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Ali H, Inayat F, Moond V, Chaudhry A, Afzal A, Anjum Z, Tahir H, Anwar MS, Dahiya DS, Afzal MS, Nawaz G, Sohail AH, Aziz M. Predicting short-term thromboembolic risk following Roux-en-Y gastric bypass using supervised machine learning. World J Gastrointest Surg 2024; 16:1097-1108. [PMID: 38690043 PMCID: PMC11056662 DOI: 10.4240/wjgs.v16.i4.1097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/07/2024] [Accepted: 03/05/2024] [Indexed: 04/22/2024] Open
Abstract
BACKGROUND Roux-en-Y gastric bypass (RYGB) is a widely recognized bariatric procedure that is particularly beneficial for patients with class III obesity. It aids in significant weight loss and improves obesity-related medical conditions. Despite its effectiveness, postoperative care still has challenges. Clinical evidence shows that venous thromboembolism (VTE) is a leading cause of 30-d morbidity and mortality after RYGB. Therefore, a clear unmet need exists for a tailored risk assessment tool for VTE in RYGB candidates. AIM To develop and internally validate a scoring system determining the individualized risk of 30-d VTE in patients undergoing RYGB. METHODS Using the 2016-2021 Metabolic and Bariatric Surgery Accreditation Quality Improvement Program, data from 6526 patients (body mass index ≥ 40 kg/m2) who underwent RYGB were analyzed. A backward elimination multivariate analysis identified predictors of VTE characterized by pulmonary embolism and/or deep venous thrombosis within 30 d of RYGB. The resultant risk scores were derived from the coefficients of statistically significant variables. The performance of the model was evaluated using receiver operating curves through 5-fold cross-validation. RESULTS Of the 26 initial variables, six predictors were identified. These included a history of chronic obstructive pulmonary disease with a regression coefficient (Coef) of 2.54 (P < 0.001), length of stay (Coef 0.08, P < 0.001), prior deep venous thrombosis (Coef 1.61, P < 0.001), hemoglobin A1c > 7% (Coef 1.19, P < 0.001), venous stasis history (Coef 1.43, P < 0.001), and preoperative anticoagulation use (Coef 1.24, P < 0.001). These variables were weighted according to their regression coefficients in an algorithm that was generated for the model predicting 30-d VTE risk post-RYGB. The risk model's area under the curve (AUC) was 0.79 [95% confidence interval (CI): 0.63-0.81], showing good discriminatory power, achieving a sensitivity of 0.60 and a specificity of 0.91. Without training, the same model performed satisfactorily in patients with laparoscopic sleeve gastrectomy with an AUC of 0.63 (95%CI: 0.62-0.64) and endoscopic sleeve gastroplasty with an AUC of 0.76 (95%CI: 0.75-0.78). CONCLUSION This simple risk model uses only six variables to assist clinicians in the preoperative risk stratification of RYGB patients, offering insights into factors that heighten the risk of VTE events.
