Assuntos
Neoplasias das Glândulas Suprarrenais/enfermagem , Neoplasias Hepáticas/enfermagem , Neoplasias Hepáticas/secundário , Transplante de Fígado/enfermagem , Neuroblastoma/enfermagem , Neoplasias das Glândulas Suprarrenais/patologia , Neoplasias das Glândulas Suprarrenais/cirurgia , Pressão Positiva Contínua nas Vias Aéreas , Seguimentos , Rejeição de Enxerto/enfermagem , Rejeição de Enxerto/patologia , Rejeição de Enxerto/cirurgia , Humanos , Lactente , Unidades de Terapia Intensiva Pediátrica , Comunicação Interdisciplinar , Colaboração Intersetorial , Testes de Função Hepática/enfermagem , Neoplasias Hepáticas/patologia , Neoplasias Hepáticas/cirurgia , Masculino , Estadiamento de Neoplasias , Neuroblastoma/patologia , Neuroblastoma/secundário , Neuroblastoma/cirurgia , Equipe de Assistência ao Paciente , Complicações Pós-Operatórias/enfermagem , ReoperaçãoAssuntos
Astenia/enfermagem , Hepatite Crônica/enfermagem , Cirrose Hepática/enfermagem , Astenia/diagnóstico , Fígado Gorduroso/diagnóstico , Fígado Gorduroso/enfermagem , Encefalopatia Hepática/diagnóstico , Encefalopatia Hepática/enfermagem , Hepatite Alcoólica/diagnóstico , Hepatite Alcoólica/enfermagem , Hepatite Crônica/diagnóstico , Humanos , Cirrose Hepática/diagnóstico , Testes de Função Hepática/enfermagem , Diagnóstico de Enfermagem , PrognósticoRESUMO
The liver is the body's largest single discrete organ. It has four major functions: metabolism and synthesis; excretion; storage; and the detoxification of potential poisons. These diverse functions mean that a single test does not give enough information to assess fully how the liver is functioning; at least five different liver function tests are required. This article, part 2 in a four-part series, discusses the information on acute and chronic liver disease that these tests can provide, and how disease affects liver function.
Assuntos
Hepatopatias , Testes de Função Hepática/métodos , Testes de Função Hepática/enfermagem , Fígado/metabolismo , Biomarcadores , Humanos , Hepatopatias/diagnóstico , Hepatopatias/metabolismo , Hepatopatias/enfermagemRESUMO
BACKGROUND: Diagnosing different types of liver diseases clinically is a quite hectic process because patients have to undergo large numbers of independent laboratory tests. On the basis of results and analysis of laboratory test, different liver diseases are classified. Hence to simplify this complex process, we have developed a Rule Base Classification Model (RBCM) to predict different types of liver diseases. The proposed model is the combination of rules and different data mining techniques. OBJECTIVE: The objective of this paper is to propose a rule based classification model with machine learning techniques for the prediction of different types of Liver diseases. METHOD: A dataset was developed with twelve attributes that include the records of 583 patients in which 441 patients were male and rests were female. Support Vector Machine (SVM), Rule Induction (RI), Decision Tree (DT), Naive Bayes (NB) and Artificial Neural Network (ANN) data mining techniques with K-cross fold technique are used with the proposed model for the prediction of liver diseases. The performance of these data mining techniques are evaluated with accuracy, sensitivity, specificity and kappa parameters as well as statistical techniques (ANOVA and Chi square test) are used to analyze the liver disease dataset and independence of attributes. RESULT: Out of 583 patients, 416 patients are liver diseases affected and rests of 167 patients are healthy. The proposed model with decision tree (DT) technique provides the better result among all techniques (RI, SVM, ANN and NB) with all parameters (Accuracy 98.46%, Sensitivity 95.7%, Specificity 95.28% and Kappa 0.983) while the SVM exhibits poor performance (Accuracy 82.33%, Sensitivity 68.03%, Specificity 91.28% and Kappa 0.801). It is also found that the best performance of the model without rules (RI, Accuracy 82.68%, Sensitivity 86.34%, Specificity 90.51% and Kappa 0.619) is almost similar to the worst performance of the rule based classification model (SVM, Accuracy 82.33%, Sensitivity 68.03%, Specificity 91.28% and Kappa 0.801 and the accuracy of chi square test is 76.67%. CONCLUSION: This study demonstrates that there is a significant difference between the proposed rules based classification model and the model without rules for the liver diseases prediction and the rule based classification model with decision tree (DT) technique provides most accurate result. This model can be used as a valuable tool for medical decision making.
Assuntos
Técnicas de Apoio para a Decisão , Hepatopatias/diagnóstico , Adolescente , Adulto , Fatores Etários , Teorema de Bayes , Árvores de Decisões , Feminino , Humanos , Hepatopatias/classificação , Testes de Função Hepática/enfermagem , Masculino , Pessoa de Meia-Idade , Redes Neurais de Computação , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Fatores Sexuais , Adulto JovemRESUMO
Coarctation is a constriction or narrowing of the aorta and presents most commonly within the first two weeks of life. This article reviews a case study of an infant diagnosed with coarctation of the aorta on day 8 of life. It includes an overview of the etiology, clinical presentation, and management plus an account of the infant's transport to a regional pediatric intensive care unit (PICU).