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Qezelbash-Chamak J, Badamchizadeh S, Eshghi K, Asadi Y. A survey of machine learning in kidney disease diagnosis. MACHINE LEARNING WITH APPLICATIONS 2022. [DOI: 10.1016/j.mlwa.2022.100418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
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Grant L, Joo P, Nemnom MJ, Thiruganasambandamoorthy V. Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data. Intern Emerg Med 2022; 17:1145-1153. [PMID: 34734350 DOI: 10.1007/s11739-021-02873-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 10/13/2021] [Indexed: 12/23/2022]
Abstract
Artificial Intelligence and machine learning (ML) methods are promising for risk-stratification, but the added benefit over traditional statistical methods remains unclear. We compared predictive models developed using machine learning (ML) methods to the Canadian Syncope Risk Score (CSRS), a risk-tool developed with logistic regression for predicting serious adverse events (SAE) after emergency department (ED) disposition for syncope. We used the prospective multicenter cohort data collected for CSRS development at 11 Canadian EDs over an 8-year period to develop four ML models to predict 30-day SAE (death, arrhythmias, MI, structural heart disease, pulmonary embolism, hemorrhage) after ED disposition. The CSRS derivation and validation cohorts were used for training and testing, respectively, and the 43 variables used included demographics, medical history, vital signs, ECG findings, blood tests and the diagnostic impression of the emergency physician. Performance was assessed using the area under the receiver-operating-characteristics curve (AUC) and calibration curves. Of the 4030 patients in the training set and 3819 patients in the test set overall, 286 (3.6%) patients suffered 30-day SAE. The AUCs for model validation in test data were CSRS 0.902 (0.877-0.926), regularized regression 0.903 (0.877-0.928), gradient boosting 0.914 (0.894-0.934), deep neural network 0.906 (0.883-0.929), simplified gradient boosting 0.904 (0.881-0.927). The AUCs and calibration slopes for the ML models and CSRS were similar. Two ML models used fewer predictors than the CSRS but matched its performance. Overall, the ML models matched the CSRS in performance, with some models using fewer predictors.
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Affiliation(s)
- Lars Grant
- Department of Emergency Medicine, McGill University, Montreal, QC, Canada
- Lady Davis Research Institute, Montreal, QC, Canada
- Jewish General Hospital, Montreal, QC, Canada
| | - Pil Joo
- The Ottawa Hospital, Ottawa, ON, Canada
| | - Marie-Joe Nemnom
- Clinical Epidemiology Program, Emergency Medicine, Ottawa Hospital Research Institute, 1053 Carling Avenue, Ottawa, ON, K1Y 4E9, Canada
| | - Venkatesh Thiruganasambandamoorthy
- The Ottawa Hospital, Ottawa, ON, Canada.
- Clinical Epidemiology Program, Emergency Medicine, Ottawa Hospital Research Institute, 1053 Carling Avenue, Ottawa, ON, K1Y 4E9, Canada.
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada.
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Abstract
PURPOSE OF REVIEW Artificial intelligence in medicine has allowed for efficient processing of large datasets to perform cognitive tasks that facilitate clinical decision-making, and it is an emerging area of research. This review aims to highlight the most pertinent and recent research in artificial intelligence in endourology, where it has been used to optimize stone diagnosis, support decision-making regarding management, predict stone recurrence, and provide new tools for bioinformatics research within endourology. RECENT FINDINGS Artificial neural networks (ANN) and machine learning approaches have demonstrated high accuracy in predicting stone diagnoses, stone composition, and outcomes of spontaneous stone passage, shockwave lithotripsy (SWL), or percutaneous nephrolithotomy (PCNL); some of these models outperform more traditional predictive models and existing nomograms. In addition, these approaches have been used to predict stone recurrence, quality of life scores, and provide novel methods of mining the electronic medical record for research. SUMMARY Artificial intelligence can be used to enhance existing approaches to stone diagnosis, management, and prevention to provide a more individualized approach to endourologic care. Moreover, it may support an emerging area of bioinformatics research within endourology. However, despite high accuracy, many of the published algorithms lack external validity and require further study before they are more widely adopted.