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Affiliation(s)
- Hassam Ali
- Department of Gastroenterology, East Carolina University Brody School of Medicine, Greenville, NC 27834, United States
| | - Faisal Inayat
- Department of Internal Medicine, Allama Iqbal Medical College, Lahore, Punjab 54550, Pakistan
| | - Vishali Moond
- Department of Internal Medicine, Saint Peter's University Hospital and Robert Wood Johnson Medical School, New Brunswick, NJ 08901, United States
| | - Ahtshamullah Chaudhry
- Department of Internal Medicine, St. Dominic's Hospital, Jackson, MS 39216, United States
| | - Arslan Afzal
- Department of Gastroenterology, East Carolina University Brody School of Medicine, Greenville, NC 27834, United States
| | - Zauraiz Anjum
- Department of Internal Medicine, Rochester General Hospital, Rochester, NY 14621, United States
| | - Hamza Tahir
- Department of Internal Medicine, Jefferson Einstein Hospital, Philadelphia, PA 19141, United States
| | - Muhammad Sajeel Anwar
- Department of Internal Medicine, UHS Wilson Medical Center, Johnson, NY 13790, United States
| | - Dushyant Singh Dahiya
- Division of Gastroenterology, Hepatology and Motility, The University of Kansas School of Medicine, Kansas, KS 66160, United States
| | - Muhammad Sohaib Afzal
- Department of Internal Medicine, Louisiana State University Health, Shreveport, LA 71103, United States
| | - Gul Nawaz
- Department of Internal Medicine, Allama Iqbal Medical College, Lahore, Punjab 54550, Pakistan
| | - Amir H Sohail
- Department of Surgery, University of New Mexico School of Medicine, Albuquerque, NM 87106, United States
| | - Muhammad Aziz
- Department of Gastroenterology and Hepatology, The University of Toledo, Toledo, OH 43606, United States
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Sharifi-Kia A, Nahvijou A, Sheikhtaheri A. Machine learning-based mortality prediction models for smoker COVID-19 patients. BMC Med Inform Decis Mak 2023; 23:129. [PMID: 37479990 PMCID: PMC10360290 DOI: 10.1186/s12911-023-02237-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 07/13/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND The large number of SARS-Cov-2 cases during the COVID-19 global pandemic has burdened healthcare systems and created a shortage of resources and services. In recent years, mortality prediction models have shown a potential in alleviating this issue; however, these models are susceptible to biases in specific subpopulations with different risks of mortality, such as patients with prior history of smoking. The current study aims to develop a machine learning-based mortality prediction model for COVID-19 patients that have a history of smoking in the Iranian population. METHODS A retrospective study was conducted across six medical centers between 18 and 2020 and 15 March 2022, comprised of 678 CT scans and laboratory-confirmed COVID-19 patients that had a history of smoking. Multiple machine learning models were developed using 10-fold cross-validation. The target variable was in-hospital mortality and input features included patient demographics, levels of care, vital signs, medications, and comorbidities. Two sets of models were developed for at-admission and post-admission predictions. Subsequently, the top five prediction models were selected from at-admission models and post-admission models and their probabilities were calibrated. RESULTS The in-hospital mortality rate for smoker COVID-19 patients was 20.1%. For "at admission" models, the best-calibrated model was XGBoost which yielded an accuracy of 87.5% and F1 score of 86.2%. For the "post-admission" models, XGBoost also outperformed the rest with an accuracy of 90.5% and F1 score of 89.9%. Active smoking was among the most important features in patients' mortality prediction. CONCLUSION Our machine learning-based mortality prediction models have the potential to be adapted for improving the management of smoker COVID-19 patients and predicting patients' chance of survival.
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Affiliation(s)
- Ali Sharifi-Kia
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Azin Nahvijou
- Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
| | - Abbas Sheikhtaheri
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
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Enodien B, Taha-Mehlitz S, Saad B, Nasser M, Frey DM, Taha A. The development of machine learning in bariatric surgery. Front Surg 2023; 10:1102711. [PMID: 36911599 PMCID: PMC9998495 DOI: 10.3389/fsurg.2023.1102711] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 02/08/2023] [Indexed: 03/14/2023] Open
Abstract
Background Machine learning (ML), is an approach to data analysis that makes the process of analytical model building automatic. The significance of ML stems from its potential to evaluate big data and achieve quicker and more accurate outcomes. ML has recently witnessed increased adoption in the medical domain. Bariatric surgery, otherwise referred to as weight loss surgery, reflects the series of procedures performed on people demonstrating obesity. This systematic scoping review aims to explore the development of ML in bariatric surgery. Methods The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR). A comprehensive literature search was performed of several databases including PubMed, Cochrane, and IEEE, and search engines namely Google Scholar. Eligible studies included journals published from 2016 to the current date. The PRESS checklist was used to evaluate the consistency demonstrated during the process. Results A total of seventeen articles qualified for inclusion in the study. Out of the included studies, sixteen concentrated on the role of ML algorithms in prediction, while one addressed ML's diagnostic capacity. Most articles (n = 15) were journal publications, whereas the rest (n = 2) were papers from conference proceedings. Most included reports were from the United States (n = 6). Most studies addressed neural networks, with convolutional neural networks as the most prevalent. Also, the data type used in most articles (n = 13) was derived from hospital databases, with very few articles (n = 4) collecting original data via observation. Conclusions This study indicates that ML has numerous benefits in bariatric surgery, however its current application is limited. The evidence suggests that bariatric surgeons can benefit from ML algorithms since they will facilitate the prediction and evaluation of patient outcomes. Also, ML approaches to enhance work processes by making data categorization and analysis easier. However, further large multicenter studies are required to validate results internally and externally as well as explore and address limitations of ML application in bariatric surgery.
