operating characteristic curve. RESULTS The clinical, Image DL, and the combined DL models were developed in 783 AP patients and validated in 195 AP patients. The combined models possessed the predictive accuracy of 90.0%, 32.4%, and 74.2% for mild, moderately severe, and severe AP. The combined DL model outperformed clinical and image DL models with 0.820 (95% confidence interval, 0.759-0.871), the sensitivity of 84.76% and the specificity of 66.67% for predicting mild AP and the area under the receiver operating characteristic curve of 0.920 (95% confidence interval, 0.873-0.954), the sensitivity of 90.32%, and the specificity of 82.93% for predicting severe AP. CONCLUSIONS The DL technology allows nonenhanced CT images as a novel tool for predicting the severity of AP.
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Affiliation(s)
- Zhiyao Chen
- From the Pancreatitis Center, Center of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Wang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Huiling Zhang
- Infervision Medical Technology Co., Ltd, Beijing, China
| | - Hongkun Yin
- Infervision Medical Technology Co., Ltd, Beijing, China
| | - Cheng Hu
- From the Pancreatitis Center, Center of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre, West China Hospital, Sichuan University, Chengdu, China
| | - Zixing Huang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Qingyuan Tan
- From the Pancreatitis Center, Center of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre, West China Hospital, Sichuan University, Chengdu, China
| | | | - Lihui Deng
- From the Pancreatitis Center, Center of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre, West China Hospital, Sichuan University, Chengdu, China
| | - Qing Xia
- From the Pancreatitis Center, Center of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre, West China Hospital, Sichuan University, Chengdu, China
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Muacevic A, Adler JR, Tatar C, Idiz UO, Demircioğlu MK, Çiçek ME, Yildiz I. The Potential Role of Model for End-Stage Liver Disease (MELD)-Sodium Score in Predicting the Severity of Acute Pancreatitis. Cureus 2022; 14:e33198. [PMID: 36742275 PMCID: PMC9891313 DOI: 10.7759/cureus.33198] [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] [Accepted: 12/31/2022] [Indexed: 01/02/2023] Open
Abstract
Background and aim Acute pancreatitis is a common inflammation of the pancreas which can be severe and even potentially mortal. High rates of mortality showed the importance of immediate identification of patients at high risk and led the clinicians to refer to various scoring systems. Our aim was to investigate a clinical predictive model using the Model for End-Stage Liver Disease-Sodium (MELD-sodium) scoring system, adapting it to acute pancreatitis patients referring to the systemic inflammatory nature of the disease and potential multi-organ failures in severe form. Methods Our multicenter study was designed retrospectively. The medical records were reviewed for the period of two years. Demographics, biochemical results, MELD-sodium scores and mortality rates were analysed. Results MELD-sodium score was found to be statistically correlated with both mortality and the severity of pancreatitis (p<0.001) and significant difference between both mild and severe (p<0.001), moderate and severe groups (p<0.001). Mortality was found to be significantly higher in patients with MELD-Na score when the cut-off value was accepted as '≥11'. Conclusion We found that MELD-sodium score was significantly associated with both severity of disease and mortality rates and also significantly effective between both mild/severe and moderate/severe groups which may be a guide for future multi-center reviews with larger patient and control groups, which can define the potential role of this non-invasive and easy-to-use predictive model in acute pancreatitis patients.
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Yuan L, Ji M, Wang S, Wen X, Huang P, Shen L, Xu J. Machine learning model identifies aggressive acute pancreatitis within 48 h of admission: a large retrospective study. BMC Med Inform Decis Mak 2022; 22:312. [PMID: 36447180 PMCID: PMC9707001 DOI: 10.1186/s12911-022-02066-3] [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] [Accepted: 11/23/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Acute pancreatitis (AP) with critical illness is linked to increased morbidity and mortality. Current risk scores to identify high-risk AP patients have certain limitations. OBJECTIVE To develop and validate a machine learning tool within 48 h after admission for predicting which patients with AP will develop critical illness based on ubiquitously available clinical, laboratory, and radiologic variables. METHODS 5460 AP patients were enrolled. Clinical, laboratory, and imaging variables were collected within 48 h after hospital admission. Least Absolute Shrinkage Selection Operator with bootstrap method was employed to select the most informative variables. Five different machine learning models were constructed to predictive likelihood of critical illness, and the optimal model (APCU) was selected. External cohort was used to validate APCU. APCU and other risk scores were compared using multivariate analysis. Models were evaluated by area under the curve (AUC). The decision curve analysis was employed to evaluate the standardized net benefit. RESULTS Xgboost was constructed and selected as APCU, involving age, comorbid disease, mental status, pulmonary infiltrates, procalcitonin (PCT), neutrophil percentage (Neu%), ALT/AST, ratio of albumin and globulin, cholinesterase, Urea, Glu, AST and serum total cholesterol. The APCU performed excellently in discriminating AP risk in internal cohort (AUC = 0.95) and external cohort (AUC = 0.873). The APCU was significant for biliogenic AP (OR = 4.25 [2.08-8.72], P < 0.001), alcoholic AP (OR = 3.60 [1.67-7.72], P = 0.001), hyperlipidemic AP (OR = 2.63 [1.28-5.37], P = 0.008) and tumor AP (OR = 4.57 [2.14-9.72], P < 0.001). APCU yielded the highest clinical net benefit, comparatively. CONCLUSION Machine learning tool based on ubiquitously available clinical variables accurately predicts the development of AP, optimizing the management of AP.
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Affiliation(s)
- Lei Yuan
- grid.260478.f0000 0000 9249 2313School of Automation, Nanjing University of Information Science and Technology, Nanjing, China ,grid.412632.00000 0004 1758 2270Department of Information Center, Wuhan University Renmin Hospital, Wuhan, Hubei China ,grid.260478.f0000 0000 9249 2313Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, NanJing, China
| | - Mengyao Ji
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei China
| | - Shuo Wang
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei China
| | - Xinyu Wen
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei China
| | - Pingxiao Huang
- grid.33199.310000 0004 0368 7223Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei China
| | - Lei Shen
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei China
| | - Jun Xu
- grid.260478.f0000 0000 9249 2313School of Automation, Nanjing University of Information Science and Technology, Nanjing, China ,grid.260478.f0000 0000 9249 2313Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, NanJing, China
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Lee DW, Cho CM. Predicting Severity of Acute Pancreatitis. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58060787. [PMID: 35744050 PMCID: PMC9227091 DOI: 10.3390/medicina58060787] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 06/02/2022] [Accepted: 06/08/2022] [Indexed: 11/16/2022]
Abstract
Acute pancreatitis has a diverse etiology and natural history, and some patients have severe complications with a high risk of mortality. The prediction of the severity of acute pancreatitis should be achieved by a careful ongoing clinical assessment coupled with the use of a multiple-factor scoring system and imaging studies. Over the past 40 years, various scoring systems have been suggested to predict the severity of acute pancreatitis. However, there is no definite and ideal scoring system with a high sensitivity and specificity. The interest in new biological markers and predictive models for identifying severe acute pancreatitis testifies to the continued clinical importance of early severity prediction. Although contrast-enhanced computed tomography (CT) is considered the gold standard for diagnosing pancreatic necrosis, early scanning for the prediction of severity is limited because the full extent of pancreatic necrosis may not develop within the first 48 h of presentation. This article provides an overview of the available scoring systems and biochemical markers for predicting severe acute pancreatitis, with a focus on their characteristics and limitations.
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Kui B, Pintér J, Molontay R, Nagy M, Farkas N, Gede N, Vincze Á, Bajor J, Gódi S, Czimmer J, Szabó I, Illés A, Sarlós P, Hágendorn R, Pár G, Papp M, Vitális Z, Kovács G, Fehér E, Földi I, Izbéki F, Gajdán L, Fejes R, Németh BC, Török I, Farkas H, Mickevicius A, Sallinen V, Galeev S, Ramírez-Maldonado E, Párniczky A, Erőss B, Hegyi PJ, Márta K, Váncsa S, Sutton R, Szatmary P, Latawiec D, Halloran C, de-Madaria E, Pando E, Alberti P, Gómez-Jurado MJ, Tantau A, Szentesi A, Hegyi P. EASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis. Clin Transl Med 2022; 12:e842. [PMID: 35653504 PMCID: PMC9162438 DOI: 10.1002/ctm2.842] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/06/2022] [Accepted: 04/11/2022] [Indexed: 12/17/2022] Open
Abstract
Background Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed. Methods The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit‐learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross‐validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross‐validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP). Results The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy‐to‐use web application in the Streamlit Python‐based framework (http://easy‐app.org/). Conclusions The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model.