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Hameed BMZ, Shah M, Naik N, Rai BP, Karimi H, Rice P, Kronenberg P, Somani B. The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades. Curr Urol Rep 2021; 22:53. [PMID: 34626246 PMCID: PMC8502128 DOI: 10.1007/s11934-021-01069-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/27/2021] [Indexed: 12/17/2022]
Abstract
Purpose of Review To highlight and review the application of artificial intelligence (AI) in kidney stone disease (KSD) for diagnostics, predicting procedural outcomes, stone passage, and recurrence rates. The systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. Recent Findings This review discusses the newer advancements in AI-driven management strategies, which holds great promise to provide an essential step for personalized patient care and improved decision making. AI has been used in all areas of KSD including diagnosis, for predicting treatment suitability and success, basic science, quality of life (QOL), and recurrence of stone disease. However, it is still a research-based tool and is not used universally in clinical practice. This could be due to a lack of data infrastructure needed to train the algorithms, wider applicability in all groups of patients, complexity of its use and cost involved with it. Summary The constantly evolving literature and future research should focus more on QOL and the cost of KSD treatment and develop evidence-based AI algorithms that can be used universally, to guide urologists in the management of stone disease.
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Affiliation(s)
- B M Zeeshan Hameed
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.,iTRUE: International Training and Research, Uro-Oncology and Endourology, Manipal, Karnataka, India
| | - Milap Shah
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.,iTRUE: International Training and Research, Uro-Oncology and Endourology, Manipal, Karnataka, India
| | - Nithesh Naik
- iTRUE: International Training and Research, Uro-Oncology and Endourology, Manipal, Karnataka, India. .,Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
| | - Bhavan Prasad Rai
- iTRUE: International Training and Research, Uro-Oncology and Endourology, Manipal, Karnataka, India.,Freeman Hospital, Newcastle upon Tyne, UK
| | - Hadis Karimi
- Department of Pharmacy, Manipal College of Pharmaceuticals, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Patrick Rice
- Department of Urology, University Hospital Southampton NHS Trust, Southampton, UK
| | | | - Bhaskar Somani
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.,iTRUE: International Training and Research, Uro-Oncology and Endourology, Manipal, Karnataka, India.,Department of Urology, University Hospital Southampton NHS Trust, Southampton, UK
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Gorincour G, Monneuse O, Ben Cheikh A, Avondo J, Chaillot PF, Journe C, Youssof E, Lecomte JC, Thomson V. Management of abdominal emergencies in adults using telemedicine and artificial intelligence. J Visc Surg 2021; 158:S26-S31. [PMID: 33714710 DOI: 10.1016/j.jviscsurg.2021.01.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The terms "telemedicine" and "artificial intelligence" (AI) are used today throughout all fields of medicine, with varying degrees of relevance. If telemedicine corresponds to practices currently being developed to supply a high quality response to medical provider shortages in the general provision of healthcare and to specific regional challenges. Through the possibilities of "scalability" and the "augmented physician" that it has helped to create, AI may also constitute a revolution in our practices. In the management of surgical emergencies, abdominal pain is one of the most frequent complaints of patients who present for emergency consultation, and up to 20% of patients prove to have an organic lesion that will require surgical management. In view of the very large number of patients concerned, the variety of clinical presentations, the potential seriousness of the etiological pathology that sometimes involves a life-threatening prognosis, healthcare workers responsible for these patients have logically been led to regularly rely on imaging examinations, which remain the critical key to subsequent management. Therefore, it is not surprising that articles have been published in recent years concerning the potential contributions of telemedicine (and teleradiology) to the diagnostic management of these patients, and also concerning the contribution of AI (albeit still in its infancy) to aid in diagnosis and treatment, including surgery. This review article presents the existing data and proposes a collaborative vision of an optimized patient pathway, giving medical meaning to the use of these tools.
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Affiliation(s)
- G Gorincour
- Imadis Téléradiologie, Lyon, Bordeaux, Marseille, France; Elsan, Clinique Bouchard, Marseille, France.