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Affiliation(s)
- Bassey Enodien
- Department of Surgery, GZO-Hospital, Wetzikon, Switzerland
| | - Stephanie Taha-Mehlitz
- Clarunis, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland
| | - Baraa Saad
- School of Medicine, St George's University of London, London, United Kingdom
| | - Maya Nasser
- School of Medicine, St George's University of London, London, United Kingdom
| | - Daniel M Frey
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Anas Taha
- Clarunis, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland.,Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
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Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:1644910. [PMID: 35756093 PMCID: PMC9226971 DOI: 10.1155/2022/1644910] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/17/2022] [Accepted: 05/22/2022] [Indexed: 12/13/2022]
Abstract
Prediction of the death among COVID-19 patients can help healthcare providers manage the patients better. We aimed to develop machine learning models to predict in-hospital death among these patients. We developed different models using different feature sets and datasets developed using the data balancing method. We used demographic and clinical data from a multicenter COVID-19 registry. We extracted 10,657 records for confirmed patients with PCR or CT scans, who were hospitalized at least for 24 hours at the end of March 2021. The death rate was 16.06%. Generally, models with 60 and 40 features performed better. Among the 240 models, the C5 models with 60 and 40 features performed well. The C5 model with 60 features outperformed the rest based on all evaluation metrics; however, in external validation, C5 with 32 features performed better. This model had high accuracy (91.18%), F-score (0.916), Area under the Curve (0.96), sensitivity (94.2%), and specificity (88%). The model suggested in this study uses simple and available data and can be applied to predict death among COVID-19 patients. Furthermore, we concluded that machine learning models may perform differently in different subpopulations in terms of gender and age groups.
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Bektaş M, Reiber BMM, Pereira JC, Burchell GL, van der Peet DL. Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives. Obes Surg 2022; 32:2772-2783. [PMID: 35713855 PMCID: PMC9273535 DOI: 10.1007/s11695-022-06146-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 06/03/2022] [Accepted: 06/03/2022] [Indexed: 11/25/2022]
Abstract
Background Machine learning (ML) has been successful in several fields of healthcare, however the use of ML within bariatric surgery seems to be limited. In this systematic review, an overview of ML applications within bariatric surgery is provided. Methods The databases PubMed, EMBASE, Cochrane, and Web of Science were searched for articles describing ML in bariatric surgery. The Cochrane risk of bias tool and the PROBAST tool were used to evaluate the methodological quality of included studies. Results The majority of applied ML algorithms predicted postoperative complications and weight loss with accuracies up to 98%. Conclusions In conclusion, ML algorithms have shown promising capabilities in the prediction of surgical outcomes after bariatric surgery. Nevertheless, the clinical introduction of ML is dependent upon the external validation of ML.