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Affiliation(s)
- Balázs Kui
- Department of Medicine, University of Szeged, Szeged, Hungary.,Centre for Translational Medicine, Department of Medicine, University of Szeged, Szeged, Hungary
| | - József Pintér
- Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Roland Molontay
- Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, Budapest, Hungary.,MTA-BME Stochastics Research Group, Budapest, Hungary
| | - Marcell Nagy
- Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Nelli Farkas
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Institute of Bioanalysis, Medical School, University of Pécs, Pécs, Hungary
| | - Noémi Gede
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary
| | - Áron Vincze
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Judit Bajor
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Szilárd Gódi
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - József Czimmer
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Imre Szabó
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Anita Illés
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Patrícia Sarlós
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Roland Hágendorn
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Gabriella Pár
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Mária Papp
- Department of Gastroenterology, Institute of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Zsuzsanna Vitális
- Department of Gastroenterology, Institute of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - György Kovács
- Department of Gastroenterology, Institute of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Eszter Fehér
- Department of Gastroenterology, Institute of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Ildikó Földi
- Department of Gastroenterology, Institute of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Ferenc Izbéki
- Szent György Teaching Hospital of County Fejér, Székesfehérvár, Hungary
| | - László Gajdán
- Szent György Teaching Hospital of County Fejér, Székesfehérvár, Hungary
| | - Roland Fejes
- Szent György Teaching Hospital of County Fejér, Székesfehérvár, Hungary
| | - Balázs Csaba Németh
- Department of Medicine, University of Szeged, Szeged, Hungary.,Centre for Translational Medicine, Department of Medicine, University of Szeged, Szeged, Hungary
| | - Imola Török
- County Emergency Clinical Hospital of Târgu Mures-Gastroenterology Clinic and University of Medicine, Pharmacy, Sciences and Technology 'George Emil Palade', Targu Mures, Romania
| | - Hunor Farkas
- County Emergency Clinical Hospital of Târgu Mures-Gastroenterology Clinic and University of Medicine, Pharmacy, Sciences and Technology 'George Emil Palade', Targu Mures, Romania
| | | | - Ville Sallinen
- Department of Transplantation and Liver Surgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Shamil Galeev
- Saint Luke Clinical Hospital, St. Petersburg, Russia
| | | | - Andrea Párniczky
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Heim Pál National Pediatric Institute, Budapest, Hungary
| | - Bálint Erőss
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary.,Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
| | - Péter Jenő Hegyi
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary
| | - Katalin Márta
- Division of Pancreatic Diseases, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary.,Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
| | - Szilárd Váncsa
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary.,Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
| | - Robert Sutton
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool and Liverpool University Hospitals NHS Foundation Trust, Liverpool, England, UK
| | - Peter Szatmary
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool and Liverpool University Hospitals NHS Foundation Trust, Liverpool, England, UK
| | - Diane Latawiec
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool and Liverpool University Hospitals NHS Foundation Trust, Liverpool, England, UK
| | - Chris Halloran
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool and Liverpool University Hospitals NHS Foundation Trust, Liverpool, England, UK
| | - Enrique de-Madaria
- Gastroenterology Department, Alicante University General Hospital, ISABIAL, Alicante, Spain
| | - Elizabeth Pando
- Department of Hepato-Pancreato-Biliary and Transplant Surgery, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Piero Alberti
- Department of Hepato-Pancreato-Biliary and Transplant Surgery, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Maria José Gómez-Jurado
- Department of Hepato-Pancreato-Biliary and Transplant Surgery, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Alina Tantau
- The 4th Medical Clinic, Iuliu Hatieganu' University of Medicine and Pharmacy, Cluj-Napoca, Romania.,Gastroenterology and Hepatology Medical Center, Cluj-Napoca, Romania
| | - Andrea Szentesi
- Centre for Translational Medicine, Department of Medicine, University of Szeged, Szeged, Hungary.,Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary
| | - Péter Hegyi
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary.,Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
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11
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Kiss S, Pintér J, Molontay R, Nagy M, Farkas N, Sipos Z, Fehérvári P, Pecze L, Földi M, Vincze Á, Takács T, Czakó L, Izbéki F, Halász A, Boros E, Hamvas J, Varga M, Mickevicius A, Faluhelyi N, Farkas O, Váncsa S, Nagy R, Bunduc S, Hegyi PJ, Márta K, Borka K, Doros A, Hosszúfalusi N, Zubek L, Erőss B, Molnár Z, Párniczky A, Hegyi P, Szentesi A. Early prediction of acute necrotizing pancreatitis by artificial intelligence: a prospective cohort-analysis of 2387 cases. Sci Rep 2022; 12:7827. [PMID: 35552440 PMCID: PMC9098474 DOI: 10.1038/s41598-022-11517-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 04/07/2022] [Indexed: 12/21/2022] Open
Abstract
Pancreatic necrosis is a consistent prognostic factor in acute pancreatitis (AP). However, the clinical scores currently in use are either too complicated or require data that are unavailable on admission or lack sufficient predictive value. We therefore aimed to develop a tool to aid in necrosis prediction. The XGBoost machine learning algorithm processed data from 2387 patients with AP. The confidence of the model was estimated by a bootstrapping method and interpreted via the 10th and the 90th percentiles of the prediction scores. Shapley Additive exPlanations (SHAP) values were calculated to quantify the contribution of each variable provided. Finally, the model was implemented as an online application using the Streamlit Python-based framework. The XGBoost classifier provided an AUC value of 0.757. Glucose, C-reactive protein, alkaline phosphatase, gender and total white blood cell count have the most impact on prediction based on the SHAP values. The relationship between the size of the training dataset and model performance shows that prediction performance can be improved. This study combines necrosis prediction and artificial intelligence. The predictive potential of this model is comparable to the current clinical scoring systems and has several advantages over them.
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Affiliation(s)
- Szabolcs Kiss
- Doctoral School of Clinical Medicine, Faculty of Medicine, University of Szeged, Szeged, 6720, Hungary.,Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.,Heim Pál National Pediatric Institute, Üllői út 86, Budapest, 1089, Hungary
| | - József Pintér
- Human and Social Data Science Lab, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, 1111, Hungary
| | - Roland Molontay
- Human and Social Data Science Lab, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, 1111, Hungary.,Stochastics Research Group, Hungarian Academy of Sciences, Budapest University of Technology and Economics, Egry József u. 1, Budapest, 1111, Hungary
| | - Marcell Nagy
- Human and Social Data Science Lab, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, 1111, Hungary
| | - Nelli Farkas
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.,Institute of Bioanalysis, Medical School, University of Pécs, Honvéd u. 1, Pécs, 7624, Hungary
| | - Zoltán Sipos
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary
| | - Péter Fehérvári
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.,Department of Biomathematics and Informatics, University of Veterinary Medicine, István u. 2, Budapest, 1078, Hungary
| | - László Pecze
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary
| | - Mária Földi
- Doctoral School of Clinical Medicine, Faculty of Medicine, University of Szeged, Szeged, 6720, Hungary.,Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.,Heim Pál National Pediatric Institute, Üllői út 86, Budapest, 1089, Hungary
| | - Áron Vincze
- Division of Gastroenterology, First Department of Medicine, Medical School, University of Pécs, Ifjúság út 13, Pécs, 7624, Hungary
| | - Tamás Takács
- Department of Medicine, University of Szeged, Kálvária sgt. 57, Szeged, 6725, Hungary
| | - László Czakó
- Department of Medicine, University of Szeged, Kálvária sgt. 57, Szeged, 6725, Hungary
| | - Ferenc Izbéki
- Department of Internal Medicine, Szent György Teaching Hospital of County Fejér, Seregélyesi út 3, Székesfehérvár, 8000, Hungary
| | - Adrienn Halász
- Doctoral School of Clinical Medicine, Faculty of Medicine, University of Szeged, Szeged, 6720, Hungary.,Department of Internal Medicine, Szent György Teaching Hospital of County Fejér, Seregélyesi út 3, Székesfehérvár, 8000, Hungary
| | - Eszter Boros
- Department of Internal Medicine, Szent György Teaching Hospital of County Fejér, Seregélyesi út 3, Székesfehérvár, 8000, Hungary
| | - József Hamvas
- Bajcsy-Zsilinszky Hospital, Maglódi út 89-91, Budapest, 1106, Hungary
| | - Márta Varga
- Department of Gastroenterology, BMKK Dr Rethy Pal Hospital, Gyulai út 18, Békéscsaba, 5600, Hungary
| | - Artautas Mickevicius
- Vilnius University Hospital Santaros Clinics, Clinics of Abdominal Surgery, Nephrourology and Gastroenterology, Faculty of Medicine, Vilnius University, Santariškių g. 2, 08410, Vilnius, Lithuania
| | - Nándor Faluhelyi
- Department of Medical Imaging, Medical School, University of Pécs, Ifjúság út 13, Pécs, 7624, Hungary
| | - Orsolya Farkas
- Department of Medical Imaging, Medical School, University of Pécs, Ifjúság út 13, Pécs, 7624, Hungary
| | - Szilárd Váncsa
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.,Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary
| | - Rita Nagy
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.,Heim Pál National Pediatric Institute, Üllői út 86, Budapest, 1089, Hungary.,Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary
| | - Stefania Bunduc
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Doctoral School, Carol Davila University of Medicine and Pharmacy, Bulevardul Eroii Sanitari 8, 050474, Bucharest, Romania
| | - Péter Jenő Hegyi
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Center, Semmelweis University, Baross u. 23, Budapest, 1082, Hungary
| | - Katalin Márta
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Center, Semmelweis University, Baross u. 23, Budapest, 1082, Hungary
| | - Katalin Borka
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,2nd Department of Pathology, Semmelweis University, Üllői út 93, Budapest, 1091, Hungary
| | - Attila Doros
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Department of Transplantation and Surgery, Semmelweis University, Baross u. 23, Budapest, 1082, Hungary
| | - Nóra Hosszúfalusi
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Department of Internal Medicine and Hematology, Semmelweis University, Szentkirályi u. 46, Budapest, 1088, Hungary
| | - László Zubek
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Department of Anaesthesiology and Intensive Therapy, Semmelweis University, Üllői út 78, Budapest, 1082, Hungary
| | - Bálint Erőss
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Center, Semmelweis University, Baross u. 23, Budapest, 1082, Hungary
| | - Zsolt Molnár
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Department of Anaesthesiology and Intensive Therapy, Semmelweis University, Üllői út 78, Budapest, 1082, Hungary.,Department of Anaesthesiology and Intensive Therapy, Poznan University of Medical Sciences, ul. św. Marii Magdaleny 14, 61861, Poznan, Wielkopolska, Poland
| | - Andrea Párniczky
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.,Heim Pál National Pediatric Institute, Üllői út 86, Budapest, 1089, Hungary
| | - Péter Hegyi
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.,Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Center, Semmelweis University, Baross u. 23, Budapest, 1082, Hungary
| | - Andrea Szentesi
- Doctoral School of Clinical Medicine, Faculty of Medicine, University of Szeged, Szeged, 6720, Hungary. .,Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.
