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Wilson MB, Ali SA, Kovatch KJ, Smith JD, Hoff PT. Machine Learning Diagnosis of Peritonsillar Abscess. Otolaryngol Head Neck Surg 2019; 161:796-799. [DOI: 10.1177/0194599819868178] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Peritonsillar abscess (PTA) is a difficult diagnosis to make clinically, with clinical examination of even otolaryngologists showing poor sensitivity and specificity. Machine learning is a form of artificial intelligence that “learns” from data to make predictions. We developed a machine learning classifier to predict the diagnosis of PTA based on patient symptoms. We retrospectively collected clinical data and symptomatology from 916 patients who underwent attempted needle aspiration for PTA. Machine learning classifiers were trained on a subset of the data to predict the presence or absence of purulence on attempted aspiration. The performance of the model was evaluated on a holdout set. The accuracy of the top-performing algorithm, the artificial neural network, was 72.3%. Artificial neural networks can use patient symptoms to exceed human ability to predict PTA in patients with clinical suspicion for PTA. Similar models can assist medical decision making for clinicians who have suspicion of PTA.
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Affiliation(s)
- Michael B. Wilson
- Department of Otolaryngology–Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan, USA
| | - S. Ahmed Ali
- Department of Otolaryngology–Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan, USA
| | - Kevin J. Kovatch
- Department of Otolaryngology–Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan, USA
| | - Josh D. Smith
- Department of Otolaryngology–Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan, USA
| | - Paul T. Hoff
- Department of Otolaryngology–Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan, USA
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Follett L, Geletta S, Laugerman M. Quantifying risk associated with clinical trial termination: A text mining approach. Inf Process Manag 2019. [DOI: 10.1016/j.ipm.2018.11.009] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Zou W, Xia Y, Li H. Fault Diagnosis of Tennessee-Eastman Process Using Orthogonal Incremental Extreme Learning Machine Based on Driving Amount. IEEE TRANSACTIONS ON CYBERNETICS 2018; 48:3403-3410. [PMID: 29994325 DOI: 10.1109/tcyb.2018.2830338] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Fault diagnosis is important to the industrial process. This paper proposes an orthogonal incremental extreme learning machine based on driving amount (DAOI-ELM) for recognizing the faults of the Tennessee-Eastman process (TEP). The basic idea of DAOI-ELM is to incorporate the Gram-Schmidt orthogonalization method and driving amount into an incremental extreme learning machine (I-ELM). The case study for the 2-D nonlinear function and regression problems from the UCI dataset results show that DAOI-ELM can obtain better generalization ability and a more compact structure of ELM than I-ELM, convex I-ELM (CI-ELM), orthogonal I-ELM (OI-ELM), and bidirectional ELM. The experimental training and testing data are derived from the simulations of TEP. The performance of DAOI-ELM is evaluated and compared with that of the back propagation neural network, support vector machine, I-ELM, CI-ELM, and OI-ELM. The simulation results show that DAOI-ELM diagnoses the TEP faults better than other methods.
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Hu G, Li H, Xia Y, Luo L. A deep Boltzmann machine and multi-grained scanning forest ensemble collaborative method and its application to industrial fault diagnosis. COMPUT IND 2018. [DOI: 10.1016/j.compind.2018.04.002] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Gambhir S, Malik SK, Kumar Y. The Diagnosis of Dengue Disease. INTERNATIONAL JOURNAL OF HEALTHCARE INFORMATION SYSTEMS AND INFORMATICS 2018. [DOI: 10.4018/ijhisi.2018070101] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This article describes how Dengue fever is a fatal and hazardous disease resulting from the bite of several species of the female mosquito (principally, Aedesaegypti). Symptoms of the dengue fever mimic those of a number of other infectious and/or mosquito-borne tropical diseases such as Viral flu, Chikungunya, and Zika fever. Yet, with dengue fever, human life can be more at risk due to severe depletion of blood platelets. Thus, early detection of dengue disease can ensure saving lives; furthermore, it can help in making a preventive move before the disease progresses to epidemic proportion. Hence, the target of this article is to propose a model for an early detection and precise diagnosis of dengue disease. In this article, three prevalent machine learning methodologies, including, Artificial Neural Network (ANN), Decision Tree (DT) and Naive Bayes (NB) are evaluated for designing a diagnostic model. The performance of these models is assessed utilizing available dengue datasets. Results comparing and contrasting performance of diagnostic models utilizing accuracy, sensitivity, specificity and error rate parameters showed that ANN-based diagnostic model appears to yield better performance measures over both the DT and NB models.
