Gramáticas evolutivas para la predicción de hipoglucemias en diabetes

Trabajo de Fin de Grado en Ingeniería Informática, Facultad de Informática UCM, Departamento Ingeniería del Software e Inteligencia Artificial, Curso 2021/2022. https://github.com/ABSysGroup/jeco/tree/TFG_GE4HYPODiabetic patients have to manage their blood sugar correctly to prevent complications. One such complication is hypoglycemia or low blood sugar, which occurs when the blood glucose concentration goes below a certain threshold. A hypoglycemic episode needs to be rectified before it becomes harmful and can be a very distressing situation for the patient. The main goal of this study is to program Structured Grammatical Evolution and Dynamic Structured Grammatical Evolution algorithms and use them to generate models for the prediction of hypoglycemic episodes in patients with diabetes. The algorithms will be used to obtain a white-box model made up of if-then-else statements that given some input data, comprised of the blood glucose and exercise readings of the patients from the previous 2 hours, optimizes a logical relation between these variables. The resulting formula will be able to determine if the patient is going to have a hypoglycemic episode in a 30, 60, 90 and 120 minutes prediction horizon.Los pacientes con diabetes deben controlar correctamente su nivel de azúcar en sangre para evitar complicaciones. Una de estas complicaciones es la hipoglucemia o nivel bajo de azúcar en sangre, que ocurre cuando la concentración de glucosa en la sangre cae por debajo de cierto umbral. Un episodio de hipoglucemia debe corregirse antes de que se vuelva dañino y puede ser una situación muy angustiosa para el paciente. El objetivo principal de este estudio es programar los algoritmos de Gramáticas Evolutivas Estructuradas y Gramáticas Evolutivas Estructuradas Dinámicas, y utilizarlos para generar modelos de predicción de episodios hipoglucémicos en pacientes con diabetes. Los algoritmos se utilizarán para obtener un modelo de caja blanca formado por sentencias if-then-else que, dados unos datos de entrada, compuestos por el nivel de la glucosa en sangre y las lecturas de datos de ejercicio de los pacientes de las 2 horas anteriores, optimiza una relación lógica entre estas variables. La fórmula resultante se usa para determinar si el paciente va a tener un episodio de hipoglucemia en un plazo de 30, 60, 90 y 120 minutos.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Learning Difference Equations with Structured Grammatical Evolution for Postprandial Glycaemia Prediction

People with diabetes must carefully monitor their blood glucose levels, especially after eating. Blood glucose regulation requires a proper combination of food intake and insulin boluses. Glucose prediction is vital to avoid dangerous post-meal complications in treating individuals with diabetes. Although traditional methods, such as artificial neural networks, have shown high accuracy rates, sometimes they are not suitable for developing personalised treatments by physicians due to their lack of interpretability. In this study, we propose a novel glucose prediction method emphasising interpretability: Interpretable Sparse Identification by Grammatical Evolution. Combined with a previous clustering stage, our approach provides finite difference equations to predict postprandial glucose levels up to two hours after meals. We divide the dataset into four-hour segments and perform clustering based on blood glucose values for the twohour window before the meal. Prediction models are trained for each cluster for the two-hour windows after meals, allowing predictions in 15-minute steps, yielding up to eight predictions at different time horizons. Prediction safety was evaluated based on Parkes Error Grid regions. Our technique produces safe predictions through explainable expressions, avoiding zones D (0.2% average) and E (0%) and reducing predictions on zone C (6.2%). In addition, our proposal has slightly better accuracy than other techniques, including sparse identification of non-linear dynamics and artificial neural networks. The results demonstrate that our proposal provides interpretable solutions without sacrificing prediction accuracy, offering a promising approach to glucose prediction in diabetes management that balances accuracy, interpretability, and computational efficiency

Blood Glucose Forecasting using LSTM Variants under the Context of Open Source Artificial Pancreas System

High accuracy of blood glucose prediction over the long term is essential for preventative diabetes management. The emerging closed-loop insulin delivery system such as the artificial pancreas system (APS) provides opportunities for improved glycaemic control for patients with type 1 diabetes. Existing blood glucose studies are proven effective only within 30 minutes but the accuracy deteriorates drastically when the prediction horizon increases to 45 minutes and 60 minutes. Deep learning, especially for long short term memory (LSTM) and its variants have recently been applied in various areas to achieve state-of-the-art results in tasks with complex time series data. In this study, we present deep LSTM based models that are capable of forecasting long term blood glucose levels with improved prediction and clinical accuracy. We evaluate our approach using 20 cases(878,000 glucose values) from Open Source Artificial Pancreas System (OpenAPS). On 30-minutes and 45-minutes prediction, our Stacked-LSTM achieved the best performance with Root-Mean-Square-Error (RMSE) marks 11.96 & 15.81 and Clark-Grid-ZoneA marks 0.887 & 0.784. In terms of 60-minutes prediction, our ConvLSTM has the best performance with RMSE = 19.6 and Clark-Grid-ZoneA=0.714. Our models outperform existing methods in both prediction and clinical accuracy. This research can hopefully support patients with type 1 diabetes to better manage their behavior in a more preventative way and can be used in future real APS context