| | - O Monneuse
- Hospices Civils de Lyon, Université Claude Bernard Lyon 1, Service de Chirurgie d'Urgences et Chirurgie Générale, Lyon, France
| | - A Ben Cheikh
- Imadis Téléradiologie, Lyon, Bordeaux, Marseille, France; Ramsay, Clinique la Sauvegarde, Lyon, France
| | | | - P-F Chaillot
- Imadis Téléradiologie, Lyon, Bordeaux, Marseille, France; Groupe C2S, Clinique du Parc, Lyon, France
| | - C Journe
- Imadis Téléradiologie, Lyon, Bordeaux, Marseille, France; Groupe C2S, Clinique du Parc, Lyon, France
| | - E Youssof
- Imadis Téléradiologie, Lyon, Bordeaux, Marseille, France; Centre d'Imagerie Médicale Clinique du Parc/Pourcel/Bergson, Saint-Étienne, France
| | - J-C Lecomte
- Imadis Téléradiologie, Lyon, Bordeaux, Marseille, France; Centre hospitalier de Saintonge, Saintes, France; Centre Aquitain d'Imagerie Médicale, Bordeaux, France
| | - V Thomson
- Imadis Téléradiologie, Lyon, Bordeaux, Marseille, France; Ramsay, Clinique la Sauvegarde, Lyon, France
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Victory Prediction of Ladies Professional Golf Association Players: Influential Factors and Comparison of Prediction Models. J Hum Kinet 2021; 77:245-259. [PMID: 34168708 PMCID: PMC8008311 DOI: 10.2478/hukin-2021-0023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
This study aims to identify the most accurate prediction model for the possibility of victory from the annual average data of 25 seasons (1993–2017) of the Ladies Professional Golf Association (LPGA), and to determine the importance of the predicting factors. The four prediction models considered in this study were a decision tree, discriminant analysis, logistic regression, and artificial neural network analysis. The mean difference in the classification accuracy of these models was analyzed using SPSS 22.0 software (IBM Corp., Armonk, NY, USA) and the one-way analysis of variance (ANOVA). When the prediction was based on technical variables, the most important predicting variables for determining victory were greens in regulation (GIR) and putting average (PA) in all four prediction models. When the prediction was based on the output of the technical variables, the most important predicting variable for determining victory was birdies in all four prediction models. When the prediction was based on the season outcome, the most important predicting variables for determining victory were the top 10 finish% (T10) and official money. A significant mean difference in classification accuracy was observed while performing the one-way ANOVA, and the least significant difference post-hoc test showed that artificial neural network analysis exhibited higher accuracy than the other models, especially, for larger data sizes. From the results of this study, it can be inferred that the player who wants to win the LPGA should aim to increase GIR, reduce PA, and improve driving distance and accuracy through training to increase the birdies chance at each hole, which can lead to lower average strokes and increased possibility of being within T10.
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Jiang H, Mao H, Lu H, Lin P, Garry W, Lu H, Yang G, Rainer TH, Chen X. Machine learning-based models to support decision-making in emergency department triage for patients with suspected cardiovascular disease. Int J Med Inform 2020; 145:104326. [PMID: 33197878 DOI: 10.1016/j.ijmedinf.2020.104326] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 10/16/2020] [Accepted: 10/30/2020] [Indexed: 12/23/2022]
Abstract
BACKGROUND Accurate differentiation and prioritization in emergency department (ED) triage is important to identify high-risk patients and to efficiently allocate of finite resources. Using data available from patients with suspected cardiovascular disease presenting at ED triage, this study aimed to train and compare the performance of four common machine learning models to assist in decision making of triage levels. METHODS This cross-sectional study in the second Affiliated Hospital of Guangzhou Medical University was conducted from August 2015 to December 2018 inclusive. Demographic information, vital signs, blood glucose, and other available triage scores were collected. Four machine learning models - multinomial logistic regression (multinomial LR), eXtreme gradient boosting (XGBoost), random forest (RF) and gradient-boosted decision tree (GBDT) - were compared. For each model, 80 % of the data set was used for training and 20 % was used to test the models. The area under the receiver operating characteristic curve (AUC), accuracy and macro- F1 were calculated for each model. RESULTS In 17,661 patients presenting with suspected cardiovascular disease, the distribution of triage of level 1, level 2, level 3 and level 4 were 1.3 %, 18.6 %, 76.5 %, and 3.6 % respectively. The AUCs were: XGBoost (0.937), GBDT (0.921), RF (0.919) and multinomial LR (0.908). Based on feature importance generated by XGBoost, blood pressure, pulse rate, oxygen saturation, and age were the most significant variables for making decisions at triage. CONCLUSION Four machine learning models had good discriminative ability of triage. XGBoost demonstrated a slight advantage over other models. These models could be used for differential triage of low-risk patients and high-risk patients as a strategy to improve efficiency and allocation of finite resources.
