Pre-Operative Prediction of Advanced Prostatic Cancer Using Clinical Decision Support Systems: Accuracy Comparison between Support Vector Machine and Artificial Neural Network

Kim, Sang-Youn; Moon, Sun Young; Jung, Dae Chul; Hwang, Sung Il; Sung, Chang Kyu; Cho, Jeong Yeon; Kim, Seung Hyup; Lee, Jiwon; Lee, Hak Jong

doi:10.3348/kjr.2011.12.5.588

Cited by 30 publications

(17 citation statements)

References 30 publications

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“…Under these conditions, probabilistic design is more flexible, allowing for selective weighing of clinical factors and rapid model development. 17,18,19,20 Finally CDSS derived from health systems knowledge bases will inherently reflect the blend of culture, experiences, and resources unique to the local environment. This process more closely approximates human judgment.…”

Section: Cdssmentioning

confidence: 99%

Knowledge Bases, Clinical Decision Support Systems, and Rapid Learning in Oncology

2015

JOP

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One of the most important benefits of health information technology is to assist the cognitive process of the human mind in the face of vast amounts of health data, limited time for decision making, and the complexity of the patient with cancer. Clinical decision support tools are frequently cited as a technologic solution to this problem, but to date useful clinical decision support systems (CDSS) have been limited in utility and implementation. This article describes three unique sources of health data that underlie fundamentally different types of knowledge bases which feed into CDSS. CDSS themselves comprise a variety of models which are discussed. The relationship of knowledge bases and CDSS to rapid learning health systems design is critical as CDSS are essential drivers of rapid learning in clinical care.

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Section: Cdssmentioning

confidence: 99%

Knowledge Bases, Clinical Decision Support Systems, and Rapid Learning in Oncology

2015

JOP

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“…[7] In an attempt to identify metastasis-related genes in colorectal cancer, SVM-T-RFE, was trained for gene expression profiles and found to be high in accuracy. [8] The performance of SVM was reported to be superior to ANN in the preoperative prediction of advanced prostatic cancers [6] and was found superior to the linear model in preoperative risk stratification with myocardial perfusion scintigrapy. [24] Kim et al [9] developed a critical care mortality prediction model by comparing machine learning algorithms including ANN, SVM, and DT.…”

Section: Discussionmentioning

confidence: 99%

“…It is a method for approximating discrete-valued functions that is robust to noisy data and capable of learning disjunctive expressions. [5] SVM has been previously used for preoperative prediction of advanced prostatic cancers [6] and tumor marker detection for different types of cancers. [7,8] ANNs, DT, and SVM were also compared with each other and performed for prediction models.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of periodontal diseases using different classification algorithms: A preliminary study

et al. 2015

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“…SVMs have been successfully applied to many classification and function prediction tasks, in various fields including science, engineering, and social sciences . SVM‐based classification methods have also been used for surgical applications, while similar regression techniques have been used to a comparatively lesser extent in medical applications . Our analysis was performed using LibSVM (National Taiwan University, http://www.csie.ntu.edu.tw/∼cjlin/libsvm/).…”

Section: Methodsmentioning

confidence: 99%

Bone mineral density and donor age are not predictive of femoral ring allograft bone mechanical strength

2014

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While metal or plastic interbody spinal fusion devices are manufactured to appropriate mechanical standards, mechanical properties of commercially prepared structural allograft bone remain relatively unassessed. Robust models predicting compressive load to failure of structural allograft bone based on easily measured variables would be useful. Three hundred twenty seven femoral rings from 34 cadaver femora were tested to failure in axial compression. Predictive variables included age, gender, bone mineral density (BMD), position along femoral shaft, maximum/minimum wall thickness, outer/inner diameter, and area. We used support vector regression and 10-fold cross-validation to develop robust nonlinear predictive models for load to failure. Model performance was measured by the root-mean-squared-deviation (RMSD) and correlation coefficients (r). A polynomial model using all variables had RMSD ¼ 7.92, r ¼ 0.84, indicating excellent performance. A model using all variables except BMD was essentially unchanged (RMSD ¼ 8.12, r ¼ 0.83). Eliminating both age and BMD produced a model with RMSD ¼ 8.41, r ¼ 0.82, again essentially unchanged. Compressive strength of structural allograft bone can be estimated using easily measured geometric parameters, without including BMD or age. As DEXA is costly and cumbersome, and setting upper age-limits for potential donors reduces the supply, our results may prove helpful to increase the quality and availability of structural allograft. Keywords: structural bone allograft; spinal fusion; biomechanics; bone mineral densityThe allograft bone industry is guided by practices intended to optimize safety and effectiveness of allograft bone in numerous clinical applications. Guidelines have been developed and used with respect to avoidance of transmission of neoplastic and infectious disease based on high level scientific data. 1,2 Adherence to these guidelines has resulted in safe and successful allograft implantation over many years of experience. 3 Despite this excellent track record, the mechanical properties of what is essentially a mechanical device 4 remain comparatively un-assessed. In fact, there are no guidelines prescribed with respect to material properties of structural allograft bone products within the Food and Drug Administration or the American Association of Tissue Banks. 2,5 A recent survey of allograft providers demonstrated that given this lack of guidelines, a variety of practices are employed with regard to screening of donors and allografts. 6 On the other hand, interbody spacers manufactured using materials such as carbon fiber, titanium, or polyetheretherketone are expected to have consistent biomechanical properties. 7-9 While the cited advantages of allograft bone over such implants include lower cost and equal or better clinical performance, 10-21 the mechanical performance of structural allograft bone intended for use as an interbody device should be similarly uniform and reliable.We have accumulated a database of load to failure measurements for fem...

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Pre-Operative Prediction of Advanced Prostatic Cancer Using Clinical Decision Support Systems: Accuracy Comparison between Support Vector Machine and Artificial Neural Network

Cited by 30 publications

References 30 publications

Knowledge Bases, Clinical Decision Support Systems, and Rapid Learning in Oncology

Knowledge Bases, Clinical Decision Support Systems, and Rapid Learning in Oncology

Diagnosis of periodontal diseases using different classification algorithms: A preliminary study

Bone mineral density and donor age are not predictive of femoral ring allograft bone mechanical strength

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