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12
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Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1249692. [PMID: 35509861 PMCID: PMC9060999 DOI: 10.1155/2022/1249692] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/29/2022] [Indexed: 11/23/2022]
Abstract
Breast cancer is one of the most commonly diagnosed female disorders globally. Numerous studies have been conducted to predict survival markers, although the majority of these analyses were conducted using simple statistical techniques. In lieu of that, this research employed machine learning approaches to develop models for identifying and visualizing relevant prognostic indications of breast cancer survival rates. A comprehensive hospital-based breast cancer dataset was collected from the National Cancer Institute's SEER Program's November 2017 update, which offers population-based cancer statistics. The dataset included female patients diagnosed between 2006 and 2010 with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3). The dataset included nine predictor factors and one predictor variable that were linked to the patients' survival status (alive or dead). To identify important prognostic markers associated with breast cancer survival rates, prediction models were constructed using K-nearest neighbor (K-NN), decision tree (DT), gradient boosting (GB), random forest (RF), AdaBoost, logistic regression (LR), voting classifier, and support vector machine (SVM). All methods yielded close results in terms of model accuracy and calibration measures, with the lowest achieved from logistic regression (accuracy = 80.57 percent) and the greatest acquired from the random forest (accuracy = 94.64 percent). Notably, the multiple machine learning algorithms utilized in this research achieved high accuracy, suggesting that these approaches might be used as alternative prognostic tools in breast cancer survival studies, especially in the Asian area.
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13
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Predicting the Need for Therapeutic Intervention and Mortality in Acute Pancreatitis: A Two-Center International Study Using Machine Learning. J Pers Med 2022; 12:jpm12040616. [PMID: 35455733 PMCID: PMC9031087 DOI: 10.3390/jpm12040616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/05/2022] [Accepted: 04/06/2022] [Indexed: 11/29/2022] Open
Abstract
Background: Current approaches to predicting intervention needs and mortality have reached 65–85% accuracy, which falls below clinical decision-making requirements in patients with acute pancreatitis (AP). We aimed to accurately predict therapeutic intervention needs and mortality on admission, in AP patients, using machine learning (ML). Methods: Data were obtained from three databases of patients admitted with AP: one retrospective (Chengdu) and two prospective (Liverpool and Chengdu) databases. Intervention and mortality differences, as well as potential predictors, were investigated. Univariate analysis was conducted, followed by a random forest ML algorithm used in multivariate analysis, to identify predictors. The ML performance matrix was applied to evaluate the model’s performance. Results: Three datasets of 2846 patients included 25 potential clinical predictors in the univariate analysis. The top ten identified predictors were obtained by ML models, for predicting interventions and mortality, from the training dataset. The prediction of interventions includes death in non-intervention patients, validated with high accuracy (96%/98%), the area under the receiver-operating-characteristic curve (0.90/0.98), and positive likelihood ratios (22.3/69.8), respectively. The post-test probabilities in the test set were 55.4% and 71.6%, respectively, which were considerably superior to existing prognostic scores. The ML model, for predicting mortality in intervention patients, performed better or equally with prognostic scores. Conclusions: ML, using admission clinical predictors, can accurately predict therapeutic interventions and mortality in patients with AP.
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14
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Nam D, Chapiro J, Paradis V, Seraphin TP, Kather JN. Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction. JHEP REPORTS : INNOVATION IN HEPATOLOGY 2022; 4:100443. [PMID: 35243281 PMCID: PMC8867112 DOI: 10.1016/j.jhepr.2022.100443] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 12/26/2021] [Accepted: 01/11/2022] [Indexed: 12/18/2022]
Abstract
Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative and quantitative information from multiple data sources to make a diagnosis, prognosticate the disease course, and recommend a treatment. In the last 5 years, advances in artificial intelligence (AI), particularly in deep learning, have made it possible to extract clinically relevant information from complex and diverse clinical datasets. In particular, histopathology and radiology image data contain diagnostic, prognostic and predictive information which AI can extract. Ultimately, such AI systems could be implemented in clinical routine as decision support tools. However, in the context of hepatology, this requires further large-scale clinical validation and regulatory approval. Herein, we summarise the state of the art in AI in hepatology with a particular focus on histopathology and radiology data. We present a roadmap for the further development of novel biomarkers in hepatology and outline critical obstacles which need to be overcome.
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15
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Machine learning predictive models for acute pancreatitis: A systematic review. Int J Med Inform 2021; 157:104641. [PMID: 34785488 DOI: 10.1016/j.ijmedinf.2021.104641] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 11/03/2021] [Accepted: 11/05/2021] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Acute pancreatitis (AP) is a common clinical pancreatic disease. Patients with different severity levels have different clinical outcomes. With the advantages of algorithms, machine learning (ML) has gradually emerged in the field of disease prediction, assisting doctors in decision-making. METHODS A systematic review was conducted using the PubMed, Web of Science, Scopus, and Embase databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Publication time was limited from inception to 29 May 2021. Studies that have used ML to establish predictive tools for AP were eligible for inclusion. Quality assessment of the included studies was conducted in accordance with the IJMEDI checklist. RESULTS In this systematic review, 24 of 2,913 articles, with a total of 8,327 patients and 47 models, were included. The studies could be divided into five categories: 10 studies (42%) reported severity prediction; 10 studies (42%), complication prediction; 3 studies (13%), mortality prediction; 2 studies (8%), recurrence prediction; and 2 studies (8%), surgery timing prediction. ML showed great accuracy in several prediction tasks. However, most of the included studies were retrospective in nature, conducted at a single centre, based on database data, and lacked external validation. According to the IJMEDI checklist and our scoring criteria, two studies were considered to be of high quality. Most studies had an obvious bias in the quality of data preparation, validation, and deployment dimensions. CONCLUSION In the prediction tasks for AP, ML has shown great potential in assisting decision-making. However, the existing studies still have some deficiencies in the process of model construction. Future studies need to optimize the deficiencies and further evaluate the comparability of the ML systems and model performance, so as to consequently develop high-quality ML-based models that can be used in clinical practice.
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16
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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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Wu KH, Cheng FJ, Tai HL, Wang JC, Huang YT, Su CM, Chang YN. Predicting in-hospital mortality in adult non-traumatic emergency department patients: a retrospective comparison of the Modified Early Warning Score (MEWS) and machine learning approach. PeerJ 2021; 9:e11988. [PMID: 34513328 PMCID: PMC8395578 DOI: 10.7717/peerj.11988] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 07/26/2021] [Indexed: 12/14/2022] Open
Abstract
Background A feasible and accurate risk prediction systems for emergency department (ED) patients is urgently required. The Modified Early Warning Score (MEWS) is a wide-used tool to predict clinical outcomes in ED. Literatures showed that machine learning (ML) had better predictability in specific patient population than traditional scoring system. By analyzing a large multicenter dataset, we aim to develop a ML model to predict in-hospital morality of the adult non traumatic ED patients for different time stages, and comparing performance with other ML models and MEWS. Methods A retrospective observational cohort study was conducted in five Taiwan EDs including two tertiary medical centers and three regional hospitals. All consecutively adult (>17 years old) non-traumatic patients admit to ED during a 9-year period (January first, 2008 to December 31th, 2016) were included. Exclusion criteria including patients with (1) out-of-hospital cardiac arrest and (2) discharge against medical advice and transferred to other hospital (3) missing collect variables. The primary outcome was in-hospital mortality and were categorized into 6, 24, 72, 168 hours mortality. MEWS was calculated by systolic blood pressure, pulse rate, respiratory rate, body temperature, and level of consciousness. An ensemble supervised stacking ML model was developed and compared to sensitive and unsensitive Xgboost, Random Forest, and Adaboost. We conducted a performance test and examine both the area under the receiver operating characteristic (AUROC) and the area under the precision and recall curve (AUPRC) as the comparative measures. Result After excluding 182,001 visits (7.46%), study group was consisted of 24,37,326 ED visits. The dataset was split into 67% training data and 33% test data for ML model development. There was no statistically difference found in the characteristics between two groups. For the prediction of 6, 24, 72, 168 hours in-hospital mortality, the AUROC of MEW and ML mode was 0.897, 0.865, 0.841, 0.816 and 0.939, 0.928, 0.913, 0.902 respectively. The stacking ML model outperform other ML model as well. For the prediction of in-hospital mortality over 48-hours, AUPRC performance of MEWS drop below 0.1, while the AUPRC of ML mode was 0.317 in 6 hours and 0.2150 in 168 hours. For each time frame, ML model achieved statistically significant higher AUROC and AUPRC than MEWS (all P < 0.001). Both models showed decreasing prediction ability as time elapse, but there was a trend that the gap of AUROC values between two model increases gradually (P < 0.001). Three MEWS thresholds (score >3, >4, and >5) were determined as baselines for comparison, ML mode consistently showed improved or equally performance in sensitivity, PPV, NPV, but not in specific. Conclusion Stacking ML methods improve predicted in-hospital mortality than MEWS in adult non-traumatic ED patients, especially in the prediction of delayed mortality.
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Affiliation(s)
- Kuan-Han Wu
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan (R.O.C.)
| | - Fu-Jen Cheng
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan (R.O.C.)
| | - Hsiang-Ling Tai
- Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan (R.O.C.)
| | - Jui-Cheng Wang
- Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan (R.O.C.)
| | - Yii-Ting Huang
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan (R.O.C.)
| | - Chih-Min Su
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan (R.O.C.)
| | - Yun-Nan Chang
- Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan (R.O.C.)
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Abstract
Introduction: Acute pancreatitis (AP) is a common gastrointestinal disease with a wide spectrum of severity and morbidity. Developed in 1974, the Ranson score was the first scoring system to prognosticate AP. Over the past decades, while the Ranson score remains widely used, it was identified to have certain limitations, such as having low predictive power. It has also been criticized for its 48-hour requirement for computation of the final score, which has been argued to potentially delay management. With advancements in our understanding of AP, is the Ranson score still relevant as an effective prognostication system for AP?Areas covered: This review summarizes the available evidence comparing Ranson score with other conventional and novel scoring systems, in terms of prognostic accuracy, benefits, limitations and clinical applicability. It also evaluates the effectiveness of Ranson score with regard to the Revised Atlanta Classification.Expert opinion: The Ranson score consistently exhibits comparable prognostic accuracy to other newer scoring systems, and the 48-hour timeframe for computing the full Ranson score is an inherent strength, not a weakness. These aspects, coupled with relative ease of use, practicality and universality of the score, advocate for the continued relevance of the Ranson score in modern clinical practice.