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Affiliation(s)
- Huilin Jiang
- Emergency Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Haifeng Mao
- Emergency Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Huimin Lu
- Emergency Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Peiyi Lin
- Emergency Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Wei Garry
- Goodwill Hessian Health Technology Co., Ltd, Beijing, China.
| | - Huijing Lu
- Emergency Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Guangqian Yang
- Emergency Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Timothy H Rainer
- Accident and Emergency Medicine Academic Unit, Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China.
| | - Xiaohui Chen
- Emergency Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
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Shah M, Naik N, Somani BK, Hameed BMZ. Artificial intelligence (AI) in urology-Current use and future directions: An iTRUE study. Turk J Urol 2020; 46:S27-S39. [PMID: 32479253 PMCID: PMC7731952 DOI: 10.5152/tud.2020.20117] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 04/12/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Artificial intelligence (AI) is used in various urological conditions such as urolithiasis, pediatric urology, urogynecology, benign prostate hyperplasia (BPH), renal transplant, and uro-oncology. The various models of AI and its application in urology subspecialties are reviewed and discussed. MATERIAL AND METHODS Search strategy was adapted to identify and review the literature pertaining to the application of AI in urology using the keywords "urology," "artificial intelligence," "machine learning," "deep learning," "artificial neural networks," "computer vision," and "natural language processing" were included and categorized. Review articles, editorial comments, and non-urologic studies were excluded. RESULTS The article reviewed 47 articles that reported characteristics and implementation of AI in urological cancer. In all cases with benign conditions, artificial intelligence was used to predict outcomes of the surgical procedure. In urolithiasis, it was used to predict stone composition, whereas in pediatric urology and BPH, it was applied to predict the severity of condition. In cases with malignant conditions, it was applied to predict the treatment response, survival, prognosis, and recurrence on the basis of the genomic and biomarker studies. These results were also found to be statistically better than routine approaches. Application of radiomics in classification and nuclear grading of renal masses, cystoscopic diagnosis of bladder cancers, predicting Gleason score, and magnetic resonance imaging with computer-assisted diagnosis for prostate cancers are few applications of AI that have been studied extensively. CONCLUSIONS In the near future, we will see a shift in the clinical paradigm as AI applications will find their place in the guidelines and revolutionize the decision-making process.
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Affiliation(s)
- Milap Shah
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
| | - Nithesh Naik
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
- Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Bhaskar K. Somani
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
- Department of Urological Surgery, University Hospital Southampton NHS Trust, Southampton, UK
| | - BM Zeeshan Hameed
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
- i-TRUE: International Training and Research in Uro-oncology and Endourology, Manipal, Karnataka, India
- KMC Innovation Centre, Manipal Academy of Higher Education, Manipal, Karnataka, India
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Lin AX, Ho AFW, Cheong KH, Li Z, Cai W, Chee ML, Ng YY, Xiao X, Ong MEH. Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17114179. [PMID: 32545399 PMCID: PMC7312953 DOI: 10.3390/ijerph17114179] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 05/29/2020] [Accepted: 06/02/2020] [Indexed: 12/11/2022]
Abstract
The accurate prediction of ambulance demand provides great value to emergency service providers and people living within a city. It supports the rational and dynamic allocation of ambulances and hospital staffing, and ensures patients have timely access to such resources. However, this task has been challenging due to complex multi-nature dependencies and nonlinear dynamics within ambulance demand, such as spatial characteristics involving the region of the city at which the demand is estimated, short and long-term historical demands, as well as the demographics of a region. Machine learning techniques are thus useful to quantify these characteristics of ambulance demand. However, there is generally a lack of studies that use machine learning tools for a comprehensive modeling of the important demand dependencies to predict ambulance demands. In this paper, an original and novel approach that leverages machine learning tools and extraction of features based on the multi-nature insights of ambulance demands is proposed. We experimentally evaluate the performance of next-day demand prediction across several state-of-the-art machine learning techniques and ambulance demand prediction methods, using real-world ambulatory and demographical datasets obtained from Singapore. We also provide an analysis of this ambulatory dataset and demonstrate the accuracy in modeling dependencies of different natures using various machine learning techniques.
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Affiliation(s)
- Adrian Xi Lin
- School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore;
| | - Andrew Fu Wah Ho
- SingHealth Duke-NUS Emergency Medicine Academic Clinical Program, Duke-National University of Singapore Medical School, Singapore 169857, Singapore;
- SingHealth Emergency Medicine Residency Programme, Duke-National University of Singapore Medical School, Singapore 169608, Singapore
- Signature Research Programme in Cardiovascular & Metabolic Disorders, Duke-National University of Singapore Medical School, Singapore 169857, Singapore
| | - Kang Hao Cheong
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore
- SUTD-Massachusetts Institute of Technology International Design Centre, Singapore 487372, Singapore
- Correspondence:
| | - Zengxiang Li
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore 138632, Singapore;
| | - Wentong Cai
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore;
| | - Marcel Lucas Chee
- Faculty of Medicine, Nursing and Health Sciences, Monash University, VIC 3800, Australia;
| | - Yih Yng Ng
- Emergency Medicine, Tan Tock Seng Hospital, Singapore 308433, Singapore;
- Home Team Medical Services Division, Ministry of Home Affairs, Singapore 179369, Singapore
| | - Xiaokui Xiao
- School of Computing, National University of Singapore, Singapore 117417, Singapore;
| | - Marcus Eng Hock Ong
- Health Services & Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore;
- Department of Emergency Medicine, Singapore General Hospital, Singapore 169608, Singapore
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Prediction of Chronic Lower Back Pain Using the Hierarchical Neural Network: Comparison with Logistic Regression-A Pilot Study. MEDICINA (KAUNAS, LITHUANIA) 2019; 55:medicina55060259. [PMID: 31181815 PMCID: PMC6630563 DOI: 10.3390/medicina55060259] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Revised: 05/31/2019] [Accepted: 06/04/2019] [Indexed: 11/16/2022]
Abstract
Background: Many studies have reported on the causes of chronic lower back pain (CLBP). The aim of this study is to identify if the hierarchical neural network (HNN) is superior to a conventional statistical model for CLBP prediction. Linear models, which included multiple regression analysis, were executed for the analysis of the survey data because of the ease of interpretation. The problem with such linear models was that we could not fully consider the influence of interactions caused by a combination of nonlinear relationships and independent variables. Materials and Methods: The subjects in our study were 96 people (30 men aged 72.3 ± 5.6 years and 66 women aged 71.9 ± 5.4 years) who participated at a college health club from 20 July 2016 to 20 March 2017. The HNN and the logistic regression analysis (LR) were used for the prediction of CLBP and the accuracy of each analysis was compared and examined by using our previously reported data. The LR verified the fit using the Hosmer-Lemeshow test. The efficiencies of the two models were compared using receiver performance analysis (ROC), the root mean square error (RMSE), and the deviance (-2 log likelihood). Results: The area under the ROC curve, the RMSE, and the -2 log likelihood for the LR were 0.7163, 0.2581, and 105.065, respectively. The area under the ROC curve, the RMSE, and the log likelihood for the HNN were 0.7650, 0.2483, and 102.787, respectively (the correct answer rates were HNN = 73.3% and LR = 70.8%). Conclusions: On the basis of the ROC curve, the RMSE, and the -2 log likelihood, the performance of the HNN for the prediction probability of CLBP is equal to or higher than the LR. In the future, the HNN may be useful as an index to judge the risk of CLBP for individual patients.
