Context-sensitive autoassociative memories as expert systems in medical diagnosis

BACKGROUND: The complexity of our contemporary medical practice has impelled the development of different decision-support aids based on artificial intelligence and neural networks. Distributed associative memories are neural network models that fit perfectly well to the vision of cognition emerging from current neurosciences. METHODS: We present the context-dependent autoassociative memory model. The sets of diseases and symptoms are mapped onto a pair of basis of orthogonal vectors. A matrix memory stores the associations between the signs and symptoms, and their corresponding diseases. A minimal numerical example is presented to show how to instruct the memory and how the system works. In order to provide a quick appreciation of the validity of the model and its potential clinical relevance we implemented an application with real data. A memory was trained with published data of neonates with suspected late-onset sepsis in a neonatal intensive care unit (NICU). A set of personal clinical observations was used as a test set to evaluate the capacity of the model to discriminate between septic and non-septic neonates on the basis of clinical and laboratory findings. RESULTS: We show here that matrix memory models with associations modulated by context can perform automatic medical diagnosis. The sequential availability of new information over time makes the system progress in a narrowing process that reduces the range of diagnostic possibilities. At each step the system provides a probabilistic map of the different possible diagnoses to that moment. The system can incorporate the clinical experience, building in that way a representative database of historical data that captures geo-demographical differences between patient populations. The trained model succeeds in diagnosing late-onset sepsis within the test set of infants in the NICU: sensitivity 100%; specificity 80%; percentage of true positives 91%; percentage of true negatives 100%; accuracy (true positives plus true negatives over the totality of patients) 93,3%; and Cohen's kappa index 0,84. CONCLUSION: Context-dependent associative memories can operate as medical expert systems. The model is presented in a simple and tutorial way to encourage straightforward implementations by medical groups. An application with real data, presented as a primary evaluation of the validity and potentiality of the model in medical diagnosis, shows that the model is a highly promising alternative in the development of accuracy diagnostic tools

Sistema reductor del espacio muerto en ventilación asistida de recién nacidos

Tribunal: Profs.Ings. Juan Pablo Oliver, José Luis Díaz Rossello, Gabriel PisciottanoLAVESPI, de la conjunción de "Lavado" y "Espiratorio", fue proyectado y construído para mejorar el desempeño de ventiladores neonatales al añadirle la capacidad de lavar el espacio muerto instrumental inyectando un pequeño flujo durante la espiración, técnica conocida como "Insuflación de Gas Transtraqueal" (TGI). El diseño de LAVESPI consiste en un equipo microprocesado con servocontrol de válvulas, sensores y una bomba de gas que incluye la idea novedosa de usar el mismo gas de ventilación durante la fase espiratoria a través de una sonda endotraqueal especial (multicanales incorporados en su pared). Para evitar exceder límites de presión que pudieran perjudicar las frágiles estructuras alveolares LAVESPI controla la presión en vía aérea y suspende la insuflación llegado el caso. LAVESPI puede añadirse a todos los ventiladores independientemente de sus marcas y modelos sin alterar su funcionamiento . LAVESPI incluye sondas especiales con 8 ductos ubicados en su pared (por ejemplo Vygon 6501.30). Las pruebas de LAVESPI evidenciaron una reducción promedio del dióxido de carbono en sangre arterial . (PaCO2) del 19% (de 61.7 mmHg ( = 7;7) a 49.7 mmHg (σ = 5;7)) en un grupo de 5 cerdos recién nacidos con pulmón sano y del 21% (de 61.2 mmHg (σ = 11;1) a 48.5 mmHg (σ = 5;7)) en un grupo de 2 cerdos recién nacidos con pulmón injuriado (media de peso de los 7 cerdos de 1641 g). LAVESPI es un prototipo de costo en componentes de USD 900 que tieneLAVESPI es un prototipo de costo en componentes de USD 900 que tiene potencial de aplicación en los centros de medicina intensiva neonatal, sin descartar su posible adaptación para pacientes adultos