Patient Diagnosis System Using Evolutionary Computation

Hospital management and business processes in hospitals have changed importantly over the past twenty years, as did the use of hospital information systems. In order to manage, search, and display patient information more efficiently, we define a patient information package . It is a set of a patients medical information from each visit. By means of patient information package s , both patient - oriented and problem - oriented query strategies, which are most frequently used in daily clinical practice and medical education, can be accommodated. As the symptoms of the patients were entered into the system and the system will run diagnosis system and gives result about the diseases he is suffering from. It also suggests the medicine for the particular di sease from which the patient is suffering to doctor

A Proposal of a Privacy-preserving Questionnaire by Non-deterministic Information and Its Analysis

We focus on a questionnaire consisting of three-choice question or multiple-choice question, and propose a privacy-preserving questionnaire by non-deterministic information. Each respondent usually answers one choice from the multiple choices, and each choice is stored as a tuple in a table data. The organizer of this questionnaire analyzes the table data set, and obtains rules and the tendency. If this table data set contains personal information, the organizer needs to employ the analytical procedures with the privacy-preserving functionality. In this paper, we propose a new framework that each respondent intentionally answers non-deterministic information instead of deterministic information. For example, he answers ‘either A, B, or C’ instead of the actual choice A, and he intentionally dilutes his choice. This may be the similar concept on the k-anonymity. Non-deterministic information will be desirable for preserving each respondent\u27s information. We follow the framework of Rough Non-deterministic Information Analysis (RNIA), and apply RNIA to the privacy-preserving questionnaire by non-deterministic information. In the current data mining algorithms, the tuples with non-deterministic information may be removed based on the data cleaning process. However, RNIA can handle such tuples as well as the tuples with deterministic information. By using RNIA, we can consider new types of privacy-preserving questionnaire.2016 IEEE International Conference on Big Data, December 5-8, 2016, Washington DC, US

The Value of Integrated Information Systems for U.S. General Hospitals

Each year, huge investments into healthcare information systems (HIS) are being made all over the world. Despite the enormous cost for the hospitals, the overall benefits and costs of the healthcare information systems have not been deeply assessed. In recent years, much previous research has investigated the link between the implementation of Information Systems and the performance of organizations. Although the value of Healthcare Information System or Healthcare Information Technology (HIS/HIT) has been found in many studies, some questions remain unclear. Do HIS/HIT systems influence different hospitals the same way? How to understand and explain the mechanism that HIS/HIT improves the performance of hospitals? To address these questions, our research will: 1) Identify the bottlenecks of the current healthcare system which affects the operation efficiency (mismatch between demand and service provided); 2) Adopt the institutional theory to explain the process of implementing HIS/HIT and the possible outcomes; 3) Conduct an empirical study, to expose issues of current healthcare system and the value of the HIS/HIT, and to identify the factors that affect the performance of different hospitals; and 4) Design a decision support system for hospitals. Based on institutional theory, we explain the empirical findings from 2014 HIMSS database. To solve the mismatch between the patient needs and doctor’s schedule, we will propose a business model for a new integrated information management system. It gives the physicians and patients a comprehensive picture needed to understand the type of different patients. A classification schema will be designed to provide recommendations for scheduling decision, and it is supported by the interactive system

Machine learning based data pre-processing for the purpose of medical data mining and decision support

Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. Sometimes, improved data quality is itself the goal of the analysis, usually to improve processes in a production database and the designing of decision support. As medicine moves forward there is a need for sophisticated decision support systems that make use of data mining to support more orthodox knowledge engineering and Health Informatics practice. However, the real-life medical data rarely complies with the requirements of various data mining tools. It is often inconsistent, noisy, containing redundant attributes, in an unsuitable format, containing missing values and imbalanced with regards to the outcome class label.Many real-life data sets are incomplete, with missing values. In medical data mining the problem with missing values has become a challenging issue. In many clinical trials, the medical report pro-forma allow some attributes to be left blank, because they are inappropriate for some class of illness or the person providing the information feels that it is not appropriate to record the values for some attributes. The research reported in this thesis has explored the use of machine learning techniques as missing value imputation methods. The thesis also proposed a new way of imputing missing value by supervised learning. A classifier was used to learn the data patterns from a complete data sub-set and the model was later used to predict the missing values for the full dataset. The proposed machine learning based missing value imputation was applied on the thesis data and the results are compared with traditional Mean/Mode imputation. Experimental results show that all the machine learning methods which we explored outperformed the statistical method (Mean/Mode).The class imbalance problem has been found to hinder the performance of learning systems. In fact, most of the medical datasets are found to be highly imbalance in their class label. The solution to this problem is to reduce the gap between the minority class samples and the majority class samples. Over-sampling can be applied to increase the number of minority class sample to balance the data. The alternative to over-sampling is under-sampling where the size of majority class sample is reduced. The thesis proposed one cluster based under-sampling technique to reduce the gap between the majority and minority samples. Different under-sampling and over-sampling techniques were explored as ways to balance the data. The experimental results show that for the thesis data the new proposed modified cluster based under-sampling technique performed better than other class balancing techniques.In further research it is found that the class imbalance problem not only affects the classification performance but also has an adverse effect on feature selection. The thesis proposed a new framework for feature selection for class imbalanced datasets. The research found that, using the proposed framework the classifier needs less attributes to show high accuracy, and more attributes are needed if the data is highly imbalanced.The research described in the thesis contains the flowing four novel main contributions.a) Improved data mining methodology for mining medical datab) Machine learning based missing value imputation methodc) Cluster Based semi-supervised class balancing methodd) Feature selection framework for class imbalance datasetsThe performance analysis and comparative study show that the use of proposed method of missing value imputation, class balancing and feature selection framework can provide an effective approach to data preparation for building medical decision support

Sistemas Especialistas como ferramenta auxiliar para o ensino da disciplina Bases da Técnica Cirúrgica.