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Manzanarez-Ozuna E, Flores DL, Gutiérrez-López E, Cervantes D, Juárez P. Model based on GA and DNN for prediction of mRNA-Smad7 expression regulated by miRNAs in breast cancer. Theor Biol Med Model 2018; 15:24. [PMID: 30594253 PMCID: PMC6310970 DOI: 10.1186/s12976-018-0095-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 11/30/2018] [Indexed: 01/06/2023] Open
Abstract
Background The Smad7 protein is negative regulator of the TGF-β signaling pathway, which is upregulated in patients with breast cancer. miRNAs regulate proteins expressions by arresting or degrading the mRNAs. The purpose of this work is to identify a miRNAs profile that regulates the expression of the mRNA coding for Smad7 in breast cancer using the data from patients with breast cancer obtained from the Cancer Genome Atlas Project. Methods We develop an automatic search method based on genetic algorithms to find a predictive model based on deep neural networks (DNN) which fit the set of biological data and apply the Olden algorithm to identify the relative importance of each miRNAs. Results A computational model of non-linear regression is shown, based on deep neural networks that predict the regulation given by the miRNA target transcripts mRNA coding for Smad7 protein in patients with breast cancer, with R2 of 0.99 is shown and MSE of 0.00001. In addition, the model is validated with the results in vivo and in vitro experiments reported in the literature. The set of miRNAs hsa-mir-146a, hsa-mir-93, hsa-mir-375, hsa-mir-205, hsa-mir-15a, hsa-mir-21, hsa-mir-20a, hsa-mir-503, hsa-mir-29c, hsa-mir-497, hsa-mir-107, hsa-mir-125a, hsa-mir-200c, hsa-mir-212, hsa-mir-429, hsa-mir-34a, hsa-let-7c, hsa-mir-92b, hsa-mir-33a, hsa-mir-15b, hsa-mir-224, hsa-mir-185 and hsa-mir-10b integrate a profile that critically regulates the expression of the mRNA coding for Smad7 in breast cancer. Conclusions We developed a genetic algorithm to select best features as DNN inputs (miRNAs). The genetic algorithm also builds the best DNN architecture by optimizing the parameters. Although the confirmation of the results by laboratory experiments has not occurred, the results allow suggesting that miRNAs profile could be used as biomarkers or targets in targeted therapies. Electronic supplementary material The online version of this article (10.1186/s12976-018-0095-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Edgar Manzanarez-Ozuna
- Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana 3917 Colonia Playitas, C.P. 22860, Ensenada, B.C., Mexico
| | - Dora-Luz Flores
- Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana 3917 Colonia Playitas, C.P. 22860, Ensenada, B.C., Mexico.
| | - Everardo Gutiérrez-López
- Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana 3917 Colonia Playitas, C.P. 22860, Ensenada, B.C., Mexico
| | - David Cervantes
- Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana 3917 Colonia Playitas, C.P. 22860, Ensenada, B.C., Mexico
| | - Patricia Juárez
- Centro de Investigación Científica y de Educación Superior de Ensenada, Carretera Ensenada-Tijuana No. 3918, Zona Playitas, C.P. 22860, Ensenada, B.C., Mexico
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Rendell K, Koprinska I, Kyme A, Ebker-White AA, Dinh MM. The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision-making. Emerg Med Australas 2018; 31:429-435. [PMID: 30469164 DOI: 10.1111/1742-6723.13199] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Revised: 09/05/2018] [Accepted: 10/05/2018] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To further develop and refine an Emergency Department (ED) in-patient admission prediction model using machine learning techniques. METHODS This was a retrospective analysis of state-wide ED data from New South Wales, Australia. Six classification algorithms (Bayesian networks, decision trees, logistic regression, naïve Bayes, neural networks and nearest neighbour) and five feature selection techniques (none, manual, correlation-based, information gain and wrapper) were examined. Presenting problem was categorised using broad (n = 20) and specific (n = 100) representations. Models were evaluated based on Area Under the Curve (AUC) and accuracy. The results were compared with the Sydney Triage to Admission Risk Tool (START), which uses logistic regression and six manually selected features. RESULTS Sixty admission prediction models were trained and validated using data from 1 721 294 patients. Under the broad representation of presenting problem, the nearest neighbour algorithm with manual feature selection had the best AUC of 0.8206 (95% CI ±0.0006), while the decision tree with no feature selection had the best accuracy of 74.83% (95% CI ±0.065). Under the specific representation, almost all models improved; the nearest neighbour with information gain feature selection had the best AUC of 0.8267 (95% CI ±0.0006), while the decision tree with wrapper or no feature selection had the best accuracy of 75.24% (95% CI ±0.064). Eleven of the machine learning models had slightly better AUC than the START model. CONCLUSION Machine learning methods demonstrate similar performance to logistic regression for ED disposition prediction models using basic triage information. This should be investigated further, especially for larger data sets with more complex clinical information.
