Analog Neural Network for Support Vector Machine Learning

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.

A Penalty Strategy Combined Varying-Parameter Recurrent Neural Network for Solving Time-Varying Multi-Type Constrained Quadratic Programming Problems

To obtain the optimal solution to the time-varying quadratic programming (TVQP) problem with equality and multitype inequality constraints, a penalty strategy combined varying-parameter recurrent neural network (PS-VP-RNN) for solving TVQP problems is proposed and analyzed. By using a novel penalty function designed in this article, the inequality constraint of the TVQP can be transformed into a penalty term that is added into the objective function of TVQP problems. Then, based on the design method of VP-RNN, a PS-VP-RNN is designed and analyzed for solving the TVQP with penalty term. One of the greatest advantages of PS-VP-RNN is that it cannot only solve the TVQP with equality constraints but can also solve the TVQP with inequality and bounded constraints. The global convergence theorem of PS-VP-RNN is presented and proved. Finally, three numerical simulation experiments with different forms of inequality and bounded constraints verify the effectiveness and accuracy of PS-VP-RNN in solving the TVQP problems.

Identifying patients with familial hypercholesterolemia using data mining methods in the Northern Great Plain region of Hungary

BACKGROUND AND AIMS

Familial hypercholesterolemia (FH) is one of the most frequent diseases with monogenic inheritance. Previous data indicated that the heterozygous form occurred in 1:250 people. Based on these reports, around 36,000-40,000 people are estimated to have FH in Hungary, however, there are no exact data about the frequency of the disease in our country. Therefore, we initiated a cooperation with a clinical site partner company that provides modern data mining methods, on the basis of medical and statistical records, and we applied them to two major hospitals in the Northern Great Plain region of Hungary to find patients with a possible diagnosis of FH.

METHODS

Medical records of 1,342,124 patients were included in our study. From the mined data, we calculated Dutch Lipid Clinic Network (DLCN) scores for each patient and grouped them according to the criteria to assess the likelihood of the diagnosis of FH. We also calculated the mean lipid levels before the diagnosis and treatment.

RESULTS

We identified 225 patients with a DLCN score of 6-8 (mean total cholesterol: 9.38 ± 3.0 mmol/L, mean LDL-C: 7.61 ± 2.4 mmol/L), and 11,706 patients with a DLCN score of 3-5 (mean total cholesterol: 7.34 ± 1.2 mmol/L, mean LDL-C: 5.26 ± 0.8 mmol/L).

CONCLUSIONS

The analysis of more regional and country-wide data and more frequent measurements of total cholesterol and LDL-C levels would increase the number of FH cases discovered. Data mining seems to be ideal for filtering and screening of FH in Hungary.

New approach for the diagnosis of extractions with neural network machine learning

INTRODUCTION

The decision to extract teeth for orthodontic treatment is important and difficult because it tends to be based on the practitioner's experiences. The purposes of this study were to construct an artificial intelligence expert system for the diagnosis of extractions using neural network machine learning and to evaluate the performance of this model.

METHODS

The subjects included 156 patients. Input data consisted of 12 cephalometric variables and an additional 6 indexes. Output data consisted of 3 bits to divide the extraction patterns. Four neural network machine learning models for the diagnosis of extractions were constructed using a back-propagation algorithm and were evaluated.

RESULTS

The success rates of the models were 93% for the diagnosis of extraction vs nonextraction and 84% for the detailed diagnosis of the extraction patterns.

CONCLUSIONS

This study suggests that artificial intelligence expert systems with neural network machine learning could be useful in orthodontics. Improved performance was achieved by components such as proper selection of the input data, appropriate organization of the modeling, and preferable generalization.

Preliminary study on wilcoxon learning machines

As is well known in statistics, the resulting linear regressors by using the rank-based Wilcoxon approach to linear regression problems are usually robust against (or insensitive to) outliers. This motivates us to introduce in this paper the Wilcoxon approach to the area of machine learning. Specifically, we investigate four new learning machines, namely Wilcoxon neural network (WNN), Wilcoxon generalized radial basis function network (WGRBFN), Wilcoxon fuzzy neural network (WFNN), and kernel-based Wilcoxon regressor (KWR). These provide alternative learning machines when faced with general nonlinear learning problems. Simple weights updating rules based on gradient descent will be derived. Some numerical examples will be provided to compare the robustness against outliers for various learning machines. Simulation results show that the Wilcoxon learning machines proposed in this paper have good robustness against outliers. We firmly believe that the Wilcoxon approach will provide a promising methodology for many machine learning problems.

A recurrent neural network for hierarchical control of interconnected dynamic systems

A recurrent neural network for the optimal control of a group of interconnected dynamic systems is presented in this paper. On the basis of decomposition and coordination strategy for interconnected dynamic systems, the proposed neural network has a two-level hierarchical structure: several local optimization subnetworks at the lower level and one coordination subnetwork at the upper level. A goal-coordination method is used to coordinate the interactions between the subsystems. By nesting the dynamic equations of the subsystems into their corresponding local optimization subnetworks, the number of dimensions of the neural network can be reduced significantly. Furthermore, the subnetworks at both the lower and upper levels can work concurrently. Therefore, the computation efficiency, in comparison with the consecutive executions of numerical algorithms on digital computers, is increased dramatically. The proposed method is extended to the case where the control inputs of the subsystems are bounded. The stability analysis shows that the proposed neural network is asymptotically stable. Finally, an example is presented which demonstrates the satisfactory performance of the neural network.