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Affiliation(s)
- Yuki Ong
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Vishal G Shelat
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- FRCS (General Surgery), FEBS (HPB Surgery), Hepato-Pancreatico-BiliarySurgery, Department of Surgery, Tan Tock Seng Hospital, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
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Prediction of Multiple Organ Failure Complicated by Moderately Severe or Severe Acute Pancreatitis Based on Machine Learning: A Multicenter Cohort Study. Mediators Inflamm 2021; 2021:5525118. [PMID: 34054342 PMCID: PMC8112913 DOI: 10.1155/2021/5525118] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 03/23/2021] [Accepted: 03/31/2021] [Indexed: 02/06/2023] Open
Abstract
Background Multiple organ failure (MOF) may lead to an increased mortality rate of moderately severe (MSAP) or severe acute pancreatitis (SAP). This study is aimed to use machine learning to predict the risk of MOF in the course of disease. Methods Clinical and laboratory features with significant differences between patients with and without MOF were screened out by univariate analysis. Prediction models were developed for selected features through six machine learning methods. The models were internally validated with a five-fold cross-validation, and a series of optimal feature subsets were generated in corresponding models. A test set was used to evaluate the predictive performance of the six models. Results 305 (68%) of 455 patients with MSAP or SAP developed MOF. Eighteen features with significant differences between the group with MOF and without it in the training and validation set were used for modeling. Interleukin-6 levels, creatinine levels, and the kinetic time were the three most important features in the optimal feature subsets selected by K-fold cross-validation. The adaptive boosting algorithm (AdaBoost) showed the best predictive performance with the highest AUC value (0.826; 95% confidence interval: 0.740 to 0.888). The sensitivity of AdaBoost (80.49%) and specificity of logistic regression analysis (93.33%) were the best scores among the six models in the test set. Conclusions A predictive model of MOF complicated by MSAP or SAP was successfully developed based on machine learning. The predictive performance was evaluated by a test set, for which AdaBoost showed a satisfactory predictive performance. The study is registered with the China Clinical Trial Registry (Identifier: ChiCTR1800016079).
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Kothari DJ, Sheth SG. Opportunity Is Knocking: Brainstorming Neural Networks for Management of Acute Pancreatitis. Pancreas 2021; 50:e11-e13. [PMID: 33370040 DOI: 10.1097/mpa.0000000000001716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
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21
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Akshintala VS, Khashab MA. Artificial intelligence in pancreaticobiliary endoscopy. J Gastroenterol Hepatol 2021; 36:25-30. [PMID: 33448514 DOI: 10.1111/jgh.15343] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 11/10/2020] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) applications in health care have exponentially increased in recent years, and a few of these are related to pancreatobiliary disorders. AI-based methods were applied to extract information, in prognostication, to guide clinical treatment decisions and in pancreatobiliary endoscopy to characterize lesions. AI applications in endoscopy are expected to reduce inter-operator variability, improve the accuracy of diagnosis, and assist in therapeutic decision-making in real time. AI-based literature must however be interpreted with caution given the limited external validation. A multidisciplinary approach combining clinical and imaging or endoscopy data will better utilize AI-based technologies to further improve patient care.
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Affiliation(s)
- Venkata S Akshintala
- Division of Gastroenterology, Johns Hopkins Medical Institutions, Baltimore, Maryland, USA
| | - Mouen A Khashab
- Division of Gastroenterology, Johns Hopkins Medical Institutions, Baltimore, Maryland, USA
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22
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Gorris M, Hoogenboom SA, Wallace MB, van Hooft JE. Artificial intelligence for the management of pancreatic diseases. Dig Endosc 2021; 33:231-241. [PMID: 33065754 PMCID: PMC7898901 DOI: 10.1111/den.13875] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/29/2020] [Accepted: 10/11/2020] [Indexed: 12/16/2022]
Abstract
Novel artificial intelligence techniques are emerging in all fields of healthcare, including gastroenterology. The aim of this review is to give an overview of artificial intelligence applications in the management of pancreatic diseases. We performed a systematic literature search in PubMed and Medline up to May 2020 to identify relevant articles. Our results showed that the development of machine-learning based applications is rapidly evolving in the management of pancreatic diseases, guiding precision medicine in clinical, endoscopic and radiologic settings. Before implementation into clinical practice, further research should focus on the external validation of novel techniques, clarifying the accuracy and robustness of these models.
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Affiliation(s)
- Myrte Gorris
- Department of Gastroenterology and HepatologyAmsterdam Gastroenterology Endocrinology MetabolismAmsterdam University Medical CentersUniversity of AmsterdamAmsterdamThe Netherlands
| | - Sanne A. Hoogenboom
- Department of Gastroenterology and HepatologyAmsterdam Gastroenterology Endocrinology MetabolismAmsterdam University Medical CentersUniversity of AmsterdamAmsterdamThe Netherlands
| | - Michael B. Wallace
- Department of Gastroenterology and HepatologyMayo Clinic JacksonvilleJacksonvilleUSA
| | - Jeanin E. van Hooft
- Department of Gastroenterology and HepatologyAmsterdam Gastroenterology Endocrinology MetabolismAmsterdam University Medical CentersUniversity of AmsterdamAmsterdamThe Netherlands
- Department of Gastroenterology and HepatologyLeiden University Medical CenterLeidenThe Netherlands
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Kao LS, McCauley JS. Evidence-Based Management of Gallstone Pancreatitis. Adv Surg 2020; 54:173-189. [PMID: 32713429 DOI: 10.1016/j.yasu.2020.04.005] [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] [Indexed: 11/18/2022]
Affiliation(s)
- Lillian S Kao
- Division of Acute Care Surgery, Department of Surgery, McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin Street, MSB 4.264, Houston, TX 77030, USA.
| | - Jayne S McCauley
- Division of Acute Care Surgery, Department of Surgery, McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin Street, MSB 4.264, Houston, TX 77030, USA. https://twitter.com/JMcCauleyMD
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Le Berre C, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smaïl-Tabbone M, Danese S, Peyrin-Biroulet L. Application of Artificial Intelligence to Gastroenterology and Hepatology. Gastroenterology 2020; 158:76-94.e2. [PMID: 31593701 DOI: 10.1053/j.gastro.2019.08.058] [Citation(s) in RCA: 280] [Impact Index Per Article: 70.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 08/22/2019] [Accepted: 08/24/2019] [Indexed: 02/07/2023]
Abstract
Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.
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Affiliation(s)
- Catherine Le Berre
- Institut des Maladies de l'Appareil Digestif, Nantes University Hospital, France; Institut National de la Santé et de la Recherche Médicale U954 and Department of Gastroenterology, Nancy University Hospital, University of Lorraine, France
| | | | - Sabeur Aridhi
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Marie-Dominique Devignes
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Laure Fournier
- Université Paris-Descartes, Institut National de la Santé et de la Recherche Médicale, Unité Mixte De Recherché S970, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Malika Smaïl-Tabbone
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Silvio Danese
- Inflammatory Bowel Disease Center and Department of Biomedical Sciences, Humanitas Clinical and Research Center, Humanitas University, Milan, Italy
| | - Laurent Peyrin-Biroulet
- Institut National de la Santé et de la Recherche Médicale U954 and Department of Gastroenterology, Nancy University Hospital, University of Lorraine, France.
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Ganggayah MD, Taib NA, Har YC, Lio P, Dhillon SK. Predicting factors for survival of breast cancer patients using machine learning techniques. BMC Med Inform Decis Mak 2019; 19:48. [PMID: 30902088 PMCID: PMC6431077 DOI: 10.1186/s12911-019-0801-4] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 03/18/2019] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Breast cancer is one of the most common diseases in women worldwide. Many studies have been conducted to predict the survival indicators, however most of these analyses were predominantly performed using basic statistical methods. As an alternative, this study used machine learning techniques to build models for detecting and visualising significant prognostic indicators of breast cancer survival rate. METHODS A large hospital-based breast cancer dataset retrieved from the University Malaya Medical Centre, Kuala Lumpur, Malaysia (n = 8066) with diagnosis information between 1993 and 2016 was used in this study. The dataset contained 23 predictor variables and one dependent variable, which referred to the survival status of the patients (alive or dead). In determining the significant prognostic factors of breast cancer survival rate, prediction models were built using decision tree, random forest, neural networks, extreme boost, logistic regression, and support vector machine. Next, the dataset was clustered based on the receptor status of breast cancer patients identified via immunohistochemistry to perform advanced modelling using random forest. Subsequently, the important variables were ranked via variable selection methods in random forest. Finally, decision trees were built and validation was performed using survival analysis. RESULTS In terms of both model accuracy and calibration measure, all algorithms produced close outcomes, with the lowest obtained from decision tree (accuracy = 79.8%) and the highest from random forest (accuracy = 82.7%). The important variables identified in this study were cancer stage classification, tumour size, number of total axillary lymph nodes removed, number of positive lymph nodes, types of primary treatment, and methods of diagnosis. CONCLUSION Interestingly the various machine learning algorithms used in this study yielded close accuracy hence these methods could be used as alternative predictive tools in the breast cancer survival studies, particularly in the Asian region. The important prognostic factors influencing survival rate of breast cancer identified in this study, which were validated by survival curves, are useful and could be translated into decision support tools in the medical domain.
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Affiliation(s)
- Mogana Darshini Ganggayah
- Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Nur Aishah Taib
- Department of Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Yip Cheng Har
- Department of Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, England
| | - Sarinder Kaur Dhillon
- Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia.