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Affiliation(s)
- Kathryn Rendell
- School of Aerospace, Mechanical and Mechatronic Engineering, Faculty of Engineering and Information Technologies, The University of Sydney, Sydney, New South Wales, Australia
| | - Irena Koprinska
- School of Information Technologies, Faculty of Engineering and Information Technologies, The University of Sydney, Sydney, New South Wales, Australia
| | - Andre Kyme
- School of Aerospace, Mechanical and Mechatronic Engineering, Faculty of Engineering and Information Technologies, The University of Sydney, Sydney, New South Wales, Australia
| | - Anja A Ebker-White
- Emergency Department, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia.,School of Nursing, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Michael M Dinh
- Emergency Department, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia.,Discipline of Emergency Medicine, School of Medicine, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
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Dick D, Wu X, Hatoum GF, Zhao W. A fiducial-less tracking method for radiation therapy of liver tumors by diaphragm disparity analysis part 1: simulation study using machine learning through artificial neural network. ACTA ACUST UNITED AC 2018. [DOI: 10.1007/s13566-018-0358-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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14
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A novel method for predicting kidney stone type using ensemble learning. Artif Intell Med 2018; 84:117-126. [DOI: 10.1016/j.artmed.2017.12.001] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 07/30/2017] [Accepted: 12/02/2017] [Indexed: 11/19/2022]
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15
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Parsaeian M, Mohammad K, Mahmoudi M, Zeraati H. Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey. IRANIAN JOURNAL OF PUBLIC HEALTH 2012; 41:86-92. [PMID: 23113198 PMCID: PMC3469002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2012] [Accepted: 04/12/2012] [Indexed: 11/04/2022]
Abstract
BACKGROUND The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. METHODS Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. RESULTS The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. CONCLUSIONS Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant.
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Affiliation(s)
- M Parsaeian
- Dept. of Epidemiology and Biostatistics, Tehran University of Medical Sciences, Tehran, Iran
| | - K Mohammad
- Dept. of Epidemiology and Biostatistics, Tehran University of Medical Sciences, Tehran, Iran
| | - M Mahmoudi
- Dept. of Epidemiology and Biostatistics, Tehran University of Medical Sciences, Tehran, Iran
| | - H Zeraati
- Dept. of Epidemiology and Biostatistics, Tehran University of Medical Sciences, Tehran, Iran,Corresponding Author: E-mail: Fax: 00982188989127
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Viceconti M. A tentative taxonomy for predictive models in relation to their falsifiability. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2011; 369:4149-4161. [PMID: 21969670 DOI: 10.1098/rsta.2011.0227] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The growing importance of predictive models in biomedical research raises some concerns on the correct methodological approach to the falsification of such models, as they are developed in interdisciplinary research contexts between physics, biology and medicine. In each of these research sectors, there are established methods to develop cause-effect explanations for observed phenomena, which can be used to predict: epidemiological models, biochemical models, biophysical models, Bayesian models, neural networks, etc. Each research sector has accepted processes to verify how correct these models are (falsification). But interdisciplinary research imposes a broader perspective, which encompasses all possible models in a general methodological framework of falsification. The present paper proposes a general definition of 'scientific model' that makes it possible to categorize predictive models into broad categories. For each of these categories, generic falsification strategies are proposed, except for the so-called 'abductive' models. For this category, which includes artificial neural networks, Bayesian models and integrative models, the definition of a generic falsification strategy requires further investigation by researchers and philosophers of science.
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Affiliation(s)
- Marco Viceconti
- Laboratorio di Tecnologia Medica, Istituto Ortopedico Rizzoli, Via di Barbiano 1/10, 40136 Bologna, Italy.
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Devi SP, Rao KS, Sangeetha SS. Prediction of surgery times and scheduling of operation theaters in ophthalmology department. J Med Syst 2010; 36:415-30. [PMID: 20703709 DOI: 10.1007/s10916-010-9486-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2010] [Accepted: 03/30/2010] [Indexed: 10/19/2022]
Abstract
This paper presents the framework for forecasting the surgery time by taking into account the surgical environment in an ophthalmology department (experience of surgeon in years, experience of anesthetist in years, staff experience in years, type of anesthesia etc.). The estimation of surgery times is done using three techniques, such as the Adaptive Neuro Fuzzy Inference Systems (ANFIS), Artificial Neural Networks (ANN) and Multiple Linear Regression Analysis (MLRA) and the results of estimation accuracy were compared. Though the developed framework is general, it is illustrated for three ophthalmologic surgeries such as the cataract surgery, corneal transplant surgery and Oculoplastic surgery. The framework is validated by using data obtained from a local hospital. It is hypothesized that by accurately knowing the surgery times, one can schedule the operations optimally resulting in the efficient utilization of the operating rooms. This increase in the efficiency is demonstrated through computer simulations of the operating theater.
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Affiliation(s)
- S Prasanna Devi
- Department of Industrial Engineering, Anna University, Chennai, India.
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