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Affiliation(s)
- Mustafa Bektaş
- Department of Gastrointestinal Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands.
| | - Beata M M Reiber
- Department of Gastrointestinal Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands
| | - Jaime Costa Pereira
- Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV, Amsterdam, the Netherlands
| | - George L Burchell
- Medical Library Department, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands
| | - Donald L van der Peet
- Department of Gastrointestinal Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands
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Mochão H, Gonçalves D, Alexandre L, Castro C, Valério D, Barahona P, Moreira-Gonçalves D, Costa PMD, Henriques R, Santos LL, Costa RS. IPOscore: An interactive web-based platform for postoperative surgical complications analysis and prediction in the oncology domain. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106754. [PMID: 35364482 DOI: 10.1016/j.cmpb.2022.106754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 03/07/2022] [Accepted: 03/11/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND The performance of traditional risk score systems to predict (post)-operative outcomes is limited. This weakness reduces confidence in its use to support clinical risk mitigation decisions. However, the rapid growth of health data in the last years offers principles to deal with some of these limitations. In this regard, the data allows the extraction of relevant information for both patients stratification and the rigorous identification of associated risk factors. The patients can then be targeted to specific preoperative optimization programs, thus contributing to the reduction of associated morbidity and mortality. OBJECTIVES The main goal of this work is, therefore, to provide a clinical decision support system (CDSS) based on data-driven modeling methods for surgical risk prediction specific for cancer patients in Portugal. RESULTS The result is IPOscore, a single web-based platform aimed at being an innovative approach to assist clinical decision-making in the surgical oncology domain. This system includes a database to store/manage the clinical data collected in a structured format, data visualization and analysis tools, and predictive machine learning models to predict postoperative outcomes in cancer patients. IPOscore also includes a pattern mining module based on biclustering to assess the discriminative power of a pattern towards postsurgical outcomes. Additionally, a mobile application is provided to this end. CONCLUSIONS The IPOscore platform is a valuable tool for surgical oncologists not only for clinical data management but also as a preventative and predictive healthcare system. Currently, this clinical support tool is being tested at the Portuguese Institute of Oncology (IPO-Porto), and can be accessed online at https://iposcore.org.
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Affiliation(s)
- Hugo Mochão
- IDMEC, Instituto Superior Tecnico, Universidade de Lisboa, Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal
| | - Daniel Gonçalves
- IDMEC, Instituto Superior Tecnico, Universidade de Lisboa, Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal; LAQV-REQUIMTE, DQ, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Campus da Caparica, Caparica, 2829-516, Portugal; INESC-ID, Lisboa, Portugal, R. Alves Redol 9, Lisboa, 1000-029, Portugal
| | - Leonardo Alexandre
- IDMEC, Instituto Superior Tecnico, Universidade de Lisboa, Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal; LAQV-REQUIMTE, DQ, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Campus da Caparica, Caparica, 2829-516, Portugal; INESC-ID, Lisboa, Portugal, R. Alves Redol 9, Lisboa, 1000-029, Portugal
| | - Carolina Castro
- Experimental Pathology and Therapeutics Group of Portuguese Institute of Oncology of Porto FG, EPE (IPO-Porto), Porto, Portugal
| | - Duarte Valério
- IDMEC, Instituto Superior Tecnico, Universidade de Lisboa, Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal
| | - Pedro Barahona
- NOVA LINCS, Dept. Informatica Faculdade de Ciencias e Tecnologia, Universidade NOVA de Lisboa, Campus da Caparica, Caparica, 2829-516, Portugal
| | - Daniel Moreira-Gonçalves
- Research Centre in Physical Activity, Health and Leisure, Faculdade de Desporto, Universidade do Porto, Porto, Portugal
| | - Paulo Matos da Costa
- General Surgery Service, Hospital Garcia de Orta, E.P.E., Portugal; Faculdade de Medicina da Universidade de Lisboa, Portugal
| | - Rui Henriques
- INESC-ID, Lisboa, Portugal, R. Alves Redol 9, Lisboa, 1000-029, Portugal; Instituto Superior Tecnico, University of Lisbon, Lisbon, Portugal
| | - Lúcio L Santos
- Surgical ICU of the Portuguese Institute of Oncology, Porto, Portugal; Surgical Oncology Department, IPO-Porto, Porto, Portugal; Experimental Pathology and Therapeutics Group of Portuguese Institute of Oncology of Porto FG, EPE (IPO-Porto), Porto, Portugal
| | - Rafael S Costa
- LAQV-REQUIMTE, DQ, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Campus da Caparica, Caparica, 2829-516, Portugal; IDMEC, Instituto Superior Tecnico, Universidade de Lisboa, Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal.