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26
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Mandalia A, Wamsteker EJ, DiMagno MJ. Recent advances in understanding and managing acute pancreatitis. F1000Res 2018; 7. [PMID: 30026919 DOI: 10.12688/f1000research.14244.1] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/20/2018] [Indexed: 12/16/2022] Open
Abstract
This review highlights advances made in recent years in the diagnosis and management of acute pancreatitis (AP). We focus on epidemiological, clinical, and management aspects of AP. Additionally, we discuss the role of using risk stratification tools to guide clinical decision making. The majority of patients suffer from mild AP, and only a subset develop moderately severe AP, defined as a pancreatic local complication, or severe AP, defined as persistent organ failure. In mild AP, management typically involves diagnostic evaluation and supportive care resulting usually in a short hospital length of stay (LOS). In severe AP, a multidisciplinary approach is warranted to minimize morbidity and mortality over the course of a protracted hospital LOS. Based on evidence from guideline recommendations, we discuss five treatment interventions, including intravenous fluid resuscitation, feeding, prophylactic antibiotics, probiotics, and timing of endoscopic retrograde cholangiopancreatography (ERCP) in acute biliary pancreatitis. This review also highlights the importance of preventive interventions to reduce hospital readmission or prevent pancreatitis, including alcohol and smoking cessation, same-admission cholecystectomy for acute biliary pancreatitis, and chemoprevention and fluid administration for post-ERCP pancreatitis. Our review aims to consolidate guideline recommendations and high-quality studies published in recent years to guide the management of AP and highlight areas in need of research.
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Affiliation(s)
- Amar Mandalia
- Division of Gastroenterology and Hepatology, University of Michigan School of Medicine, Ann Arbor, MI, 48109, USA
| | - Erik-Jan Wamsteker
- Division of Gastroenterology and Hepatology, University of Michigan School of Medicine, Ann Arbor, MI, 48109, USA
| | - Matthew J DiMagno
- Division of Gastroenterology and Hepatology, University of Michigan School of Medicine, Ann Arbor, MI, 48109, USA
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27
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Abstract
This review highlights advances made in recent years in the diagnosis and management of acute pancreatitis (AP). We focus on epidemiological, clinical, and management aspects of AP. Additionally, we discuss the role of using risk stratification tools to guide clinical decision making. The majority of patients suffer from mild AP, and only a subset develop moderately severe AP, defined as a pancreatic local complication, or severe AP, defined as persistent organ failure. In mild AP, management typically involves diagnostic evaluation and supportive care resulting usually in a short hospital length of stay (LOS). In severe AP, a multidisciplinary approach is warranted to minimize morbidity and mortality over the course of a protracted hospital LOS. Based on evidence from guideline recommendations, we discuss five treatment interventions, including intravenous fluid resuscitation, feeding, prophylactic antibiotics, probiotics, and timing of endoscopic retrograde cholangiopancreatography (ERCP) in acute biliary pancreatitis. This review also highlights the importance of preventive interventions to reduce hospital readmission or prevent pancreatitis, including alcohol and smoking cessation, same-admission cholecystectomy for acute biliary pancreatitis, and chemoprevention and fluid administration for post-ERCP pancreatitis. Our review aims to consolidate guideline recommendations and high-quality studies published in recent years to guide the management of AP and highlight areas in need of research.
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Affiliation(s)
- Amar Mandalia
- Division of Gastroenterology and Hepatology, University of Michigan School of Medicine, Ann Arbor, MI, 48109, USA
| | - Erik-Jan Wamsteker
- Division of Gastroenterology and Hepatology, University of Michigan School of Medicine, Ann Arbor, MI, 48109, USA
| | - Matthew J DiMagno
- Division of Gastroenterology and Hepatology, University of Michigan School of Medicine, Ann Arbor, MI, 48109, USA
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Calcaterra SL, Scarbro S, Hull ML, Forber AD, Binswanger IA, Colborn KL. Prediction of Future Chronic Opioid Use Among Hospitalized Patients. J Gen Intern Med 2018; 33:898-905. [PMID: 29404943 PMCID: PMC5975151 DOI: 10.1007/s11606-018-4335-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 11/07/2017] [Accepted: 01/17/2018] [Indexed: 11/29/2022]
Abstract
BACKGROUND Opioids are commonly prescribed in the hospital; yet, little is known about which patients will progress to chronic opioid therapy (COT) following discharge. We defined COT as receipt of ≥ 90-day supply of opioids with < 30-day gap in supply over a 180-day period or receipt of ≥ 10 opioid prescriptions over 1 year. Predictive tools to identify hospitalized patients at risk for future chronic opioid use could have clinical utility to improve pain management strategies and patient education during hospitalization and discharge. OBJECTIVE The objective of this study was to identify a parsimonious statistical model for predicting future COT among hospitalized patients not on COT before hospitalization. DESIGN Retrospective analysis electronic health record (EHR) data from 2008 to 2014 using logistic regression. PATIENTS Hospitalized patients at an urban, safety net hospital. MAIN MEASUREMENTS Independent variables included medical and mental health diagnoses, substance and tobacco use disorder, chronic or acute pain, surgical intervention during hospitalization, past year receipt of opioid or non-opioid analgesics or benzodiazepines, opioid receipt at hospital discharge, milligrams of morphine equivalents prescribed per hospital day, and others. KEY RESULTS Model prediction performance was estimated using area under the receiver operator curve, accuracy, sensitivity, and specificity. A model with 13 covariates was chosen using stepwise logistic regression on a randomly down-sampled subset of the data. Sensitivity and specificity were optimized using the Youden's index. This model predicted correctly COT in 79% of the patients and no COT correctly in 78% of the patients. CONCLUSIONS Our model accessed EHR data to predict 79% of the future COT among hospitalized patients. Application of such a predictive model within the EHR could identify patients at high risk for future chronic opioid use to allow clinicians to provide early patient education about pain management strategies and, when able, to wean opioids prior to discharge while incorporating alternative therapies for pain into discharge planning.
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Affiliation(s)
- S L Calcaterra
- Hospital Medicine, Denver Health Medical Center, Denver, CO, USA. .,Division of General Internal Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, USA.
| | - S Scarbro
- University of Colorado Adult and Child Consortium for Health Outcomes Research and Delivery Science, Aurora, CO, USA.,Rocky Mountain Prevention Research Center, Colorado School of Public Health, University of Colorado Denver, Aurora, CO, USA
| | - M L Hull
- Hospital Medicine, Denver Health Medical Center, Denver, CO, USA
| | - A D Forber
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - I A Binswanger
- Division of General Internal Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, USA.,Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, USA
| | - K L Colborn
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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29
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Forecasting of rehabilitation treatment in sufferers from lateral displacement of patella using artificial intelligence. SPORT SCIENCES FOR HEALTH 2018. [DOI: 10.1007/s11332-017-0397-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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30
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Gupta P, Rettiganti M, Wilcox A, Vuong-Dac MA, Gossett JM, Imamura M, Chakraborty A. An Empirically Derived Pediatric Cardiac Inotrope Score Associated With Pediatric Heart Surgery. Semin Thorac Cardiovasc Surg 2018; 30:62-68. [PMID: 29360599 DOI: 10.1053/j.semtcvs.2018.01.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/16/2018] [Indexed: 11/11/2022]
Abstract
We aimed to empirically derive an inotrope score to predict real-time outcomes using the doses of inotropes after pediatric cardiac surgery. The outcomes evaluated included in-hospital mortality, prolonged hospital length of stay, and composite poor outcome (mortality or prolonged hospital length of stay). The study population included patients <18 years of age undergoing heart operations (with or without cardiopulmonary bypass) of varying complexity. To create this novel pediatric cardiac inotrope score (PCIS), we collected the data on the highest doses of 4 commonly used inotropes (epinephrine, norepinephrine, dopamine, and milrinone) in the first 24 hours after heart operation. We employed a hierarchical framework by representing discrete probability models with continuous latent variables that depended on the dosage of drugs for a particular patient. We used Bayesian conditional probit regression to model the effects of the inotropes on the mean of the latent variables. We then used Markov chain Monte Carlo simulations for simulating posterior samples to create a score function for each of the study outcomes. The training dataset utilized 1030 patients to make the scientific model. An online calculator for the tool can be accessed at https://soipredictiontool.shinyapps.io/InotropeScoreApp. The newly proposed empiric PCIS demonstrated a high degree of discrimination for predicting study outcomes in children undergoing heart operations. The newly proposed empiric PCIS provides a novel measure to predict real-time outcomes using the doses of inotropes among children undergoing heart operations of varying complexity.
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Affiliation(s)
- Punkaj Gupta
- Division of Pediatric Cardiology, Department of Pediatrics, University of Arkansas for Medical Sciences, College of Medicine, Arkansas Children's Hospital, Little Rock, Arkansas.
| | - Mallikarjuna Rettiganti
- Biostatistics Program, Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Andrew Wilcox
- Division of Pediatric Cardiology, Department of Pediatrics, University of Arkansas for Medical Sciences, College of Medicine, Arkansas Children's Hospital, Little Rock, Arkansas
| | - Mai-Anh Vuong-Dac
- Division of Pediatric Cardiology, Department of Pediatrics, University of Arkansas for Medical Sciences, College of Medicine, Arkansas Children's Hospital, Little Rock, Arkansas
| | - Jeffrey M Gossett
- Biostatistics Program, Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Michiaki Imamura
- Division of Pediatric Cardiac Surgery, Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Avishek Chakraborty
- Department of Mathematical Sciences, University of Arkansas, Fayetteville, Arkansas
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Otero TMN, Canales C, Yeh DD, Hou PC, Belcher DM, Quraishi SA. Elevated red cell distribution width at initiation of critical care is associated with mortality in surgical intensive care unit patients. J Crit Care 2016; 34:7-11. [PMID: 27288601 DOI: 10.1016/j.jcrc.2016.03.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2016] [Revised: 02/24/2016] [Accepted: 03/09/2016] [Indexed: 11/18/2022]
Abstract
PURPOSE Recent evidence suggests that red cell distribution width (RDW) is associated with mortality in mixed cohorts of critically ill patients. Our goal was to investigate whether elevated RDW at initiation of critical care in the intensive care unit (ICU) is associated with 90-day mortality in surgical patients. METHODS We performed a retrospective, single-center cohort study. Normal RDW was defined as 11.5%-14.5%. To investigate the association of admission RDW with 90-day mortality, we performed a logistic regression analysis, controlling for age, sex, race, body mass index, Nutrition Risk Screening 2002 score, Acute Physiology and Chronic Health Evaluation II score, hospital length of stay, as well as levels of creatinine, albumin, and mean corpuscular volume. RESULTS 500 patients comprised the analytic cohort; 47% patients had elevated RDW and overall 90-day mortality was 28%. Logistic regression analysis demonstrated that patients with elevated RDW had a greater than two-fold increased odds of mortality (OR 2.28: 95%CI 1.20-4.33) compared to patients with normal RDW. CONCLUSIONS Elevated RDW at initiation of care is associated with increased odds of 90-day mortality in surgical ICU patients. These data support the need for prospective studies to determine whether RDW can improve risk stratification in surgical ICU patients.
