Brugada syndrome (BrS) is a condition characterized by a distinct ST-segment elevation in the right precordial leads of the electrocardiogram and, clinically, by an increased risk of cardiac arrhythmia and sudden death. The condition predominantly exhibits an autosomal dominant pattern of inheritance with an average prevalence of 5:10,000 worldwide. Currently, more than 100 mutations in seven genes have been associated with BrS. Loss-of-function mutations in SCN5A, which encodes the α-subunit of the Nav1.5 sodium ion channel conducting the depolarizing INa current, causes 15–20% of BrS cases. A few mutations have been described in GPD1L, which encodes glycerol-3-phosphate dehydrogenase-1 like protein; CACNA1C, which encodes the α-subunit of the Cav1.2 ion channel conducting the depolarizing IL,Ca current; CACNB2, which encodes the stimulating β2-subunit of the Cav1.2 ion channel; SCN1B and SCN3B, which, in the heart, encodes β-subunits of the Nav1.5 sodium ion channel, and KCNE3, which encodes the ancillary inhibitory β-subunit of several potassium channels including the Kv4.3 ion channel conducting the repolarizing potassium Ito current. BrS exhibits variable expressivity, reduced penetrance, and “mixed phenotypes,” where families contain members with BrS as well as long QT syndrome, atrial fibrillation, short QT syndrome, conduction disease, or structural heart disease, have also been described. Hum Mutat 30:1–11, 2009. © 2009 Wiley-Liss, Inc.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/humu.21066

The genetic basis of Brugada syndrome: a mutation update.

Enabling Medication Management Through Health Information Technology

Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)

https://pure.rug.nl/ws/files/66844988/263.full.pdf

A critical assessment of feature selection methods for biomarker discovery in clinical proteomics

Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B.

In the health insurance system of Japan, a fee-for-service system has been applied to individual treatment services since 1958. This system involves a structural problem of causing an increase in examination and drug administration. A flat-fee payment system called DPC was introduced in April 2003 to solve the problems of the fee-for-service system. Based on the data of 2003 and 2004, we assessed the impact of DPC in Japan, and obtained the following conclusions: First, the introduction of DPC in Japan could not decrease the absolute value of medical costs; second, the internal efficiency of the institutions was improved, for example, by reducing the mean length of hospitalizations; third, the DPC-based diagnosis classification is considered to be effective for simplifying the medical fee system within the framework of EBM and for providing patients with information; and fourth, after introduction of the DPC, structural problems remain in the flat-fee payment system, such as examination and treatment of low quality, selection of patients and up coding. Its introduction should thus be performed with sufficient caution. We will make greater efforts to establish a better medical fee system by evaluating these problems.

Impact of the Japanese Diagnosis Procedure Combination-based Payment System in Japan

Although liver biopsy is currently regarded as the gold standard for staging liver fibrosis in chronic hepatitis C, it is a costly invasive procedure and carries a small risk for complication Our aim in this study was to construct a simple model to distinguish between patients with no or mild fibrosis (METAVIR F0---F1) versus those with clinically significant fibrosis (METAVIR F2---F4) We retrospectively studied 204 consecutive CHC patients Thirty-four serum markers with age, gender, duration of infection were assessed to classify fibrosis with a classifier known as the support vector machine (SVM) The method of feature selection known as sequential forward floating selection (SFFS) was introduced before the performance of SVM When four serum markers were extracted with SFFS-SVM, F2---F4 could be predicted accurately in 96% Our study showed that application of this model could identify CHC patients with clinically significant fibrosis with a high degree of accuracy and may decrease the need for liver biopsy

Support Vector Machine-Based Feature Selection for Classification of Liver Fibrosis Grade in Chronic Hepatitis C

Brugada syndrome is characterized by an ST-segment elevation in the right precordial ECG leads and a high incidence of sudden death in patients with structurally normal hearts. Some trials have demonstrated that the cost-effectiveness of ICD implantation treatment in patients with structurally abnormal hearts is more favorable than that of control treatment. We used Treeage pro 2005 to estimate costs and survival among the Brugada syndrome patients who received either an ICD or were treated by control therapy of Ito-blocking properties (quinidine) or β-blockers (propranolol). In conclusion, our analysis suggests that prophylactic implantation of an ICD has good cost-effectiveness in patients with Brugada syndrome who are at high risk of sudden death. ICD treatment has shown a cost-effectiveness ratio below $9,591 per QALY gained from trials of defibrillator vs β-blockers for Unexplained Death in Thailand (DEBUT). The control therapy of quinidine may be a good choice for patients who are infants or living in developing countries.

Cost-Effectiveness of Implantable Cardioverter-Defibrillators in Brugada Syndrome Treatment

Chronic hepatitis C is a disease that is difficult to treat. At present, interferon might be the only drug, which can cure this kind of disease, but its efficacy is limited and patients face the risk of side effects and high expense, so doctors considering interferon must make a serious choice. The purpose of this study is to establish a simple model and use the clinical data to predict the interferon efficacy. This model is a combination of Feature Subset Selection and the Classifier using a Support Vector Machine (SVM). The study indicates that when five features have been selected, the identification by the SVM is as follows: the identification rate for the effective group is 85%, and the ineffective group 83%. Analysis of selected features show that HCV-RNA level, hepatobiopsy, HCV genotype, ALP and CHE are the most significant features. The results thus serve for the doctors' reference when they make decisions regarding interferon treatment.

/pdf/efficacy-of-interferon-treatment-for-chronic-hepatitis-c-51x9wf6t9e.pdf

Efficacy of Interferon Treatment for Chronic Hepatitis C Predicted by Feature Subset Selection and Support Vector Machine

Today, radio-frequency ablation has been shown to be a safe and effective method to treat paroxysmal tachycardia with Wolff-Parkinson-White syndrome. The many criteria reported for localizing the sites of accessory pathways from a 12-lead electrocardiogram have not proven adequate to differentiate the correct sites of accessory pathways for all situations. This study trained an artificial neural network to differentiate the varied features needed to localize 10 sites of accessory pathways. One hundred fifty patients underwent successful catheter ablation, with manifest single and antegradely conducting accessory pathways. Using the two electrocardiogram features of polarity of delta wave and R wave's share of QRS complex, an artificial neural network learned the characteristics of electrocardiogram waves for each site of the 10 accessory pathways through 90 learning cases, and an applicable network model was developed for testing. In 58 of 60 test cases (96.7%), sites of accessory pathways were localized correctly by the network. Based on the method employed in the present study, it thus becomes possible to predict the sites of accessory pathways with Wolff-Parkinson-White syndrome in more detail by using an artificial neural network with a 12-lead electrocardiogram. In the future, when this method is incorporated into a conventional automatic electrocardiogram system which could analyze delta waves and ORS complex, it will become useful to automatically diagnose the locations of the accessory pathways with Wolff-Parkinson-White syndrome in clinical practice.

Kai Wang

Papers

Impact of the Japanese Diagnosis Procedure Combination-based Payment System in Japan

Support Vector Machine-Based Feature Selection for Classification of Liver Fibrosis Grade in Chronic Hepatitis C

Cost-Effectiveness of Implantable Cardioverter-Defibrillators in Brugada Syndrome Treatment

Efficacy of Interferon Treatment for Chronic Hepatitis C Predicted by Feature Subset Selection and Support Vector Machine

Use of an artificial neural network to localize accessory pathways of Wolff-Parkinson-White syndrome with 12-lead electrocardiogram.