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Bellini V, Valente M, Turetti M, Del Rio P, Saturno F, Maffezzoni M, Bignami E. Current Applications of Artificial Intelligence in Bariatric Surgery. Obes Surg 2022; 32:2717-2733. [PMID: 35616768 PMCID: PMC9273529 DOI: 10.1007/s11695-022-06100-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 05/02/2022] [Accepted: 05/04/2022] [Indexed: 11/27/2022]
Abstract
The application of artificial intelligence technologies is growing in several fields of healthcare settings. The aim of this article is to review the current applications of artificial intelligence in bariatric surgery. We performed a review of the literature on Scopus, PubMed and Cochrane databases, screening all relevant studies published until September 2021, and finally including 36 articles. The use of machine learning algorithms in bariatric surgery is explored in all steps of the clinical pathway, from presurgical risk-assessment and intraoperative management to complications and outcomes prediction. The models showed remarkable results helping physicians in the decision-making process, thus improving the quality of care, and contributing to precision medicine. Several legal and ethical hurdles should be overcome before these methods can be used in common practice.
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Affiliation(s)
- Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Marina Valente
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Melania Turetti
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Paolo Del Rio
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Francesco Saturno
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Massimo Maffezzoni
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
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A Scoping Review of Artificial Intelligence and Machine Learning in Bariatric and Metabolic Surgery: Current Status and Future Perspectives. Obes Surg 2021; 31:4555-4563. [PMID: 34264433 DOI: 10.1007/s11695-021-05548-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/12/2021] [Accepted: 06/17/2021] [Indexed: 01/01/2023]
Abstract
Artificial intelligence (AI) is a revolution in data analysis with emerging roles in various specialties and with various applications. The objective of this scoping review was to retrieve current literature on the fields of AI that have been applied to metabolic bariatric surgery (MBS) and to investigate potential applications of AI as a decision-making tool of the bariatric surgeon. Initial search yielded 3260 studies published from January 2000 until March 2021. After screening, 49 unique articles were included in the final analysis. Studies were grouped into categories, and the frequency of appearing algorithms, dataset types, and metrics were documented. The heterogeneity of current studies showed that meticulous validation, strict reporting systems, and reliable benchmarking are mandatory for ensuring the clinical validity of future research.
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Sheikhtaheri A, Zarkesh MR, Moradi R, Kermani F. Prediction of neonatal deaths in NICUs: development and validation of machine learning models. BMC Med Inform Decis Mak 2021; 21:131. [PMID: 33874944 PMCID: PMC8056638 DOI: 10.1186/s12911-021-01497-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 04/13/2021] [Indexed: 11/23/2022] Open
Abstract
Background Prediction of neonatal deaths in NICUs is important for benchmarking and evaluating healthcare services in NICUs. Application of machine learning techniques can improve physicians’ ability to predict the neonatal deaths. The aim of this study was to present a neonatal death risk prediction model using machine learning techniques. Methods This study was conducted in Tehran, Iran in two phases. Initially, important risk factors in neonatal death were identified and then several machine learning models including Artificial Neural Network (ANN), decision tree (Random Forest (RF), C5.0 and CHART tree), Support Vector Machine (SVM), Bayesian Network and Ensemble models were developed. Finally, we prospectively applied these models to predict neonatal death in a NICU and followed up the neonates to compare the outcomes of these neonates with real outcomes. Results 17 factors were considered important in neonatal mortality prediction. The highest Area Under the Curve (AUC) was achieved for the SVM and Ensemble models with 0.98. The best precision and specificity were 0.98 and 0.94, respectively for the RF model. The highest accuracy, sensitivity and F-score were achieved for the SVM model with 0.94, 0.95 and 0.96, respectively. The best performance of models in prospective evaluation was for the ANN, C5.0 and CHAID tree models. Conclusion Using the developed machine learning models can help physicians predict the neonatal deaths in NICUs. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-021-01497-8.