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Affiliation(s)
| | | | - Yugal Kumar
- Jaypee University of Information Technology, Solan, India
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Abstract
In our previous study, our input data set consisted of 78 rats, the blood loss in percent as a dependent variable, and 11 independent variables (heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, respiration rate, temperature, perfusion index, lactate concentration, shock index, and new index (lactate concentration/perfusion)). The machine learning methods for multicategory classification were applied to a rat model in acute hemorrhage to predict the four Advanced Trauma Life Support (ATLS) hypovolemic shock classes for triage in our previous study. However, multicategory classification is much more difficult and complicated than binary classification. We introduce a simple approach for classifying ATLS hypovolaemic shock class by predicting blood loss in percent using support vector regression and multivariate linear regression (MLR). We also compared the performance of the classification models using absolute and relative vital signs. The accuracies of support vector regression and MLR models with relative values by predicting blood loss in percent were 88.5% and 84.6%, respectively. These were better than the best accuracy of 80.8% of the direct multicategory classification using the support vector machine one-versus-one model in our previous study for the same validation data set. Moreover, the simple MLR models with both absolute and relative values could provide possibility of the future clinical decision support system for ATLS classification. The perfusion index and new index were more appropriate with relative changes than absolute values.
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Berlyand Y, Raja AS, Dorner SC, Prabhakar AM, Sonis JD, Gottumukkala RV, Succi MD, Yun BJ. How artificial intelligence could transform emergency department operations. Am J Emerg Med 2018; 36:1515-1517. [PMID: 29321109 DOI: 10.1016/j.ajem.2018.01.017] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Accepted: 01/03/2018] [Indexed: 12/20/2022] Open
Affiliation(s)
- Yosef Berlyand
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States
| | - Ali S Raja
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States; Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
| | - Stephen C Dorner
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States; Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Harvard Affiliated Emergency Medicine Residency Program, 5 Emerson Place, Suite 101, Boston, MA 02114, United States
| | - Anand M Prabhakar
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States; Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
| | - Jonathan D Sonis
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States; Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
| | - Ravi V Gottumukkala
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States; Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
| | - Marc David Succi
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States; Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Medically Engineered Solutions in Healthcare (MESH) Incubator, Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
| | - Brian J Yun
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States; Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States.
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Zhang J, Friberg IM, Kift-Morgan A, Parekh G, Morgan MP, Liuzzi AR, Lin CY, Donovan KL, Colmont CS, Morgan PH, Davis P, Weeks I, Fraser DJ, Topley N, Eberl M. Machine-learning algorithms define pathogen-specific local immune fingerprints in peritoneal dialysis patients with bacterial infections. Kidney Int 2017; 92:179-191. [PMID: 28318629 PMCID: PMC5484022 DOI: 10.1016/j.kint.2017.01.017] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 01/04/2017] [Accepted: 01/12/2017] [Indexed: 12/01/2022]
Abstract
The immune system has evolved to sense invading pathogens, control infection, and restore tissue integrity. Despite symptomatic variability in patients, unequivocal evidence that an individual's immune system distinguishes between different organisms and mounts an appropriate response is lacking. We here used a systematic approach to characterize responses to microbiologically well-defined infection in a total of 83 peritoneal dialysis patients on the day of presentation with acute peritonitis. A broad range of cellular and soluble parameters was determined in peritoneal effluents, covering the majority of local immune cells, inflammatory and regulatory cytokines and chemokines as well as tissue damage–related factors. Our analyses, utilizing machine-learning algorithms, demonstrate that different groups of bacteria induce qualitatively distinct local immune fingerprints, with specific biomarker signatures associated with Gram-negative and Gram-positive organisms, and with culture-negative episodes of unclear etiology. Even more, within the Gram-positive group, unique immune biomarker combinations identified streptococcal and non-streptococcal species including coagulase-negative Staphylococcus spp. These findings have diagnostic and prognostic implications by informing patient management and treatment choice at the point of care. Thus, our data establish the power of non-linear mathematical models to analyze complex biomedical datasets and highlight key pathways involved in pathogen-specific immune responses.