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Affiliation(s)
- Tiffany M N Otero
- Tufts University School of Medicine, 136 Harrison Ave, Boston, MA, 02111; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114.
| | - Cecilia Canales
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114; University of California, 252 Irvine Hall, Irvine, CA, 92697.
| | - D Dante Yeh
- Department of Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114; Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115.
| | - Peter C Hou
- Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115; Department of Emergency Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115.
| | - Donna M Belcher
- Department of Nutrition and Food Services, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114.
| | - Sadeq A Quraishi
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114; Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115.
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Pediatric Index of Cardiac Surgical Intensive Care Mortality Risk Score for Pediatric Cardiac Critical Care. Pediatr Crit Care Med 2015. [PMID: 26196254 DOI: 10.1097/pcc.0000000000000489] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Comparison of clinical outcomes is imperative in the evaluation of healthcare quality. Risk adjustment for children undergoing cardiac surgery poses unique challenges, due to its distinct nature. We developed a risk-adjustment tool specifically focused on critical care mortality for the pediatric cardiac surgical population: the Pediatric Index of Cardiac Surgical Intensive care Mortality score. DESIGN Retrospective analysis of prospectively collected pediatric critical care data. SETTING Pediatric critical care units in the United States. PATIENTS Pediatric cardiac intensive care surgical patients. INTERVENTIONS Prospectively collected data from consecutive patients admitted to ICUs were obtained from The Virtual PICU System (VPS, LLC, Los Angeles, CA), a national pediatric critical care database. Thirty-two candidate physiologic, demographic, and diagnostic variables were analyzed for inclusion in the development of the Pediatric Index of Cardiac Surgical Intensive care Mortality model. Multivariate logistic regression with stepwise selection was used to develop the model. MEASUREMENTS AND MAIN RESULTS A total of 16,574 cardiac surgical patients from the 55 PICUs across the United States were included in the analysis. Thirteen variables remained in the final model, including the validated Society of Thoracic Surgeons-European Association of Cardio-Thoracic Surgery Congenital Heart Surgery Mortality (STAT) score and admission time with respect to cardiac surgery, which identifies whether the patient underwent the index surgical procedure before or after admission to the ICU. Pediatric Index of Cardiac Surgical Intensive Care Mortality (PICSIM) performance was compared with the performance of Pediatric Risk of Mortality-3 and Pediatric Index of Mortality-2 risk of mortality scores, as well as the STAT score and STAT categories by calculating the area under the curve of the receiver operating characteristic from a validation dataset: PICSIM (area under the curve = 0.87) performed better than Pediatric Index of Mortality-2 (area under the curve = 0.81), Pediatric Risk of Mortality-3 (area under the curve = 0.82), STAT score (area under the curve = 0.77), STAT category (area under the curve = 0.75), and Risk Adjustment for Congenital Heart Surgery-1 (area under the curve = 0.74). CONCLUSIONS This newly developed mortality score, PICSIM, consisting of 13 risk variables encompassing physiology, cardiovascular condition, and time of admission to the ICU showed better discrimination than Pediatric Index of Mortality-2, Pediatric Risk of Mortality-3, and STAT score and category for mortality in a multisite cohort of pediatric cardiac surgical patients. The introduction of the variable "admission time with respect to cardiac surgery" allowed prediction of mortality when patients are admitted to the ICU either before or after the index surgical procedure.
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Evidence-Based Pediatric Outcome Predictors to Guide the Allocation of Critical Care Resources in a Mass Casualty Event. Pediatr Crit Care Med 2015; 16:e207-16. [PMID: 26121100 DOI: 10.1097/pcc.0000000000000481] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVE ICU resources may be overwhelmed by a mass casualty event, triggering a conversion to Crisis Standards of Care in which critical care support is diverted away from patients least likely to benefit, with the goal of improving population survival. We aimed to devise a Crisis Standards of Care triage allocation scheme specifically for children. DESIGN A triage scheme is proposed in which patients would be divided into those requiring mechanical ventilation at PICU presentation and those not, and then each group would be evaluated for probability of death and for predicted duration of resource consumption, specifically, duration of PICU length of stay and mechanical ventilation. Children will be excluded from PICU admission if their mortality or resource utilization is predicted to exceed predetermined levels ("high risk"), or if they have a low likelihood of requiring ICU support ("low risk"). Children entered into the Virtual PICU Performance Systems database were employed to develop prediction equations to assign children to the exclusion categories using logistic and linear regression. Machine Learning provided an alternative strategy to develop a triage scheme independent from this process. SETTING One hundred ten American PICUs SUBJECTS : One hundred fifty thousand records from the Virtual PICU database. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS The prediction equations for probability of death had an area under the receiver operating characteristic curve more than 0.87. The prediction equation for belonging to the low-risk category had lower discrimination. R for the prediction equations for PICU length of stay and days of mechanical ventilation ranged from 0.10 to 0.18. Machine learning recommended initially dividing children into those mechanically ventilated versus those not and had strong predictive power for mortality, thus independently verifying the triage sequence and broadly verifying the algorithm. CONCLUSION An evidence-based predictive tool for children is presented to guide resource allocation during Crisis Standards of Care, potentially improving population outcomes by selecting patients likely to benefit from short-duration ICU interventions.
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Chong SL, Liu N, Barbier S, Ong MEH. Predictive modeling in pediatric traumatic brain injury using machine learning. BMC Med Res Methodol 2015; 15:22. [PMID: 25886156 PMCID: PMC4374377 DOI: 10.1186/s12874-015-0015-0] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2015] [Accepted: 03/05/2015] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Pediatric traumatic brain injury (TBI) constitutes a significant burden and diagnostic challenge in the emergency department (ED). While large North American research networks have derived clinical prediction rules for the head injured child, these may not be generalizable to practices in countries with traditionally low rates of computed tomography (CT). We aim to study predictors for moderate to severe TBI in our ED population aged < 16 years. METHODS This was a retrospective case-control study based on data from a prospective surveillance head injury database. Cases were included if patients presented from 2006 to 2014, with moderate to severe TBI. Controls were age-matched head injured children from the registry, obtained in a 4 control: 1 case ratio. These children remained well on diagnosis and follow up. Demographics, history, and physical examination findings were analyzed and patients followed up for the clinical course and outcome measures of death and neurosurgical intervention. To predict moderate to severe TBI, we built a machine learning (ML) model and a multivariable logistic regression model and compared their performances by means of Receiver Operating Characteristic (ROC) analysis. RESULTS There were 39 cases and 156 age-matched controls. The following 4 predictors remained statistically significant after multivariable analysis: Involvement in road traffic accident, a history of loss of consciousness, vomiting and signs of base of skull fracture. The logistic regression model was created with these 4 variables while the ML model was built with 3 extra variables, namely the presence of seizure, confusion and clinical signs of skull fracture. At the optimal cutoff scores, the ML method improved upon the logistic regression method with respect to the area under the ROC curve (0.98 vs 0.93), sensitivity (94.9% vs 82.1%), specificity (97.4% vs 92.3%), PPV (90.2% vs 72.7%), and NPV (98.7% vs 95.4%). CONCLUSIONS In this study, we demonstrated the feasibility of using machine learning as a tool to predict moderate to severe TBI. If validated on a large scale, the ML method has the potential not only to guide discretionary use of CT, but also a more careful selection of head injured children who warrant closer monitoring in the hospital.
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Affiliation(s)
- Shu-Ling Chong
- Department of Emergency Medicine, KK Women's and Children's Hospital, Singapore, Singapore.
| | - Nan Liu
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore.
- Centre for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore, Singapore.
| | - Sylvaine Barbier
- Centre for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore, Singapore.
| | - Marcus Eng Hock Ong
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore.
- Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore, Singapore.
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Shen Y, Deng X, Xu N, Li Y, Miao B, Cui N. Relationship between the degree of severe acute pancreatitis and patient immunity. Surg Today 2014; 45:1009-17. [PMID: 25410475 DOI: 10.1007/s00595-014-1083-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2014] [Accepted: 08/12/2014] [Indexed: 12/21/2022]
Abstract
PURPOSE To investigate the relationship between the APACHE II score and the immunity of patients with severe acute pancreatitis. METHODS Clinical data were collected from 88 patients with acute pancreatitis, divided into four groups according to the severity of the disease. C-reactive protein (CRP), tumor necrosis factor-α, interleukin-6, interleukin-10, interleukin-4 and endotoxin (ET) in serum were measured on admission and then on days 3, 5, and 7. RESULTS The incidence of local complications and multiple organ dysfunction syndrome increased with a higher APACHE II score. The CRP levels were increased significantly on day 3 in all four groups, but remained high only in the extremely severe group. In the mild and moderate groups, the pro-/anti-inflammatory cytokines peaked on day 3 and then decreased slowly. In the severe and extremely severe groups, the proinflammatory cytokines levels peaked on days 3 and 5, and then decreased rapidly. The antiinflammatory cytokines increased progressively on days 3, 5 and 7. The ET levels peaked significantly and then decreased slowly in the mild, moderate and severe groups, but remained high in the extremely severe group. CONCLUSIONS An APACHE II score of 16 or higher is predictive of more local and systemic complications, excessive immune response, and premature immunosuppression.
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Affiliation(s)
- Yinfeng Shen
- Department of Surgery, Hubei Hospital of Chinese Medicine, Hubei University of Chinese Medicine, Wuhan, People's Republic of China
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Risk Scoring for Prediction of Acute Cardiac Complications from Imbalanced Clinical Data. IEEE J Biomed Health Inform 2014; 18:1894-902. [DOI: 10.1109/jbhi.2014.2303481] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Liu N, Koh ZX, Goh J, Lin Z, Haaland B, Ting BP, Ong MEH. Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection. BMC Med Inform Decis Mak 2014; 14:75. [PMID: 25150702 PMCID: PMC4150554 DOI: 10.1186/1472-6947-14-75] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Accepted: 08/18/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The key aim of triage in chest pain patients is to identify those with high risk of adverse cardiac events as they require intensive monitoring and early intervention. In this study, we aim to discover the most relevant variables for risk prediction of major adverse cardiac events (MACE) using clinical signs and heart rate variability. METHODS A total of 702 chest pain patients at the Emergency Department (ED) of a tertiary hospital in Singapore were included in this study. The recruited patients were at least 30 years of age and who presented to the ED with a primary complaint of non-traumatic chest pain. The primary outcome was a composite of MACE such as death and cardiac arrest within 72 h of arrival at the ED. For each patient, eight clinical signs such as blood pressure and temperature were measured, and a 5-min ECG was recorded to derive heart rate variability parameters. A random forest-based novel method was developed to select the most relevant variables. A geometric distance-based machine learning scoring system was then implemented to derive a risk score from 0 to 100. RESULTS Out of 702 patients, 29 (4.1%) met the primary outcome. We selected the 3 most relevant variables for predicting MACE, which were systolic blood pressure, the mean RR interval and the mean instantaneous heart rate. The scoring system with these 3 variables produced an area under the curve (AUC) of 0.812, and a cutoff score of 43 gave a sensitivity of 82.8% and specificity of 63.4%, while the scoring system with all the 23 variables had an AUC of 0.736, and a cutoff score of 49 gave a sensitivity of 72.4% and specificity of 63.0%. Conventional thrombolysis in myocardial infarction score and the modified early warning score achieved AUC values of 0.637 and 0.622, respectively. CONCLUSIONS It is observed that a few predictors outperformed the whole set of variables in predicting MACE within 72 h. We conclude that more predictors do not necessarily guarantee better prediction results. Furthermore, machine learning-based variable selection seems promising in discovering a few relevant and significant measures as predictors.
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Affiliation(s)
| | | | | | | | | | | | - Marcus Eng Hock Ong
- Department of Emergency Medicine, Singapore General Hospital, Outram Road, Singapore 169608, Singapore.
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Gomatos IP, Xiaodong X, Ghaneh P, Halloran C, Raraty M, Lane B, Sutton R, Neoptolemos JP. Prognostic markers in acute pancreatitis. Expert Rev Mol Diagn 2014; 14:333-46. [PMID: 24649820 DOI: 10.1586/14737159.2014.897608] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2024]
Abstract
Acute pancreatitis has a mortality rate of 5-10%. Early deaths are mainly due to multiorgan failure and late deaths are due to septic complications from pancreatic necrosis. The recently described 2012 Revised Atlanta Classification and the Determinant Classification both provide a more accurate description of edematous and necrotizing pancreatitis and local complications. The 2012 Revised Atlanta Classification uses the modified Marshall scoring system for assessing organ dysfunction. The Determinant Classification uses the sepsis-related organ failure assessment scoring system for organ dysfunction and, unlike the 2012 Revised Atlanta Classification, includes infected necrosis as a criterion of severity. These scoring systems are used to assess systemic complications requiring intensive therapy unit support and intra-abdominal complications requiring minimally invasive interventions. Numerous prognostic systems and markers have been evaluated but only the Glasgow system and serum CRP levels provide pragmatic prognostic accuracy early on. Novel concepts using genetic, transcriptomic and proteomic profiling and also functional imaging for the identification of specific disease patterns are now required.
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Affiliation(s)
- Ilias P Gomatos
- NIHR Pancreas Biomedical Research Unit, the Royal Liverpool University and Broadgreen Hospitals NHS Trust and the University of Liverpool, Liverpool L69 3GA, UK
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van den Heever M, Mittal A, Haydock M, Windsor J. The use of intelligent database systems in acute pancreatitis--a systematic review. Pancreatology 2013; 14:9-16. [PMID: 24555973 DOI: 10.1016/j.pan.2013.11.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2013] [Revised: 10/15/2013] [Accepted: 11/18/2013] [Indexed: 12/11/2022]
Abstract
INTRODUCTION Acute pancreatitis (AP) is a complex disease with multiple aetiological factors, wide ranging severity, and multiple challenges to effective triage and management. Databases, data mining and machine learning algorithms (MLAs), including artificial neural networks (ANNs), may assist by storing and interpreting data from multiple sources, potentially improving clinical decision-making. AIMS 1) Identify database technologies used to store AP data, 2) collate and categorise variables stored in AP databases, 3) identify the MLA technologies, including ANNs, used to analyse AP data, and 4) identify clinical and non-clinical benefits and obstacles in establishing a national or international AP database. METHODS Comprehensive systematic search of online reference databases. The predetermined inclusion criteria were all papers discussing 1) databases, 2) data mining or 3) MLAs, pertaining to AP, independently assessed by two reviewers with conflicts resolved by a third author. RESULTS Forty-three papers were included. Three data mining technologies and five ANN methodologies were reported in the literature. There were 187 collected variables identified. ANNs increase accuracy of severity prediction, one study showed ANNs had a sensitivity of 0.89 and specificity of 0.96 six hours after admission--compare APACHE II (cutoff score ≥8) with 0.80 and 0.85 respectively. Problems with databases were incomplete data, lack of clinical data, diagnostic reliability and missing clinical data. CONCLUSION This is the first systematic review examining the use of databases, MLAs and ANNs in the management of AP. The clinical benefits these technologies have over current systems and other advantages to adopting them are identified.
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Affiliation(s)
| | - Anubhav Mittal
- Department of Surgery, University of Auckland, Auckland, New Zealand
| | - Matthew Haydock
- Department of Surgery, University of Auckland, Auckland, New Zealand
| | - John Windsor
- Department of Surgery, University of Auckland, Auckland, New Zealand.
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Khan A, Doucette JA, Cohen R. Validation of an ontological medical decision support system for patient treatment using a repository of patient data. ACM T INTEL SYST TEC 2013. [DOI: 10.1145/2508037.2508049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
In this article, we begin by presenting OMeD, a medical decision support system, and argue for its value over purely probabilistic approaches that reason about patients for time-critical decision scenarios. We then progress to present Holmes, a Hybrid Ontological and Learning MEdical System which supports decision making about patient treatment. This system is introduced in order to cope with the case of missing data. We demonstrate its effectiveness by operating on an extensive set of real-world patient health data from the CDC, applied to the decision-making scenario of administering sleeping pills. In particular, we clarify how the combination of semantic, ontological representations, and probabilistic reasoning together enable the proposal of effective patient treatments. Our focus is thus on presenting an approach for interpreting medical data in the context of real-time decision making. This constitutes a comprehensive framework for the design of medical recommendation systems for potential use by medical professionals and patients both, with the end result being personalized patient treatment. We conclude with a discussion of the value of our particular approach for such diverse considerations as coping with misinformation provided by patients, performing effectively in time-critical environments where real-time decisions are necessary, and potential applications facilitating patient information gathering.
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Affiliation(s)
- Atif Khan
- University of Waterloo, Ontario, Canada
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Scoring of human acute pancreatitis: state of the art. Langenbecks Arch Surg 2013; 398:789-97. [PMID: 23680979 DOI: 10.1007/s00423-013-1087-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2013] [Accepted: 05/01/2013] [Indexed: 12/13/2022]
Abstract
BACKGROUND Acute pancreatitis remains as one of the most difficult and challenging digestive disorder to predict in terms of clinical course and outcome. Every case has an individual course and therefore acute pancreatitis remains challenging and fascinating. Due to this variability, many different scoring systems have evolved during the last decades. Every scoring system has advantages and disadvantages. Not every scoring system is capable of assessing the clinical time course of the disease, some are only suitable for the time of initial presentation. AIM This paper will give an overview on the development of different widely used scoring systems and their performance in assessing severity and prognosis of acute pancreatitis. CONCLUSION Severity assessment means objective quantification of overall severity of illness. Early and reliable stratification of severity is required to decide best treatment of the individual patient, preparation for possible evolving complications or for referral to specialist centers. No single scoring system is able to cover the entire range of problems associated with treatment and assessment of acute pancreatitis. In our clinical experience, we recommend hematocrit upon admission, daily sequential organ failure assessment score and procalcitonin, C-reactive protein on day 3 and CT severity index beyond the first week. These scoring tools together with close clinical follow-up of the patient ultimately lead to an optimized treatment of this challenging disease.
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Liu N, Lin Z, Cao J, Koh Z, Zhang T, Huang GB, Ser W, Ong MEH. An Intelligent Scoring System and Its Application to Cardiac Arrest Prediction. ACTA ACUST UNITED AC 2012; 16:1324-31. [DOI: 10.1109/titb.2012.2212448] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Ai X, Qian X, Pan W, Xu J, Hu W, Terai T, Sato N, Watanabe S. Ultrasound-guided percutaneous drainage may decrease the mortality of severe acute pancreatitis. J Gastroenterol 2010; 45:77-85. [PMID: 19787287 DOI: 10.1007/s00535-009-0129-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2009] [Accepted: 08/19/2009] [Indexed: 02/04/2023]
Abstract
OBJECTIVES To evaluate the efficacy and safety of ultrasound-guided percutaneous catheter drainage (PCD) treatment for severe acute pancreatitis compared to conservative and conventional surgical treatments. METHODS Eighty-one patients with severe acute pancreatitis (SAP) were admitted and divided into three groups: forty-nine cases in the conservative therapy group; nineteen cases in the surgery group; and thirteen cases in the PCD therapy group. Forty-five patients with a CT severity index (CTSI) < or = 8.0 received conservative treatment. One patient with CTSI = 7.0 underwent surgery. Thirty-five patients with a CTSI > 8.0 were randomly selected for surgery or PCD treatment. After randomization, six patients (four patients in the surgery group and two patients in the PCD group) were dropped from the study. The total number of patients included in the surgery and PCD groups was sixteen and thirteen, respectively. RESULTS Four patients (8.2%) in the conservative therapy group died, five patients (31.3%) in surgery group with a CTSI > 8.0 died, and all patients in the PCD group survived. The mortality rate was lower in the PCD group than in the surgery group (P = 0.048). The serum C-reactive protein (CRP) level recovered more quickly in patients in the PCD group compared to those in the surgery group (P < 0.001). CONCLUSIONS Patients with SAP and a CTSI < or = 8.0 could be treated with conservative therapy, while patients with a CTSI > 8.0 should be treated with surgery or PCD therapy if the life-threatening complications of extensive fluid collection or necrosis are a factor. However, PCD therapy used in a timely manner for drainage may decrease mortality in patients with SAP, decrease inflammatory mediator release, and avoid incidence of severe sepsis or acute respiratory distress syndrome (ARDS) and emergency surgery.
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Affiliation(s)
- Xinbo Ai
- Department of Gastroenterology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88# Jiefang Road, 310009 Hangzhou, China
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Prediction of severe sepsis using SVM model. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2010; 680:75-81. [PMID: 20865488 DOI: 10.1007/978-1-4419-5913-3_9] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Sepsis is an infectious condition that results in damage to organs. This paper proposes a severe sepsis model based on Support Vector Machine (SVM) for predicting whether a septic patient will become severe sepsis. We chose several clinical physiology of sepsis for identifying the features used for SVM. Based on the model, a medical decision support system is proposed for clinical diagnosis. The results show that the prognosis of a septic patient can be more precisely predicted than ever. We conduct several experiments, whose results demonstrate that the proposed model provides high accuracy and high sensitivity and can be used as a reminding system to provide in-time treatment in ICU.
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DiMagno MJ, Wamsteker EJ, DeBenedet AT. Advances in managing acute pancreatitis. F1000 MEDICINE REPORTS 2009; 1:59. [PMID: 20539749 PMCID: PMC2881482 DOI: 10.3410/m1-59] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
This review highlights advances in acute pancreatitis (AP) made in the past year. We focus on clinical aspects of AP - severe disease especially - and risk stratification tools to guide the clinical care of patients. Most patients with AP have mild disease that requires a diagnostic evaluation, self-limited supportive care, and a short hospital stay. In patients with potentially severe AP, it is important for clinicians to use available risk-stratifying tools to identify high-risk patients and initiate timely interventions such as aggressive fluid resuscitation, close monitoring, early initiation of enteral nutrition, and appropriate use of endoscopic retrograde cholangio-pancreatography. This approach decreases morbidity and possibly mortality and is supported by evidence drawn from recent clinical guidelines, historical literature, and the highest quality studies published in the last year.
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Affiliation(s)
- Matthew J DiMagno
- Department of Internal Medicine, University of Michigan School of Medicine1500 East Medical Center Drive, Ann Arbor, MI 48109USA
- Division of Gastroenterology and Hepatology, University of Michigan School of Medicine1500 East Medical Center Drive, Ann Arbor, MI 48109USA
| | - Erik-Jan Wamsteker
- Department of Internal Medicine, University of Michigan School of Medicine1500 East Medical Center Drive, Ann Arbor, MI 48109USA
- Division of Gastroenterology and Hepatology, University of Michigan School of Medicine1500 East Medical Center Drive, Ann Arbor, MI 48109USA
| | - Anthony T DeBenedet
- Department of Internal Medicine, University of Michigan School of Medicine1500 East Medical Center Drive, Ann Arbor, MI 48109USA
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Abstract
BACKGROUND Acute pancreatitis has a variable natural history and in a proportion of patients is associated with severe complications and a significant risk of death. The various tools available for risk assessment in acute pancreatitis are reviewed. METHODS Relevant medical literature from PubMed, Ovid, Embase, Web of Science and The Cochrane Library websites to May 2008 was reviewed. RESULTS AND CONCLUSION Over the past 30 years several scoring systems have been developed to predict the severity of acute pancreatitis in the first 48-72 h. Biochemical and immunological markers, imaging modalities and novel predictive models may help identify patients at high risk of complications or death. Recently, there has been a recognition of the importance of the systemic inflammatory response syndrome and organ dysfunction.
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Affiliation(s)
- R Mofidi
- Department of Clinical and Surgical Sciences Surgery, University of Edinburgh, Edinburgh, UK
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Silva A, Cortez P, Santos MF, Gomes L, Neves J. Rating organ failure via adverse events using data mining in the intensive care unit. Artif Intell Med 2008; 43:179-93. [PMID: 18486459 DOI: 10.1016/j.artmed.2008.03.010] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2007] [Revised: 03/28/2008] [Accepted: 03/31/2008] [Indexed: 10/22/2022]
Abstract
OBJECTIVE The main intensive care unit (ICU) goal is to avoid or reverse the organ failure process by adopting a timely intervention. Within this context, early identification of organ impairment is a key issue. The sequential organ failure assessment (SOFA) is an expert-driven score that is widely used in European ICUs to quantify organ disorder. This work proposes a complementary data-driven approach based on adverse events, defined from commonly monitored biometrics. The aim is to study the impact of these events when predicting the risk of ICU organ failure. MATERIALS AND METHODS A large database was considered, with a total of 25,215 daily records taken from 4425 patients and 42 European ICUs. The input variables include the case mix (i.e. age, diagnosis, admission type and admission from) and adverse events defined from four bedside physiologic variables (i.e. systolic blood pressure, heart rate, pulse oximeter oxygen saturation and urine output). The output target is the organ status (i.e. normal, dysfunction or failure) of six organ systems (respiratory, coagulation, hepatic, cardiovascular, neurological and renal), as measured by the SOFA score. Two data mining (DM) methods were compared: multinomial logistic regression (MLR) and artificial neural networks (ANNs). These methods were tested in the R statistical environment, using 20 runs of a 5-fold cross-validation scheme. The area under the receiver operator characteristic (ROC) curve and Brier score were used as the discrimination and calibration measures. RESULTS The best performance was obtained by the ANNs, outperforming the MLR in both discrimination and calibration criteria. The ANNs obtained an average (over all organs) area under the ROC curve of 64, 69 and 74% and Brier scores of 0.18, 0.16 and 0.09 for the dysfunction, normal and failure organ conditions, respectively. In particular, very good results were achieved when predicting renal failure (ROC curve area of 76% and Brier score of 0.06). CONCLUSION Adverse events, taken from bedside monitored data, are important intermediate outcomes, contributing to a timely recognition of organ dysfunction and failure during ICU length of stay. The obtained results show that it is possible to use DM methods to get knowledge from easy obtainable data, thus making room for the development of intelligent clinical alarm monitoring.
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Affiliation(s)
- Alvaro Silva
- Serviço de Cuidados Intensivos, Hospital Geral de Santo António, Porto, Portugal
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Schütte K, Malfertheiner P. Markers for predicting severity and progression of acute pancreatitis. Best Pract Res Clin Gastroenterol 2008; 22:75-90. [PMID: 18206814 DOI: 10.1016/j.bpg.2007.10.013] [Citation(s) in RCA: 61] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Several tools have been developed for severity stratification in acute pancreatitis. They include single biochemical markers, imaging methods, and complex scoring systems, all of which aim at an early detection of severe acute pancreatitis to optimise monitoring and treatment of patients as early as possible. Among single biochemical markers, C-reactive protein (CRP) remains the most useful. Despite its delayed increase, peaking not earlier than 72 h after the onset of symptoms, it is accurate and widely available. Many other markers have been evaluated for their usefulness, and for some of them very promising data could be shown. Among them interleukin 6 seems to be the most promising parameter for use in clinical routine. For the detection of pancreatic infection, procalcitonin is the most sensitive, and can be used as an indicator for the need for fine-needle aspiration of pancreatic necrosis. Regarding imaging, contrast-enhanced computed tomography is still the reference method for the detection of necrotising acute pancreatitis. Pancreatitis-specific scoring systems have been shown to be of value for the prediction of severity and progression of acute pancreatitis, but cannot be applied any earlier than 48 h after admission to hospital. The APACHE-II score has not been developed specifically for acute pancreatitis and is rather complex to assess, but has been proven to be an early and reliable tool. Indication, timing and consequences of the methods applied need to be carefully considered and incorporated into clinical assessments to avoid costs and harm to the patient.
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Affiliation(s)
- Kerstin Schütte
- Department of Gastroenterology, Hepatology and Infectious Diseases, Otto-von-Guericke University, Leipziger Str. 44, 39120 Magdeburg, Germany
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Zhu M, Zhang Z, Hirdes JP, Stolee P. Using machine learning algorithms to guide rehabilitation planning for home care clients. BMC Med Inform Decis Mak 2007; 7:41. [PMID: 18096079 PMCID: PMC2235834 DOI: 10.1186/1472-6947-7-41] [Citation(s) in RCA: 45] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2007] [Accepted: 12/20/2007] [Indexed: 11/24/2022] Open
Abstract
Background Targeting older clients for rehabilitation is a clinical challenge and a research priority. We investigate the potential of machine learning algorithms – Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) – to guide rehabilitation planning for home care clients. Methods This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP. Results The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP. Conclusion Machine learning algorithms achieved superior predictions than the current protocol. Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.
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Affiliation(s)
- Mu Zhu
- Department of Health Studies and Gerontology, University of Waterloo, Waterloo, ON, Canada.
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