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Affiliation(s)
- Abbas Sheikhtaheri
- Health Management and Economics Research Center, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Zarkesh
- Maternal, Fetal and Neonatal Research Center, Tehran University of Medical Sciences, Tehran, Iran.,Department of Neonatology, Yas Complex Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Raheleh Moradi
- Family Health Institute, Maternal, Fetal and Neonatal Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Farzaneh Kermani
- Health Information Technology Department, School of Allied Medical Sciences, Semnan University of Medical Sciences, Semnan, Iran.
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Srikanth N, Xie L, Morales-Marroquin E, Ofori A, de la Cruz-Muñoz N, Messiah SE. Intersection of smoking, e-cigarette use, obesity, and metabolic and bariatric surgery: a systematic review of the current state of evidence. J Addict Dis 2021; 39:331-346. [PMID: 33543677 DOI: 10.1080/10550887.2021.1874817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
BACKGROUND Millions of Americans qualify for metabolic and bariatric surgery (MBS) based on the proportion of the population with severe obesity. Simultaneously, the use of electronic nicotine/non-nicotine delivery systems (ENDS) has become epidemic. OBJECTIVE We conducted a timely systematic review to examine the impact of tobacco and ENDS use on post-operative health outcomes among MBS patients. METHODS PRISMA guidelines were used as the search framework. Keyword combinations of either "smoking," "tobacco," "e-cigarette," "vaping," or "ENDS" and "bariatric surgery," "RYGB," or "sleeve gastrectomy" were used as search terms in PUBMED, Science Direct, and EMBASE. Studies published in English between January 1990 and June 2020 were screened. RESULTS From the 3251 articles found, a total of 48 articles were included in the review. No articles described a relationship between ENDS and post-operative health outcomes in MBS patients. Seven studies reported smokers had greater post-MBS weight loss, six studies suggested no relationship between smoking and post-MBS weight loss, and one study reported smoking cessation pre-MBS was related to post-MBS weight gain. Perioperative use of tobacco is positively associated with several post-surgery complications and mortality in MBS patients. CONCLUSIONS Combustible tobacco use among MBS patients is significantly related to higher mortality risk and complication rates, but not weight loss. No data currently is available on the impact of ENDS use in these patients. With ENDS use at epidemic levels, it is imperative to determine any potential health effects among patients with severe obesity, and who complete MBS.
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Affiliation(s)
- Nimisha Srikanth
- School of Public Health, Dallas Regional Campus, University of Texas Health Science Center, Dallas, TX, USA.,Center for Pediatric Population Health, UTHealth School of Public Health and Children's Health System of Texas, Dallas, TX, USA.,School of Public Health, Texas A&M University, College Station, TX, USA
| | - Luyu Xie
- School of Public Health, Dallas Regional Campus, University of Texas Health Science Center, Dallas, TX, USA.,Center for Pediatric Population Health, UTHealth School of Public Health and Children's Health System of Texas, Dallas, TX, USA
| | - Elisa Morales-Marroquin
- School of Public Health, Dallas Regional Campus, University of Texas Health Science Center, Dallas, TX, USA.,Center for Pediatric Population Health, UTHealth School of Public Health and Children's Health System of Texas, Dallas, TX, USA
| | - Ashley Ofori
- School of Public Health, Dallas Regional Campus, University of Texas Health Science Center, Dallas, TX, USA.,Center for Pediatric Population Health, UTHealth School of Public Health and Children's Health System of Texas, Dallas, TX, USA
| | | | - Sarah E Messiah
- School of Public Health, Dallas Regional Campus, University of Texas Health Science Center, Dallas, TX, USA.,Center for Pediatric Population Health, UTHealth School of Public Health and Children's Health System of Texas, Dallas, TX, USA
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Clinical decision support system to predict chronic kidney disease: A fuzzy expert system approach. Int J Med Inform 2020; 138:104134. [PMID: 32298972 DOI: 10.1016/j.ijmedinf.2020.104134] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 03/01/2020] [Accepted: 03/24/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND OBJECTIVES Diagnosis and early intervention of chronic kidney disease are essential to prevent loss of kidney function and a large amount of financial resources. To this end, we developed a fuzzy logic-based expert system for diagnosis and prediction of chronic kidney disease and evaluate its robustness against noisy data. METHODS At first, we identified the diagnostic parameters and risk factors through a literature review and a survey of 18 nephrologists. Depending on the features selected, a set of fuzzy rules for the prediction of chronic kidney disease was determined by reviewing the literature, guidelines and consulting with nephrologists. Fuzzy expert system was developed using MATLAB software and Mamdani Inference System. Finally, the fuzzy expert system was evaluated using data extracted from 216 randomly selected medical records of patients with and without chronic kidney disease. We added noisy data to our dataset and compare the performance of the system on original and noisy datasets. RESULTS We selected 16 parameters for the prediction of chronic kidney disease. The accuracy, sensitivity, and specificity of the final system were 92.13 %, 95.37 %, and 88.88 %, respectively. The area under the curve was 0.92 and the Kappa coefficient was 0.84, indicating a very high correlation between the system diagnosis and the final diagnosis recorded in the medical records. The performance of the system on noisy input variables indicated that in the worse scenario, the accuracy, sensitivity, and specificity of the system decreased only by 4.43 %, 7.48 %, and 5.41 %, respectively. CONCLUSION Considering the desirable performance of the proposed expert system, the system can be useful in the prediction of chronic kidney disease.
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O'Neill AC, Yang D, Roy M, Sebastiampillai S, Hofer SOP, Xu W. Development and Evaluation of a Machine Learning Prediction Model for Flap Failure in Microvascular Breast Reconstruction. Ann Surg Oncol 2020; 27:3466-3475. [PMID: 32152777 DOI: 10.1245/s10434-020-08307-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND Despite high success rates, flap failure remains an inherent risk in microvascular breast reconstruction. Identifying patients who are at high risk for flap failure would enable us to recommend alternative reconstructive techniques. However, as flap failure is a rare event, identification of risk factors is statistically challenging. Machine learning is a form of artificial intelligence that automates analytical model building. It has been proposed that machine learning can build superior prediction models when the outcome of interest is rare. METHODS In this study we evaluate machine learning resampling and decision-tree classification models for the prediction of flap failure in a large retrospective cohort of microvascular breast reconstructions. RESULTS A total of 1012 patients were included in the study. Twelve patients (1.1%) experienced flap failure. The ROSE informed oversampling technique and decision-tree classification resulted in a strong prediction model (AUC 0.95) with high sensitivity and specificity. In the testing cohort, the model maintained acceptable specificity and predictive power (AUC 0.67), but sensitivity was reduced. The model identified four high-risk patient groups. Obesity, comorbidities and smoking were found to contribute to flap loss. The flap failure rate in high-risk patients was 7.8% compared with 0.44% in the low-risk cohort (p = 0.001). CONCLUSIONS This machine-learning risk prediction model suggests that flap failure may not be a random event. The algorithm indicates that flap failure is multifactorial and identifies a number of potential contributing factors that warrant further investigation.
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Affiliation(s)
- Anne C O'Neill
- Division of Plastic Surgery, Department of Surgery and Surgical Oncology, University Health Network, University of Toronto, Toronto, Canada. Anne.O'
| | - Donyang Yang
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Melissa Roy
- Division of Plastic Surgery, Department of Surgery and Surgical Oncology, University Health Network, University of Toronto, Toronto, Canada
| | - Stephanie Sebastiampillai
- Division of Plastic Surgery, Department of Surgery and Surgical Oncology, University Health Network, University of Toronto, Toronto, Canada
| | - Stefan O P Hofer
- Division of Plastic Surgery, Department of Surgery and Surgical Oncology, University Health Network, University of Toronto, Toronto, Canada
| | - Wei Xu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
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