45 research outputs found

    Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network

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    Background: A database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group. Methods: The three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80 % of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20 % of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models. Conclusions: The ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection

    Applying machine learning to predict patient-specific current CD4 cell count in order to determine the progression of human immunodeficiency virus (HIV) infection

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    This work shows the application of machine learning to predict current CD4 cell count of an HIV-positive patient using genome sequences, viral load and time. A regression model predicting actual CD4 cell counts and a classification model predicting if a patient’s CD4 cell count is less than 200 was built using a support vector machine and neural network. The most accurate regression and classification model took as input the viral load, time, and genome and produced a correlation of co-efficient of 0.9 and an accuracy of 95%, respectively, proving that a CD4 cell count measure may be accurately predicted using machine learning on genotype, viral load and time.Keywords: Human immunodeficiency virus (HIV), antigens, CD4, computational biology, artificial intelligence, data mining, pattern recognition.African Journal of Biotechnology Vol. 12(23), pp. 3724-373

    Machine learning in transfusion medicine: A scoping review

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    Comparison of Artificial Neural Network and Logistic Regression Models for Predicting In-Hospital Mortality after Primary Liver Cancer Surgery

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    BACKGROUND: Since most published articles comparing the performance of artificial neural network (ANN) models and logistic regression (LR) models for predicting hepatocellular carcinoma (HCC) outcomes used only a single dataset, the essential issue of internal validity (reproducibility) of the models has not been addressed. The study purposes to validate the use of ANN model for predicting in-hospital mortality in HCC surgery patients in Taiwan and to compare the predictive accuracy of ANN with that of LR model. METHODOLOGY/PRINCIPAL FINDINGS: Patients who underwent a HCC surgery during the period from 1998 to 2009 were included in the study. This study retrospectively compared 1,000 pairs of LR and ANN models based on initial clinical data for 22,926 HCC surgery patients. For each pair of ANN and LR models, the area under the receiver operating characteristic (AUROC) curves, Hosmer-Lemeshow (H-L) statistics and accuracy rate were calculated and compared using paired T-tests. A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and the relative importance of variables. Compared to the LR models, the ANN models had a better accuracy rate in 97.28% of cases, a better H-L statistic in 41.18% of cases, and a better AUROC curve in 84.67% of cases. Surgeon volume was the most influential (sensitive) parameter affecting in-hospital mortality followed by age and lengths of stay. CONCLUSIONS/SIGNIFICANCE: In comparison with the conventional LR model, the ANN model in the study was more accurate in predicting in-hospital mortality and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data

    Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

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    The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation

    Hybrid ANFIS-Taguchi Method Based on PCA for Blood Bank Demand Forecasting

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    Kan; hastalıklar, ameliyatlar veya yaralanmalar nedeniyle her gün binlerce insan tarafından ihtiyaç duyulan hayati bir üründür. Bu nedenle hastanelerin kan ihtiyacını karşılayan kan bankalarının stoklarında yeterli miktarda kan bulundurması gereklidir. Gereğinden az miktarda kan elde bulundurulması ihtiyacın karşılanamaması ve can kaybı gibi önemli sorunlar oluştururken, fazla miktarda kanın stoklanması ise kanın bozulmasına ve kan ihtiyacı olan farklı hastanelerin stoksuz kalmasına neden olmaktadır. Bu çalışmada öncelikle kan bileşenlerinden biri olan eritrosit süspansiyonu talebine etki eden kriterler belirlenerek; bu kriterlere göre makine öğrenme algoritmalarından uyarlamalı ağ tabanlı bulanık çıkarım sistemi (ANFIS) yöntemi ile talebin tahmin edilmesi amaçlanmaktadır. Ancak talebe etki eden çok sayıda kriter olduğu için gruplandırarak azaltmak ve kriterler arasındaki bağımlılıkları ortadan kaldırmak amacıyla temel bileşen analizi (PCA) yönteminden yararlanılmıştır. Ayrıca ANFIS’in performansı; modelin yapısı ve öğrenmesini etkileyen parametre değerlerinin doğru belirlenmesi ile ilişkili olduğundan en yüksek doğrulukla tahmini sağlayacak değerler Taguchi deney tasarımı yöntemiyle belirlenmiştir. Geliştirilen PCA esaslı hibrit ANFIS-Taguchi yöntemi bir bölge kan merkezinde uygulanmıştır. Korelasyon katsayısı (??) performans ölçütü ile yöntemin tahmin yeteneği değerlendirilmiştir. Uygulama sonunda tahmin edilen eritrosit süspansiyon talep miktarının %88.1 oranla gerçekleşen talep miktarı ile benzer sonuç verdiği görülmüştür.Blood is a vital product that is needed by thousands of people every day due to diseases, surgeries or injuries. For this reason, it is necessary that the blood banks have enough blood quantity to meet the blood needs of hospitals . The provision of small amounts of blood in hospitals creates significant problems such as loss of life and can’t meet the demand. On the other hand, the stocking of large amounts of blood leads to the wastage of blood and the stockless of blood different hospitals. The aim of this study is to determine the criteria affecting blood demand and to forecast the blood demand by the machine learning algorithm Adaptive Network Based Fuzzy Inference System (ANFIS) method. However, since the number of impact criteria is high, principal component analysis (PCA) method has been used in order to decrease criteria and eliminate the dependencies between the criteria. In addition, the performance of ANFIS depend on determining ANFIS parameters that affect its structure and learning. So to provide the highest learning ANFIS parameters were determined by the Taguchi experimental design method. The developed hybrid method was applied in a regional blood center and evaluated with correlation coefficient (??). At the end of the application, it is seen that the estimated red blood cells demand is similar to the demand amount realized at the rate of 88.1%