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Affiliation(s)
- Jingjing Zhang
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Ida M Friberg
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Ann Kift-Morgan
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Gita Parekh
- Mologic Ltd., Bedford Technology Park, Thurleigh, Bedford, UK
| | - Matt P Morgan
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK; Directorate of Critical Care, Cardiff and Vale University Health Board, University Hospital of Wales, Heath Park, Cardiff, UK
| | - Anna Rita Liuzzi
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Chan-Yu Lin
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK; Kidney Research Center, Chang Gung Memorial Hospital, Chang Gung University, College of Medicine, Taoyuan City, Taiwan
| | - Kieron L Donovan
- Wales Kidney Research Unit, Heath Park Campus, Cardiff, UK; Directorate of Nephrology and Transplantation, Cardiff and Vale University Health Board, University Hospital of Wales, Heath Park, Cardiff, UK
| | | | - Peter H Morgan
- Cardiff Business School, Cardiff University, Cardiff, UK
| | - Paul Davis
- Mologic Ltd., Bedford Technology Park, Thurleigh, Bedford, UK
| | - Ian Weeks
- Systems Immunity Research Institute, Cardiff University, Cardiff, UK
| | - Donald J Fraser
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK; Wales Kidney Research Unit, Heath Park Campus, Cardiff, UK; Directorate of Nephrology and Transplantation, Cardiff and Vale University Health Board, University Hospital of Wales, Heath Park, Cardiff, UK; Systems Immunity Research Institute, Cardiff University, Cardiff, UK
| | - Nicholas Topley
- Wales Kidney Research Unit, Heath Park Campus, Cardiff, UK; Systems Immunity Research Institute, Cardiff University, Cardiff, UK
| | - Matthias Eberl
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK; Systems Immunity Research Institute, Cardiff University, Cardiff, UK.
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Jamshidnezhad A, Azizi A, Zadeh SR, Shirali S, Shoushtari MH, Sabaghan Y, Ziagham V, Attarzadeh M. A Computer Based Model in Comparison with Sonography Imaging to Diagnosis of Acute Appendicitis in Iran. J Acute Med 2017; 7:10-18. [PMID: 32995164 DOI: 10.6705/j.jacme.2017.0701.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Introduction Acute appendicitis overlaps with conditions of other diseases in terms of Symptoms and signs in the first hours of presentation. Ultrasound imaging and laboratory tests are usually used to decrease the diagnosis errors in the case of abdominal pain. However, same results may be happened using the mentioned examination tools for a string of diseases with abdominal pain. Moreover, those tests raise the medical costs for hospitals and patients. Clinical Decision Support Systems (CDSSs) can be used to assist the physicians to make the proper health care decisions particularly in the unreliable conditions. Objectives To improve the decision making process by physicians in diagnosis of acute appendicitis, an optimizing model was developed. The main objective is to discover a diagnostic model using the minimum clinical factors available in the first hours of abdominal pain. Methods Fuzzy-rule based classifier is a known technique in the Decision Support Systems (DSSs). In this article thus the useful clinical factors were explored and the diagnosis knowledge was discovered using Honey Bee Reproduction Cycle (HRBC) algorithm in the Fuzzy-rule based system. In this model, the proposed algorithm created the Fuzzy rules as the diagnosis knowledge in an optimizing process. To evaluate the accuracy of the proposed model for diagnosing of appendicitis, a collection of data was gathered from abdominal patients who referred to the educational general hospitals in Ahvaz, Iran in 2014 to 2015 years. In this process, the proposed model was optimized first in a training phase using a training dataset, and then it was tested with the testing dataset. Then, the achieved results from the computer base model were compared with ultrasound imaging findings before surgery as well as other detection methods in the previous studies. Results The comparison results illustrated that the proposed hybrid classification model as a CDSS improves considerably the accuracy of acute appendicitis diagnosis. Experimental outcomes illustrated that the proposed algorithm improves considerably the optimization performance in the diagnostic problem with the accuracy rate of 89.9%. The mentioned rate was achieved while a limited range of factors as the input parameters were used in the hybrid model. Conclusion The proposed differential diagnostic model can be used as a CDSS especially conditions in which access to costly equipment such as CT scans and Sonography tools are limited. The developed model improves the diagnosis time as well as the treatment costs for the patients with acute abdomen suspicious of acute appendicitis.
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Affiliation(s)
- Amir Jamshidnezhad
- Ahvaz Jundishapur University of Medical Sciences Department of Health Information Technology, Faculty of Allied Health Sciences Ahvaz Iran
| | - Ahmad Azizi
- Ahvaz Jundishapur University of Medical Sciences Department of Health Information Technology, Faculty of Allied Health Sciences Ahvaz Iran
| | - Sara Rekabeslami Zadeh
- Ahvaz Jundishapur University of Medical Sciences Department of Health Information Technology, Faculty of Allied Health Sciences Ahvaz Iran
| | - Saeed Shirali
- Ahvaz Jundishapur University of Medical Sciences Department of Laboratory Sciences, Faculty of Allied Health Sciences Ahvaz Iran
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Saly D, Yang A, Triebwasser C, Oh J, Sun Q, Testani J, Parikh CR, Bia J, Biswas A, Stetson C, Chaisanguanthum K, Wilson FP. Approaches to Predicting Outcomes in Patients with Acute Kidney Injury. PLoS One 2017; 12:e0169305. [PMID: 28122032 PMCID: PMC5266278 DOI: 10.1371/journal.pone.0169305] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Accepted: 12/14/2016] [Indexed: 11/19/2022] Open
Abstract
Despite recognition that Acute Kidney Injury (AKI) leads to substantial increases in morbidity, mortality, and length of stay, accurate prognostication of these clinical events remains difficult. It remains unclear which approaches to variable selection and model building are most robust. We used data from a randomized trial of AKI alerting to develop time-updated prognostic models using stepwise regression compared to more advanced variable selection techniques. We randomly split data into training and validation cohorts. Outcomes of interest were death within 7 days, dialysis within 7 days, and length of stay. Data elements eligible for model-building included lab values, medications and dosages, procedures, and demographics. We assessed model discrimination using the area under the receiver operator characteristic curve and r-squared values. 2241 individuals were available for analysis. Both modeling techniques created viable models with very good discrimination ability, with AUCs exceeding 0.85 for dialysis and 0.8 for death prediction. Model performance was similar across model building strategies, though the strategy employing more advanced variable selection was more parsimonious. Very good to excellent prediction of outcome events is feasible in patients with AKI. More advanced techniques may lead to more parsimonious models, which may facilitate adoption in other settings.
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Affiliation(s)
- Danielle Saly
- Yale University School of Medicine, New Haven, CT, United States of America
| | - Alina Yang
- Yale University School of Medicine, New Haven, CT, United States of America
| | - Corey Triebwasser
- Yale University School of Public Health, New Haven, CT, United States of America
| | - Janice Oh
- Yale University School of Public Health, New Haven, CT, United States of America
| | - Qisi Sun
- Yale University School of Medicine, New Haven, CT, United States of America
| | - Jeffrey Testani
- Yale University School of Medicine, New Haven, CT, United States of America
| | - Chirag R. Parikh
- Yale University School of Medicine, New Haven, CT, United States of America
- Program of Applied Translational Research, Yale University School of Medicine, New Haven, CT, United States of America
- Clinical Epidemiology Research Center, Veterans Affairs Medical Center, West Haven, CT, United States of America
| | - Joshua Bia
- Program of Applied Translational Research, Yale University School of Medicine, New Haven, CT, United States of America
| | - Aditya Biswas
- Program of Applied Translational Research, Yale University School of Medicine, New Haven, CT, United States of America
| | | | | | - F. Perry Wilson
- Yale University School of Medicine, New Haven, CT, United States of America
- Program of Applied Translational Research, Yale University School of Medicine, New Haven, CT, United States of America
- Clinical Epidemiology Research Center, Veterans Affairs Medical Center, West Haven, CT, United States of America
- * E-mail:
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Wise ES, Hocking KM, Kavic SM. Prediction of excess weight loss after laparoscopic Roux-en-Y gastric bypass: data from an artificial neural network. Surg Endosc 2016; 30:480-488. [PMID: 26017908 PMCID: PMC4662927 DOI: 10.1007/s00464-015-4225-7] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2014] [Accepted: 04/17/2015] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Laparoscopic Roux-en-Y gastric bypass (LRYGB) has become the gold standard for surgical weight loss. The success of LRYGB may be measured by excess body mass index loss (%EBMIL) over 25 kg/m(2), which is partially determined by multiple patient factors. In this study, artificial neural network (ANN) modeling was used to derive a reasonable estimate of expected postoperative weight loss using only known preoperative patient variables. Additionally, ANN modeling allowed for the discriminant prediction of achievement of benchmark 50% EBMIL at 1 year postoperatively. METHODS Six hundred and forty-seven LRYGB included patients were retrospectively reviewed for preoperative factors independently associated with EBMIL at 180 and 365 days postoperatively (EBMIL180 and EBMIL365, respectively). Previously validated factors were selectively analyzed, including age; race; gender; preoperative BMI (BMI0); hemoglobin; and diagnoses of hypertension (HTN), diabetes mellitus (DM), and depression or anxiety disorder. Variables significant upon multivariate analysis (P < .05) were modeled by "traditional" multiple linear regression and an ANN, to predict %EBMIL180 and %EBMIL365. RESULTS The mean EBMIL180 and EBMIL365 were 56.4 ± 16.5 % and 73.5 ± 21.5%, corresponding to total body weight losses of 25.7 ± 5.9% and 33.6 ± 8.0%, respectively. Upon multivariate analysis, independent factors associated with EBMIL180 included black race (B = -6.3%, P < .001), BMI0 (B = -1.1%/unit BMI, P < .001), and DM (B = -3.2%, P < .004). For EBMIL365, independently associated factors were female gender (B = 6.4%, P < .001), black race (B = -6.7%, P < .001), BMI0 (B = -1.2%/unit BMI, P < .001), HTN (B = -3.7%, P = .03), and DM (B = -6.0%, P < .001). Pearson r(2) values for the multiple linear regression and ANN models were 0.38 (EBMIL180) and 0.35 (EBMIL365), and 0.42 (EBMIL180) and 0.38 (EBMIL365), respectively. ANN prediction of benchmark 50% EBMIL at 365 days generated an area under the curve of 0.78 ± 0.03 in the training set (n = 518) and 0.83 ± 0.04 (n = 129) in the validation set. CONCLUSIONS Available at https://redcap.vanderbilt.edu/surveys/?s=3HCR43AKXR, this or other ANN models may be used to provide an optimized estimate of postoperative EBMIL following LRYGB.
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Affiliation(s)
- Eric S Wise
- Department of Surgery, Vanderbilt University Medical Center, 1161 21st Ave S, MCN T2121, Nashville, TN, 37232-2730, USA.
- Department of General Surgery, University of Maryland Medical Center, Baltimore, MD, USA.
| | - Kyle M Hocking
- Department of Surgery, Vanderbilt University Medical Center, 1161 21st Ave S, MCN T2121, Nashville, TN, 37232-2730, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Stephen M Kavic
- Department of General Surgery, University of Maryland Medical Center, Baltimore, MD, USA
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Andersson M, Rubér M, Ekerfelt C, Hallgren HB, Olaison G, Andersson RE. Can new inflammatory markers improve the diagnosis of acute appendicitis? World J Surg 2015; 38:2777-83. [PMID: 25099684 DOI: 10.1007/s00268-014-2708-7] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
BACKGROUND The diagnosis of appendicitis is difficult and resource consuming. New inflammatory markers have been proposed for the diagnosis of appendicitis, but their utility in combination with traditional diagnostic variables has not been tested. Our objective is to explore the potential of new inflammatory markers for improving the diagnosis of appendicitis. METHODS The diagnostic properties of the six most promising out of 21 new inflammatory markers (interleukin [IL]-6, chemokine ligand [CXCL]-8, chemokine C-C motif ligand [CCL]-2, serum amyloid A [SAA], matrix metalloproteinase [MMP]-9, and myeloperoxidase [MPO]) were compared with traditional diagnostic variables included in the Appendicitis Inflammatory Response (AIR) score (right iliac fossa pain, vomiting, rebound tenderness, guarding, white blood cell [WBC] count, proportion neutrophils, C-reactive protein and body temperature) in 432 patients with suspected appendicitis by uni- and multivariable regression models. RESULTS Of the new inflammatory variables, SAA, MPO, and MMP9 were the strongest discriminators for all appendicitis (receiver operating characteristics [ROC] 0.71) and SAA was the strongest discriminator for advanced appendicitis (ROC 0.80) compared with defence or rebound tenderness, which were the strongest traditional discriminators for all appendicitis (ROC 0.84) and the WBC count for advanced appendicitis (ROC 0.89). CCL2 was the strongest independent discriminator beside the AIR score variables in a multivariable model. The AIR score had an ROC area of 0.91 and could correctly classify 58.3 % of the patients, with an accuracy of 92.9 %. This was not improved by inclusion of the new inflammatory markers. CONCLUSION The conventional diagnostic variables for appendicitis, as combined in the AIR score, is an efficient screening instrument for classifying patients as low-, indeterminate-, or high-risk for appendicitis. The addition of the new inflammatory variables did not improve diagnostic performance further.
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Affiliation(s)
- Manne Andersson
- Department of Clinical and Experimental Medicine, Surgery, Faculty of Health Sciences, Linköping University, Linköping, Sweden,
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Yoo TK, Kim SK, Choi SB, Kim DY, Kim DW. Interpretation of movement during stair ascent for predicting severity and prognosis of knee osteoarthritis in elderly women using support vector machine. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:192-6. [PMID: 24109657 DOI: 10.1109/embc.2013.6609470] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Several studies have demonstrated that pathologic movement changes in knee osteoarthritis (OA) may contribute to disease progression. The aim of this study was to investigate the association between movement changes during stair ascent and pain, radiographic severity, and prognosis of knee OA in the elderly women using machine learning (ML) over a seven-year follow-up period. Eighteen elderly female patients with knee OA and 20 healthy controls were enrolled. Kinematic data for stair ascent were obtained using a 3D-motion analysis system at baseline. Kinematic factors were analyzed based on one of the popular ML methods, support vector machines (SVM). SVM was used to search kinematic predictors associated with pain, radiographic severity of knee OA, and unfavorable outcomes, which were defined as persistent knee pain as reported at the seven-year follow-up or as having undergone total knee replacement during the follow-up period. Six patients (46.2%) had unfavorable outcomes at the seven-year follow-up. SVM showed accuracy of detection of knee OA (97.4%), prediction of pain (83.3%), radiographic severity (83.3%), and unfavorable outcomes (69.2%). The predictors with SVM included the time of stair ascent, maximal anterior pelvis tilting, knee flexion at initial foot contact, and ankle dorsiflexion at initial foot contact. The interpretation of movement during stair ascent using ML may be helpful for physicians not only in detecting knee OA, but also in evaluating pain and radiographic severity.
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Kim SK, Yoo TK, Oh E, Kim DW. Osteoporosis risk prediction using machine learning and conventional methods. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:188-91. [PMID: 24109656 DOI: 10.1109/embc.2013.6609469] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women, and compared with the ability of a conventional clinical decision tool, osteoporosis self-assessment tool (OST). We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Surveys (KNHANES V-1). The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and logistic regression (LR) based on various predictors associated with low bone density. The learning models were compared with OST. SVM had significantly better area under the curve (AUC) of the receiver operating characteristic (ROC) than ANN, LR, and OST. Validation on the test set showed that SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0%. We were the first to perform comparisons of the performance of osteoporosis prediction between the machine learning and conventional methods using population-based epidemiological data. The machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.
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Kenngott HG, Wagner M, Nickel F, Wekerle AL, Preukschas A, Apitz M, Schulte T, Rempel R, Mietkowski P, Wagner F, Termer A, Müller-Stich BP. Computer-assisted abdominal surgery: new technologies. Langenbecks Arch Surg 2015; 400:273-81. [PMID: 25701196 DOI: 10.1007/s00423-015-1289-8] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2015] [Accepted: 02/09/2015] [Indexed: 12/16/2022]
Abstract
BACKGROUND Computer-assisted surgery is a wide field of technologies with the potential to enable the surgeon to improve efficiency and efficacy of diagnosis, treatment, and clinical management. PURPOSE This review provides an overview of the most important new technologies and their applications. METHODS A MEDLINE database search was performed revealing a total of 1702 references. All references were considered for information on six main topics, namely image guidance and navigation, robot-assisted surgery, human-machine interface, surgical processes and clinical pathways, computer-assisted surgical training, and clinical decision support. Further references were obtained through cross-referencing the bibliography cited in each work. Based on their respective field of expertise, the authors chose 64 publications relevant for the purpose of this review. CONCLUSION Computer-assisted systems are increasingly used not only in experimental studies but also in clinical studies. Although computer-assisted abdominal surgery is still in its infancy, the number of studies is constantly increasing, and clinical studies start showing the benefits of computers used not only as tools of documentation and accounting but also for directly assisting surgeons during diagnosis and treatment of patients. Further developments in the field of clinical decision support even have the potential of causing a paradigm shift in how patients are diagnosed and treated.
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Affiliation(s)
- H G Kenngott
- Department of General, Abdominal and Transplant Surgery, Ruprecht-Karls-University, Heidelberg, Germany
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In Silico Predictions of Human Skin Permeability using Nonlinear Quantitative Structure–Property Relationship Models. Pharm Res 2015; 32:2360-71. [DOI: 10.1007/s11095-015-1629-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2014] [Accepted: 01/13/2015] [Indexed: 10/24/2022]
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67
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Application of Entropy-Based Attribute Reduction and an Artificial Neural Network in Medicine: A Case Study of Estimating Medical Care Costs Associated with Myocardial Infarction. ENTROPY 2014. [DOI: 10.3390/e16094788] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Screening for prediabetes using machine learning models. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2014; 2014:618976. [PMID: 25165484 PMCID: PMC4140121 DOI: 10.1155/2014/618976] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Accepted: 07/08/2014] [Indexed: 12/30/2022]
Abstract
The global prevalence of diabetes is rapidly increasing. Studies support the necessity of screening and interventions for prediabetes, which could result in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for prediabetes. Data from the Korean National Health and Nutrition Examination Survey (KNHANES) were used, excluding subjects with diabetes. The KNHANES 2010 data (n = 4685) were used for training and internal validation, while data from KNHANES 2011 (n = 4566) were used for external validation. We developed two models to screen for prediabetes using an artificial neural network (ANN) and support vector machine (SVM) and performed a systematic evaluation of the models using internal and external validation. We compared the performance of our models with that of a screening score model based on logistic regression analysis for prediabetes that had been developed previously. The SVM model showed the areas under the curve of 0.731 in the external datasets, which is higher than those of the ANN model (0.729) and the screening score model (0.712), respectively. The prescreening methods developed in this study performed better than the screening score model that had been developed previously and may be more effective method for prediabetes screening.
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Yoo TK, Kim SK, Kim DW, Choi JY, Lee WH, Oh E, Park EC. Osteoporosis risk prediction for bone mineral density assessment of postmenopausal women using machine learning. Yonsei Med J 2013; 54:1321-30. [PMID: 24142634 PMCID: PMC3809875 DOI: 10.3349/ymj.2013.54.6.1321] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
PURPOSE A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women compared to the ability of conventional clinical decision tools. MATERIALS AND METHODS We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Examination Surveys. The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests, artificial neural networks (ANN), and logistic regression (LR) based on simple surveys. The machine learning models were compared to four conventional clinical decision tools: osteoporosis self-assessment tool (OST), osteoporosis risk assessment instrument (ORAI), simple calculated osteoporosis risk estimation (SCORE), and osteoporosis index of risk (OSIRIS). RESULTS SVM had significantly better area under the curve (AUC) of the receiver operating characteristic than ANN, LR, OST, ORAI, SCORE, and OSIRIS for the training set. SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0% at total hip, femoral neck, or lumbar spine for the testing set. The significant factors selected by SVM were age, height, weight, body mass index, duration of menopause, duration of breast feeding, estrogen therapy, hyperlipidemia, hypertension, osteoarthritis, and diabetes mellitus. CONCLUSION Considering various predictors associated with low bone density, the machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.
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Affiliation(s)
- Tae Keun Yoo
- Department of Medical Engineering, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-752, Korea.
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Diabetic retinopathy risk prediction for fundus examination using sparse learning: a cross-sectional study. BMC Med Inform Decis Mak 2013; 13:106. [PMID: 24033926 PMCID: PMC3847617 DOI: 10.1186/1472-6947-13-106] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2013] [Accepted: 09/02/2013] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Blindness due to diabetic retinopathy (DR) is the major disability in diabetic patients. Although early management has shown to prevent vision loss, diabetic patients have a low rate of routine ophthalmologic examination. Hence, we developed and validated sparse learning models with the aim of identifying the risk of DR in diabetic patients. METHODS Health records from the Korea National Health and Nutrition Examination Surveys (KNHANES) V-1 were used. The prediction models for DR were constructed using data from 327 diabetic patients, and were validated internally on 163 patients in the KNHANES V-1. External validation was performed using 562 diabetic patients in the KNHANES V-2. The learning models, including ridge, elastic net, and LASSO, were compared to the traditional indicators of DR. RESULTS Considering the Bayesian information criterion, LASSO predicted DR most efficiently. In the internal and external validation, LASSO was significantly superior to the traditional indicators by calculating the area under the curve (AUC) of the receiver operating characteristic. LASSO showed an AUC of 0.81 and an accuracy of 73.6% in the internal validation, and an AUC of 0.82 and an accuracy of 75.2% in the external validation. CONCLUSION The sparse learning model using LASSO was effective in analyzing the epidemiological underlying patterns of DR. This is the first study to develop a machine learning model to predict DR risk using health records. LASSO can be an excellent choice when both discriminative power and variable selection are important in the analysis of high-dimensional electronic health records.
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Sun T, Wang J, Li X, Lv P, Liu F, Luo Y, Gao Q, Zhu H, Guo X. Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 111:519-524. [PMID: 23727300 DOI: 10.1016/j.cmpb.2013.04.016] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2012] [Revised: 04/24/2013] [Accepted: 04/24/2013] [Indexed: 06/02/2023]
Abstract
Lung cancer is one of the most common forms of cancer resulting in over a million deaths per year worldwide. In this paper, the usage of support vector machine (SVM) classification for lung cancer is investigated, presenting a systematic quantitative evaluation against Boosting, Decision trees, k-nearest neighbor, LASSO regressions, neural networks and random forests. A large database of 5984 regions of interest (ROIs) and 488 input features (including textural features, patient characteristics, and morphological features) were used to train the classifiers and evaluate for their performance. The evaluation for classifiers' performance was based on a tenfold cross validation framework, receiver operating characteristic curve (ROC), and Matthews correlation coefficient. Area under curve (AUC) of SVM, Boosting, Decision trees, k-nearest neighbor, LASSO, neural networks, random forests were 0.94, 0.86, 0.73, 0.72, 0.91, 0.92, and 0.85, respectively. It was proved that SVM classification offered significantly increased classification performance compared to the reference methods. This scheme may be used as an auxiliary tool to differentiate between benign and malignant SPNs of CT images in future.
