The use of expert systems on the differential diagnosis of urinary incontinence

Abstract

RESUMOO diagnóstico diferencial dos tipos de incontinência urinária é algumas vezes difícil de estabelecer. Via de regra, somente os resultados de exames urodinâmicos permitem um diagnóstico acurado. Entretanto, esse exame nem sempre é factível, porque requer equipamento especial e também pessoal treinado para realizar e interpretar o exame. Alguns sistemas especialistas têm sido desenvolvidos para assistir profissionais que atuam nessa área. Propõe-se aqui apresentar a definição de inteligência artificial; explicar o que são sistemas especialistas, sistemas de apoio à decisão e sua aplicação na área da saúde e, discutir alguns sistemas especialistas desenvolvidos para o diagnóstico diferencial da incontinência urinária. Conclui-se que esses sistemas podem ser úteis não somente para o ensino, mas também como apoio à decisão na prá-tica clínica diária. A despeito disso, por vá-rias razões, os profissionais de saúde usualmente hesitam em usar o sistema especialista computacional para dar suporte ao processo de decisão. DESCRITORES ABSTRACTThe differential diagnosis of urinary incontinence classes is sometimes difficult to establish. As a rule, only the results of urodynamic testing allow an accurate diagnosis. However, this exam is not always feasible, because it requires special equipment, and also trained personnel to lead and interpret the exam. Some expert systems have been developed to assist health professionals in this field. Therefore, the aims of this paper are to present the definition of Artificial Intelligence; to explain what Expert System and System for Decision Support are and its application in the field of health and to discuss some expert systems for differential diagnosis of urinary incontinence. It is concluded that expert systems may be useful not only for teaching purposes, but also as decision support in daily clinical practice. Despite this, for several reasons, health professionals usually hesitate to use the computer expert system to support their decision making process. KEY WORDSUrinary incontinence. Women's health. Expert systems. RESUMENEl diagnóstico diferencial de los tipos de incontinencia urinaria es algunas veces difícil. En general, solamente los resultados de exámenes urodinâmicos permiten uno diagnóstico preciso. Entretanto, no es siempre posible hacer ése examen porque requiere equipo especial y personal entrenado hacia realizar y interpretar lo examen. Sistemas especialistas tienen sido hechos hacia asistir los profesionales de salud en ese campo. Propone-se presentar aquí lo que es inteligencia artificial; explicar lo que son sistemas especialistas, sistemas hacia apoyo a la decisión y suya aplicación en el área de la salud y discutir sistemas especialistas hacia el diagnóstico diferencial de la incontinencia. Concluye-se que los sistemas especialistas puedan ser usados no solamente hacia la enseñanza, mas también como apoyo a la decisión en la práctica clí-nica. A pesar de eso, por varias razones, profesionales de salud usualmente resisten en emplear el sistema especialis...

Perfil Sociodemográfico E Clínico De Pacientes Com Incontinência Urinária – Um Estudo De Caso Em Maringa- Pr

“…A presença é confirmada quando há qualquer queixa de perda involuntária de urina (LOPES et al, 2009). No entanto, o diagnóstico que determina o tipo de IU, que o individuo apresenta, pode ser difícil de ser estabelecido em alguns casos.…”

Utilization of Artificial Intelligence for Diagnosis and Management of Urinary Incontinence in Women Residing in Areas with Low Resources: An Overview

“…The Galactica system, although performed well, hasn't been used recently in clinical studies, which suggests that this system would not be of much use in an AI system being deployed in low-resource locations [42]. Lopes (2009) referenced three main categories of AI systems for diagnosis of UI including rule-based logic systems such as ALTURIN.EXP using EXPERTMD and ALTURIN.SDD (sensibility and specificity upper to 98%), genetic algorithm-based systems such as Galactica (mentioned above from Laurikka, 1999), and fuzzy logic-based approved by NANDA (determine diagnosis in total accordance with panel for 79.5% of cases with 1.5% error) [43]. Hung (2019) utilized DeepSurv's deep learning model to predict UI after a robot-assisted radical prostatectomy (RARP) with performance metrics coming from 100 patients, with a mean absolute error of 85.9 and confidence interval of 0.6 [44].…”

Produção científica sobre eliminações urinárias em periódicos de enfermagem brasileiros

“…In the analyzed papers, it is observed that nurses have worked in care delivery to UI patients, seeking clinical information together with the multiprofessional team, offering advice on treatments and service offer, promoting educative actions and performing nursing consultations with a view to a better quality of life (1,(24)(25)(26) . Nursing care in the recovery of urination functions is essential for physical re-establishment and self-esteem.…”

“…Nursing care in the recovery of urination functions is essential for physical re-establishment and self-esteem. The following are indicated; strengthening of pelvic muscles, particularly those muscles with difficulties to start or interrupt urination; bladder retraining through repetition, establishment of routines for urination times, positive reinforcement to patients regarding actions performed and self-catheterization for patients with chronic disorders or bone marrow injury (1,(24)(25)(26) . UI was the most frequent theme in Brazilian publications.…”