Apresenta-se neste trabalho a contribuição que os sistemas especialistas poderão oferecer aos estudantes de Medicina, em particular aos de Bases da Técnica Cirúrgica, no sentido de se ter uma ferramenta que permita: simulação de situações clínicas as mais diversas; auxilie no processo de tomada de decisões (diagnóstico e terapêutica); sirva para testar conhecimentos de forma interativa: o computador formula perguntas e o aluno as responde (o aluno verificará se o estado meta (solução de um problema) corresponde ao que ele idealizou). Como os fluxogramas de decisão correspondem a uma das formas de representação do conhecimento médico mais amplamente utilizadas, desenvolveu-se um software: ‘O Sistema Gerador de Regras’ (SGR), que permite, a partir do desenho destas estruturas sob forma de grafos valorados, fornecimento de todos os elementos, sobre o formato de um relatório, necessários para construção de sistemas especialistas (variáveis e regras de produção). Para ampliação da contribuição da engenharia do conhecimento na tarefa de elaboração de sistemas especialistas, confeccionou-se, também, uma base de conhecimento que possibilita o diagnóstico de distúrbios hidroeletrolíticos (tópico abordado na disciplina: Bases da Técnica Cirúrgica) que emprega outras formas de representação do conhecimento que não fluxogramas de decisão. Usando o SGR, os sistemas especialistas poderão ser montados por qualquer usuário, que tenha conhecimento de uso de computadores e interfaces gráficas (Windows), a partir de fluxogramas de decisão previamente elaborados, dispensando na maioria das vezes o engenheiro do conhecimento. Para a construção dos sistemas especialista empregou-se o “shell” Expert Sinta, desenvolvido pelo Laboratório de Inteligência Artificial (LIA) da Universidade Federal do Ceará.It comes in this work the contribution that the expert systems can offer to the medical students, in matter the one of Surgical Technique Bases, in the sense of having a tool that allows: simulation in most several clinical situations; to aid in the route of to take decisions (diagnosis and therapeutics); to test knowledge in an interactive way: the computer formulates questions and the student answers them (the student will be verified the state goal (solution of a problem) corresponds that he idealized). As decision’s flowcharts correspond one of the medical knowledge representation’s ways more thoroughly used, it grew a software: ‘Rules Generating System’ (SGR), that allows, starting from the drawing of these structures under form of valued graphs, supply all the elements, on the format of a report, necessary to construct expert systems (variables and production rules). To enlarge the contribution of the knowledge’s engineering in the task of elaborate expert systems, it was made, also, a knowledge base that makes possible diagnosis in hydro electrolytic disturbances (topic approached in the Bases of the Surgica Technique course) that uses other forms of representation of the knowledge that no decision’s flowcharts. Using SGR, the expert systems can be mounted for any user, that has knowledge in the use of computers and graphical interfaces (Windows), starting from decision’s flowcharts previously elaborated, releasing most of the time the knowledge’s engineer. For the expert system construction it was used the Expert Sinta shell, developed by the Laboratory of Artificial intelligence (LIA) of the Ceará Federal University

The effects of centrally acting ACE inhibitors on the rate of cognitive and functional decline in dementia: a KDD approach

Alzheimer’s Disease and other dementias are one of the most challenging illnesses confronting countries with ageing populations. Treatment options for dementia are limited, and the costs are significant. There is a growing need to develop new treatments for dementia, especially for the elderly. There is also growing evidence that centrally acting angiotensin converting enzyme (ACE) inhibitors, which cross the blood-brain barrier, are associated with a reduced rate of cognitive and functional decline in dementia, especially in Alzheimer’s disease (AD). The aim of this research is to investigate the effects of centrally acting ACE inhibitors (CACE-Is) on the rate of cognitive and functional decline in dementia, using a three phased KDD process. KDD, as a scientific way to process and analysis clinical data, is used to find useful insights from a variety of clinical databases. The data used are from three clinic databases: Geriatric Assessment Tool (GAT), the Doxycycline and Rifampin for Alzheimer’s Disease (DARAD), and the Qmci validation databases, which were derived from several different geriatric clinics in Canada. This research involves patients diagnosed with AD, vascular or mixed dementia only. Patients were included if baseline and end-point (at least six months apart) Standardised Mini-Mental State Examination (SMMSE), Quick Mild Cognitive Impairment (Qmci) or Activities Daily Living (ADL) scores were available. Basically, the rates of change are compared between patients taking CACE-Is, and those not currently treated with CACE-Is. The results suggest that there is a statistically significant difference in the rate of decline in cognitive and functional scores between CACE-I and NoCACE-I patients. This research also validates that the Qmci, a new short assessment test, has potential to replace the current popular screening tests for cognition in the clinic and clinical trials