    Self-healing of Concrete Under Diverse Environmental Exposure

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    Self-healing efficiency of cement-based materials has so far been evaluated mostly through the healing of surface cracks, without adequately capturing the dominant effects of environmental exposure or accurately quantifying the volume of cracks healed. In addition, the effects of diverse additions such as silica-based materials, swelling agents, superabsorbent polymers, and carbonating minerals on self-healing performance under different environmental exposure, remain largely unexplored. In this dissertation, multiple test methods were used to investigate self-healing of cracks in cement mortar incorporating metakaolin, bentonite, fly ash, superabsorbent polymers, and calcium carbonate microfiller under different environmental exposure (i.e. cold and hot temperatures, high and low humidity, wet and dry cycles, and continuous underwater submersion). Change in crack width was monitored using optical microscopy. Scanning electron microscopy coupled with energy disperse X-ray analysis was used to identify healing compounds. Mercury intrusion porosimetry and water absorption were employed to assess porosity. X-ray computed micro-tomography (X-ray µCT) with 3-dimensional image processing was used to segment and quantify cracks before and after healing. The findings should stimulate concerted research efforts to bridge the gap between ideal laboratory conditions and realistic field exposure in future self-healing research endeavors. Furthermore, an attempt was made to develop a hybrid artificial intelligence-based model to accurately predict the ability of concrete to heal its own cracks. A comprehensive database of concrete crack healing was created and used to train the proposed GA–ANN model. The results showed that the proposed GA–ANN model can capture the complex effects of various self-healing agents (e.g. biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials. This could allow tailoring self-healing strategies for enhancing the durability design of concrete, thus leading to reduced maintenance and repair costs of concrete civil infrastructure

    Random Numbers Generated from Audio and Video Sources

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    Random numbers are very useful in simulation, chaos theory, game theory, information theory, pattern recognition, probability theory, quantum mechanics, statistics, and statistical mechanics. The random numbers are especially helpful in cryptography. In this work, the proposed random number generators come from white noise of audio and video (A/V) sources which are extracted from high-resolution IPCAM, WEBCAM, and MPEG-1 video files. The proposed generator applied on video sources from IPCAM and WEBCAM with microphone would be the true random number generator and the pseudorandom number generator when applied on video sources from MPEG-1 video file. In addition, when applying NIST SP 800-22 Rev.1a 15 statistics tests on the random numbers generated from the proposed generator, around 98% random numbers can pass 15 statistical tests. Furthermore, the audio and video sources can be found easily; hence, the proposed generator is a qualified, convenient, and efficient random number generator

    Improved Quantum-Inspired Evolutionary Algorithm for Engineering Design Optimization

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    An improved quantum-inspired evolutionary algorithm is proposed for solving mixed discrete-continuous nonlinear problems in engineering design. The proposed Latin square quantum-inspired evolutionary algorithm (LSQEA) combines Latin squares and quantum-inspired genetic algorithm (QGA). The novel contribution of the proposed LSQEA is the use of a QGA to explore the optimal feasible region in macrospace and the use of a systematic reasoning mechanism of the Latin square to exploit the better solution in microspace. By combining the advantages of exploration and exploitation, the LSQEA provides higher computational efficiency and robustness compared to QGA and real-coded GA when solving global numerical optimization problems with continuous variables. Additionally, the proposed LSQEA approach effectively solves mixed discrete-continuous nonlinear design optimization problems in which the design variables are integers, discrete values, and continuous values. The computational experiments show that the proposed LSQEA approach obtains better results compared to existing methods reported in the literature
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