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Affiliation(s)
- Tao Sun
- School of Public Health, Capital Medical University, Beijing 100069, China.
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Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques. Med Biol Eng Comput 2013; 51:1059-67. [DOI: 10.1007/s11517-013-1091-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2013] [Accepted: 06/08/2013] [Indexed: 10/26/2022]
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A preclustering-based ensemble learning technique for acute appendicitis diagnoses. Artif Intell Med 2013; 58:115-24. [DOI: 10.1016/j.artmed.2013.03.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2011] [Revised: 03/03/2013] [Accepted: 03/17/2013] [Indexed: 12/29/2022]
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Wray CJ, Kao LS, Millas SG, Tsao K, Ko TC. Acute appendicitis: controversies in diagnosis and management. Curr Probl Surg 2013; 50:54-86. [PMID: 23374326 DOI: 10.1067/j.cpsurg.2012.10.001] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Choi JY, Kim SK, Lee WH, Yoo TK, Kim DW. A survival prediction model of rats in hemorrhagic shock using the random forest classifier. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:5570-3. [PMID: 23367191 DOI: 10.1109/embc.2012.6347256] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Hemorrhagic shock is the cause of one third of deaths resulting from injury in the world. Although many studies have tried to diagnose hemorrhagic shock early and accurately, such attempts were inconclusive due to compensatory mechanisms of humans. The objective of this study was to construct a survival prediction model of rats in hemorrhagic shock using a random forest (RF) model, which is a newly emerged classifier acknowledged for its performance. Heart rate (HR), mean arterial pressure (MAP), respiratory rate (RR), lactate concentration (LC), and perfusion (PF) measured in rats were used as input variables for the RF model and its performance was compared with that of a logistic regression (LR) model. Before constructing the models, we performed a 5-fold cross validation for RF variable selection and forward stepwise variable selection for the LR model to see which variables are important for the models. For the LR model, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (ROC-AUC) were 1, 0.89, 0.94, and 0.98, respectively. For the RF models, sensitivity, specificity, accuracy, and AUC were 0.96, 1, 0.98, and 0.99, respectively. In conclusion, the RF model was superior to the LR model for survival prediction in the rat model.
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Affiliation(s)
- Joon Yul Choi
- Brain Korea 21 Project for Medical Science, Yonsei University, Seoul, Korea.
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Yoldaş Ö, Tez M, Karaca T. Artificial neural networks in the diagnosis of acute appendicitis. Am J Emerg Med 2012; 30:1245-7. [DOI: 10.1016/j.ajem.2011.06.019] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2011] [Revised: 06/03/2011] [Accepted: 06/03/2011] [Indexed: 11/16/2022] Open
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Humes DJ, Simpson J. Clinical Presentation of Acute Appendicitis: Clinical Signs—Laboratory Findings—Clinical Scores, Alvarado Score and Derivate Scores. IMAGING OF ACUTE APPENDICITIS IN ADULTS AND CHILDREN 2012. [DOI: 10.1007/174_2011_211] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Hamilton EF, Smith S, Yang L, Warrick P, Ciampi A. Third- and fourth-degree perineal lacerations: defining high-risk clinical clusters. Am J Obstet Gynecol 2011; 204:309.e1-6. [PMID: 21349493 DOI: 10.1016/j.ajog.2010.12.048] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2010] [Revised: 12/01/2010] [Accepted: 12/30/2010] [Indexed: 11/16/2022]
Abstract
OBJECTIVE Statistical methods that measure the independent contribution of individual factors for third-/fourth-degree perineal laceration (TFPL) fall short when the clinician is faced with a combination of factors. Our objective was to demonstrate how a statistical technique, classification and regression trees (CART), can identify high-risk clinical clusters. STUDY DESIGN We performed multivariable logistic regression, and CART analysis on data from 25,150 term vaginal births. RESULTS Multivariable analyses found strong associations with the use of episiotomy, forceps, vacuum, nulliparity, and birthweight. CART ranked episiotomy, operative delivery, and birthweight as the more discriminating factors and defined distinct risk groups with TFPL rates that ranged from 0-100%. For example, without episiotomy, the rate of TFPL was 2.2%. In the presence of an episiotomy, forceps, and birthweight of >3634 g, the rate of TFPL was 68.9%. CONCLUSION CART showed that certain combinations held low risk, where as other combinations carried extreme risk, which clarified how choices on delivery options can markedly affect the rate of TFPL for specific mothers.
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