A Proposed Method to Identify the Occurrence of Diabetes in Human Body using Machine Learning Technique

Advanced machine-learning techniques are often used for reasoning-based diagnosis and advanced prediction system within the healthcare industry. The methods and algorithms are based on the historical clinical data and factbased Medicare evaluation. Diabetes is a global problem. Each year people are developing diabetes and due to diabetes, a lot of people are going for organ amputation. According to the World Health Organization (WHO), there is a sharp rise in number of people developing diabetes. In 1980, it was estimated that 180 million people with diabetes worldwide. This number has risen from 108 million to 422 million in 2014. WHO also reported that 1.6 million deaths in 2016 due to diabetes. Diabetes occurs due to insufficient production of insulin from pancreas. Several research show that unhealthy diet, smoking, less exercise, Body Mass Index (BMI) are the primary cause of diabetes. This paper shows the use of machine learning that can identify a patient of being diabetic or non-diabetic based on previous clinical data. In this article, a method is shown to analyze and compare the relationship between different clinical parameters such as age, BMI, Diet-chart, systolic Blood Pressure etc. After evaluating all the factors this research work successfully combined all the related factors in a single mathematical equation which is very effective to analyze the risk percentage and risk evaluation based on given input parameters by the participants or users

Chatbot assistant for diabetic patients

Dissertação de mestrado em Informatics EngineeringNowadays, with the existence of several chronic health conditions, Diabetes Mellitus (DM) being one of the main ones, there is a great burden that patients must have in order to be able to take care of themselves. Thus, in addition to seeking to resolve their needs by obtaining information from health professionals, they increasingly seek information and advices in forums, communities and groups. The use of dialogue systems has become essential in people’s lives. The development of conversational agents is still an unresolved research problem that poses many challenges in the Artificial Intelligence (AI) community. This work aims to build an AI chatbot that is based on the principles and techniques of AI directed to Natural Language Processing (NLP) and Deep Learning to help people newly diagnosed with DM in the self management of the disease. A literature search of DM education and information for people newly diagnosed with DM was con ducted. Additionally, the main topics in which patients ask for support were retrieved from a search of several online support groups of DM, as well as questionnaires with 8 patients and interviews with 3 health professionals. The application were developed through the back-end side in Python and the front-end side in React Native and its communication was made through WebSockets. Furthermore, an interaction and interface design are developed in this work using Human-Centered Design (HCD) methodology. For that purpose, iterative usability test sessions were conducted with 12 users using Think Aloud methods and the System Usability Scale (SUS). The chatbot developed is Information Retrieval (IR) type and answered questions asked by users in a human-like way. The result of the usability tests of the final version of the application was satisfactory (with a System Usability Scale (SUS) score of 88) and users found the application quite intuitive and robust. Further studies should concentrate on customizing the chatbot to each user by collecting information from prior interactions and verifying the impact of using this chatbot for newly diagnosed Portuguese users with DM.Atualmente, com a existência de várias condições crónicas de saúde sendo uma das principais a Diabetes Mellitus (DM), há um grande fardo que os pacientes devem ter para poderem cuidar de si mesmos. Assim, para além dos pacientes buscarem procurar resolver as suas necessidades por meio da obtenção de informações junto aos profissionais de saúde, cada vez mais buscam informações e conselhos em fóruns, comunidades e grupos. O uso de sistemas de diálogo tornou-se essencial na vida das pessoas. O desenvolvimento de agentes conversacionais é ainda um problema de pesquisa não resolvido que apresenta muitos desafios na comunidade da Inteligência Artificial (IA). Este trabalho visa construir um IA chatbot que é baseado nos princípios e técnicas de Inteligência Artificial (IA) direcionado a Processamento de Linguagem Natural (PLN) e Aprendizagem Profunda para ajudar pessoas recém-diagnosticadas com DM no autocuidado desta doença. Neste trabalho, foi acompanhada uma pesquisa bibliográfica sobre a educação e informações da DM para pessoas recém-diagnosticadas com DM. Além disso, foram abordados em vários grupos de apoio online relacionados com a DM os principais tópicos que os pacientes pedem apoio, a utilização de um questionário com 8 pacientes e entrevistas com 3 profissionais de saúde. A aplicação foi desenvolvida através do back-end em Python e front-end em React Native e a sua comunicação foi feita através de WebSockets. Foi também desenvolvido um design de interação e interface através da metodologia Human-Centered Design (HCD). Para tal, foram realizadas sessões de testes iterativos de usabilidade com 12 participantes seguindo os métodos Think Aloud e System Usability Scale (SUS). O chatbot desenvolvido é do tipo Information Retrieval (IR) e responde às perguntas feitas pelos utilizadores de forma humana. O resultado dos testes de usabilidade da versão final da aplicação foram satisfatórios (SUS de 88) e os utilizadores acharam a aplicação bastante intuitiva e robusta. Os estudos futuros devem concentrar-se na personalização do chatbot para cada utilizador, com a coleção de informações e de interações anteriores e na verificação do impacto da utilização deste chatbot para utilizadores portugueses recém-diagnosticados com DM

Machine Learning Methods with Noisy, Incomplete or Small Datasets

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios