Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network

A deep learning network was used to predict future blood glucose levels, as this can permit diabetes patients to take action before imminent hyperglycaemia and hypoglycaemia. A sequential model with one long-short-term memory (LSTM) layer, one bidirectional LSTM layer and several fully connected layers was used to predict blood glucose levels for different prediction horizons. The method was trained and tested on 26 datasets from 20 real patients. The proposed network outperforms the baseline methods in terms of all evaluation criteria.Comment: 5 pages, submitted to 2018 14th Symposium on Neural Networks and Applications (NEUREL

STOCHASTIC SEASONAL MODELS FOR GLUCOSE PREDICTION IN TYPE 1 DIABETES

[ES] La diabetes es un importante problema de salud mundial, siendo una de las enfermedades no transmisibles más graves después de las enfermedades cardiovasculares, el cáncer y las enfermedades respiratorias crónicas. La prevalencia de la diabetes ha aumentado constantemente en las últimas décadas, especialmente en países de ingresos bajos y medios. Se estima que 425 millones de personas en todo el mundo tenían diabetes en 2017, y para 2045 este número puede aumentar a 629 millones. Alrededor del 10% de las personas con diabetes padecen diabetes tipo 1, caracterizada por una destrucción autoinmune de las células beta en el páncreas, responsables de la secreción de la hormona insulina. Sin insulina, la glucosa plasmática aumenta a niveles nocivos, provocando complicaciones vasculares a largo plazo. Hasta que se encuentre una cura, el manejo de la diabetes depende de los avances tecnológicos para terapias de reemplazo de insulina. Con la llegada de los monitores continuos de glucosa, la tecnología ha evolucionado hacia sistemas automatizados. Acuñados como "páncreas artificial", los dispositivos de control de glucosa en lazo cerrado suponen hoy en día un cambio de juego en el manejo de la diabetes. La investigación en las últimas décadas ha sido intensa, dando lugar al primer sistema comercial a fines de 2017, y muchos más están siendo desarrollados por las principales industrias de dispositivos médicos. Sin embargo, como dispositivo de primera generación, muchos problemas aún permanecen abiertos y nuevos avances tecnológicos conducirán a mejoras del sistema para obtener mejores resultados de control glucémico y reducir la carga del paciente, mejorando significativamente la calidad de vida de las personas con diabetes tipo 1. En el centro de cualquier sistema de páncreas artificial se encuentra la predicción de glucosa, tema abordado en esta tesis. La capacidad de predecir la glucosa a lo largo de un horizonte de predicción dado, y la estimación de las tendencias futuras de glucosa, es la característica más importante de cualquier sistema de páncreas artificial, para poder tomar medidas preventivas que eviten por completo el riesgo para el paciente. La predicción de glucosa puede aparecer como parte del algoritmo de control en sí, como en sistemas basados en técnicas de control predictivo basado en modelo (MPC), o como parte de un sistema de supervisión para evitar episodios de hipoglucemia. Sin embargo, predecir la glucosa es un problema muy desafiante debido a la gran variabilidad inter e intra-sujeto que sufren los pacientes, cuyas fuentes solo se entienden parcialmente. Esto limita las prestaciones predictivas de los modelos, imponiendo horizontes de predicción relativamente cortos, independientemente de la técnica de modelado utilizada (modelos fisiológicos, basados en datos o híbridos). La hipótesis de partida de esta tesis es que la complejidad de la dinámica de la glucosa requiere la capacidad de caracterizar grupos de comportamientos en los datos históricos del paciente que llevan naturalmente al concepto de modelado local. Además, la similitud de las respuestas en un grupo puede aprovecharse aún más para introducir el concepto clásico de estacionalidad en la predicción de glucosa. Como resultado, los modelos locales estacionales están en el centro de esta tesis. Se utilizan varias bases de datos clínicas que incluyen comidas mixtas y ejercicio para demostrar la viabilidad y superioridad de las prestaciones de este enfoque.[CA] La diabetisés un important problema de salut mundial, sent una de les malalties no transmissibles més greus després de les malalties cardiovasculars, el càncer i les malalties respiratòries cròniques. La prevalença de la diabetis ha augmentat constantment en les últimes dècades, especialment en països d'ingressos baixos i mitjans. S'estima que 425 milions de persones a tot el món tenien diabetis en 2017, i per 2045 aquest nombre pot augmentar a 629 milions. Al voltant del 10% de les persones amb diabetis pateixen diabetis tipus 1, caracteritzada per una destrucció autoimmune de les cèl·lules beta en el pàncrees, responsables de la secreció de l'hormona insulina. Sense insulina, la glucosa plasmàtica augmenta a nivells nocius, provocant complicacions vasculars a llarg termini. Fins que es trobi una cura, el maneig de la diabetis depén dels avenços tecnològics per a teràpies de reemplaçament d'insulina. Amb l'arribada dels monitors continus de glucosa, la tecnologia ha evolucionat cap a sistemes automatitzats. Encunyats com "pàncrees artificial", els dispositius de control de glucosa en llaç tancat suposen avui dia un canvi de joc en el maneig de la diabetis. La investigació en les últimes dècades ha estat intensa, donant lloc al primer sistema comercial a finals de 2017, i molts més estan sent desenvolupats per les principals indústries de dispositius mèdics. No obstant això, com a dispositiu de primera generació, molts problemes encara romanen oberts i nous avenços tecnològics conduiran a millores del sistema per obtenir millors resultats de control glucèmic i reduir la càrrega del pacient, millorant significativament la qualitat de vida de les persones amb diabetis tipus 1. Al centre de qualsevol sistema de pàncrees artificial es troba la predicció de glucosa, tema abordat en aquesta tesi. La capacitat de predir la glucosa al llarg d'un horitzó de predicció donat, i l'estimació de les tendències futures de glucosa, és la característica més important de qualsevol sistema de pàncrees artificial, per poder prendre mesures preventives que evitin completament el risc per el pacient. La predicció de glucosa pot aparèixer com a part de l'algoritme de control en si, com en sistemes basats en técniques de control predictiu basat en model (MPC), o com a part d'un sistema de supervisió per evitar episodis d'hipoglucèmia. No obstant això, predir la glucosa és un problema molt desafiant degut a la gran variabilitat inter i intra-subjecte que pateixen els pacients, les fonts només s'entenen parcialment. Això limita les prestacions predictives dels models, imposant horitzons de predicció relativament curts, independentment de la tècnica de modelatge utilitzada (models fisiològics, basats en dades o híbrids). La hipòtesi de partida d'aquesta tesi és que la complexitat de la dinàmica de la glucosa requereix la capacitat de caracteritzar grups de comportaments en les dades històriques del pacient que porten naturalment al concepte de modelatge local. A més, la similitud de les respostes en un grup pot aprofitar-se encara més per introduir el concepte clàssic d'estacionalitat en la predicció de glucosa. Com a resultat, els models locals estacionals estan al centre d'aquesta tesi. S'utilitzen diverses bases de dades clíniques que inclouen menjars mixtes i exercici per demostrar la viabilitat i superioritat de les prestacions d'aquest enfocament.[EN] Diabetes is a significant global health problem, one of the most serious noncommunicable diseases after cardiovascular diseases, cancer and chronic respiratory diseases. Diabetes prevalence has been steadily increasing over the past decades, especially in low- and middle-income countries. It is estimated that 425 million people worldwide had diabetes in 2017, and by 2045 this number may rise to 629 million. About 10% of people with diabetes suffer from type 1 diabetes, characterized by autoimmune destruction of the beta-cells in the pancreas, responsible for the secretion of the hormone insulin. Without insulin, plasma glucose rises to deleterious levels, provoking long-term vascular complications. Until a cure is found, the management of diabetes relies on technological developments for insulin replacement therapies. With the advent of continuous glucose monitors, technology has been evolving towards automated systems. Coined as "artificial pancreas", closed-loop glucose control devices are nowadays a game-changer in diabetes management. Research in the last decades has been intense, yielding a first commercial system in late 2017 and many more are in the pipeline of the main medical devices industry. However, as a first-generation device, many issues still remain open and new technological advancements will lead to system improvements for better glycemic control outputs and reduced patient's burden, improving significantly the quality of life of people with type 1 diabetes. At the core of any artificial pancreas system is glucose prediction, the topic addressed in this thesis. The ability to predict glucose along a given prediction horizon, and estimation of future glucose trends, is the most important feature of any artificial pancreas system, in order to be able to take preventive actions to entirely avoid risk to the patient. Glucose prediction can appear as part of the control algorithm itself, such as in systems based on model predictive control (MPC) techniques, or as part of a monitoring system to avoid hypoglycemic episodes. However, predicting glucose is a very challenging problem due to the large inter- and intra-subject variability that patients suffer, whose sources are only partially understood. These limits models forecasting performance, imposing relatively short prediction horizons, despite the modeling technique used (physiological, data-driven or hybrid approaches). The starting hypothesis of this thesis is that the complexity of glucose dynamics requires the ability to characterize clusters of behaviors in the patient's historical data naturally yielding to the concept of local modeling. Besides, the similarity of responses in a cluster can be further exploited to introduce the classical concept of seasonality into glucose prediction. As a result, seasonal local models are at the core of this thesis. Several clinical databases including mixed meals and exercise are used to demonstrate the feasibility and superiority of the performance of this approach.This work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under the FPI grant BES-2014-069253 and projects DPI2013-46982-C2-1-R and DPI2016-78831-C2-1-R. Moreover, with relation to this grant, a short stay was done at the end of 2017 at the Illinois Institute of Technology, Chicago, United States of America, under the supervision of Prof. Ali Cinar, for four months from 01/09/2017 to 29/12/2017.Montaser Roushdi Ali, E. (2020). STOCHASTIC SEASONAL MODELS FOR GLUCOSE PREDICTION IN TYPE 1 DIABETES [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/136574TESI

A machine-learning approach to predict postprandial hypoglycemia

Background For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. Methods We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. Results In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. Conclusion In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.11Ysciescopu

A Simple Modeling Framework For Prediction In The Human Glucose-Insulin System

In this paper, we build a new, simple, and interpretable mathematical model to describe the human glucose-insulin system. Our ultimate goal is the robust control of the blood glucose (BG) level of individuals to a desired healthy range, by means of adjusting the amount of nutrition and/or external insulin appropriately. By constructing a simple yet flexible model class, with interpretable parameters, this general model can be specialized to work in different settings, such as type 2 diabetes mellitus (T2DM) and intensive care unit (ICU); different choices of appropriate model functions describing uptake of nutrition and removal of glucose differentiate between the models. In both cases, the available data is sparse and collected in clinical settings, major factors that have constrained our model choice to the simple form adopted. The model has the form of a linear stochastic differential equation (SDE) to describe the evolution of the BG level. The model includes a term quantifying glucose removal from the bloodstream through the regulation system of the human body, and another two terms representing the effect of nutrition and externally delivered insulin. The parameters entering the equation must be learned in a patient-specific fashion, leading to personalized models. We present numerical results on patient-specific parameter estimation and future BG level forecasting in T2DM and ICU settings. The resulting model leads to the prediction of the BG level as an expected value accompanied by a band around this value which accounts for uncertainties in the prediction. Such predictions, then, have the potential for use as part of control systems which are robust to model imperfections and noisy data. Finally, a comparison of the predictive capability of the model with two different models specifically built for T2DM and ICU contexts is also performed.Comment: 47 pages, 9 figures, 7 table

Prediction of Adverse Glycemic Events from Continuous Glucose Monitoring Signal

The most important objective of any diabetes therapy is to maintain the blood glucose concentration within the euglycemic range, avoiding or at least mitigating critical hypo/hyperglycemic episodes. Modern continuous glucose monitoring (CGM) devices bear the promise of providing the patients with an increased and timely awareness of glycemic conditions as these get dangerously near to hypo/hyperglycemia. The challenge is to detect, with reasonable advance, the patterns leading to risky situations, allowing the patient to make therapeutic decisions on the basis of future (predicted) glucose concentration levels. We underline that a technically sound performance comparison of the approaches proposed in recent years has yet to be done, thus it is unclear which one is preferred. The aim of this study is to fill this gap by carrying out a comparative analysis among the most common methods for glucose event prediction. Both regression and classification algorithms have been implemented and analyzed, including static and dynamic training approaches. The dataset consists of 89 CGM time series measured in diabetic subjects for 7 subsequent days. Performance metrics, specifically defined to assess and compare the event-prediction capabilities of the methods, have been introduced and analyzed. Our numerical results show that a static training approach exhibits better performance, in particular when regression methods are considered. However, classifiers show some improvement when trained for a specific event category, such as hyperglycemia, achieving performance comparable to the regressors, with the advantage of predicting the events sooner. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Analisis model Autoregressive Integrated Moving Average (ARIMA) Box-Jenkins pada penderita diabetes

INDONESIA: Diabetes mellitus adalah kondisi pasien yang mengeluarkan urin dalam jumlah besar dengan kadar gula yang tinggi. Diabetes mellitus adalah penyakit kronis dimana pankreas tidak dapat memproduksi insulin untuk mengatur glukosa darah. Disfungsi ini dapat menyebabkan hipoglikemia (glukosa darah rendah) dan hiperglikemia (gula darah tinggi). Salah satu metode peramalan adalah ARIMA Box-Jenkins. Peramalan metode ARIMA ini bisa digunakan untuk meramalkan penderita Diabetes di Rumah Sakit Islam Gondanglegi. Tujuan dari penelitian ini adalah untuk mengetahui hasil analisis model ARIMA (autoregressive integrated moving average) Box-Jenkins pada penderita diabetes di Rumah Sakit Islam Gondanglegi. Peramalan merupakan upaya untuk meramalkan kondisi masa depan dengan menggunakan pengujian di masa lalu. ARIMA adalah singkatan dari autoregressive integrated moving average yang dibagi menjadi 3 model yaitu AR (Autoregressive), MA (Moving Average), dan ARMA (Autoregressive - Moving Average). Dengan menggunakan metode ini model parameter yang didapatkan adalah model ARIMA (1, 0, 2). Dengan parameter AR (1) sebesar 0,942, parameter MA (1) sebesar 0,528, dan parameter MA (2) sebesar 0,437. Dari hasil model tersebut dihasilkan ramalan penderita Diabetes di Rumah Sakit Islam Gondanglegi untuk periode yang akan datang selama 12 periode yaitu dari Januari 2021 – Desember 2021 dengan hasil yang cenderung konstan yaitu sebanyak 448 pasien pada bulan Januari 2021 sampai Oktober 2021 dan 449 pasien pada bulan November 2021 sampai Desember 2021. Dengan kesalahan (error) menggunakan MAPE (Mean Absolute Percentage Error) pada hasil peramalan data penderita Diabetes sebesar 22,47%. Oleh karena itu dapat disimpulkan bahwa data tersebut sesuai untuk digunakan Model ARIMA yang telah dipilih. Karena hasil peramalan yang diperoleh dari nilai MAPE tersebut cukup baik.   ENGLISH: Diabetes mellitus is a condition of patients who excrete large amounts of urine with high sugar levels. Diabetes mellitus is a chronic disease in which insulin cannot be produced to regulate blood glucose. This dysfunction can lead to hypoglycemia (low blood glucose) and hyperglycemia (high blood sugar). One of the forecasting methods is ARIMA Box-Jenkins. This ARIMA forecasting method can be used to predict diabetes sufferers at the Gondanglegi Islamic Hospital. The purpose of this study was to determine the results of the Box-Jenkins ARIMA (autoregressive integrated moving average) model analysis in diabetics at the Gondanglegi Islamic Hospital. Forecasting is an attempt to predict future conditions by using past tests. ARIMA stands for autoregressive integrated moving average which is divided into 3 models, namely AR (Autoregressive), MA (Moving Average), and ARMA (Autoregressive - Moving Average). By using the parameter method of this model, what is obtained is the ARIMA model (1, 0, 2). With AR parameter (1) of 0,942, MA parameter (1) of 0,528, and MA parameter (2) of 0,437. From the results of the model, predictions of diabetes sufferers at the Gondanglegi Islamic Hospital for the future period will be for 12 periods, namely from January 2021 - December 2021 with results that tend to be constant, namely as many as 448 patients in January 2021 to October 2021 and 449 patients in November 2021 to December 2021. With an error using MAPE (Mean Absolute Percentage Error) on the results of forecasting data for Diabetes sufferers of 22,47%. Therefore, it can be said that the data is suitable for use with the ARIMA model that has been selected. Because the forecasting results obtained from the MAPE value are quite good.   ARABIC: داء السكري هو حالة المرضى الذين يفرزون كميات كبيرة من البول مع ارتفاع مستويات السكر. مرض السكري هو مرض مزمن لا يمكن فيه إنتاج الأنسولين لتنظيم جلوكوز الدم. يمكن أن يؤدي هذا الخلل الوظيفي إلى نقص السكر في الدم (انخفاض نسبة السكر في الدم) وارتفاع السكر في الدم (ارتفاع نسبة السكر في الدم). إحدى طرق التنبؤ هي (ARIMA Box-Jenkins). يمكن استخدام طريقة التنبؤ ARIMA للتنبؤ بمرضى السكري في مستشفى Gondanglegi الإسلامي. كان الغرض من هذه الدراسة هو تحديد نتائج تحليل نموذج ARIMA (Autoregressive Integrated Moving Average) Box-Jenkins في مرضى السكري في مستشفى Gondanglegi الإسلامي. التنبؤ هو محاولة للتنبؤ بالظروف المستقبلية باستخدام الاختبارات السابقة. يرمز ARIMA إلى المتوسط المتحرك المتكامل الانحدار الذاتي والذي ينقسم إلى ۳ نماذج ، وهي AR (الانحدار التلقائي) و MA (المتوسط المتحرك) و ARMA (الانحدار التلقائي - المتوسط المتحرك). باستخدام طريقة المعلمة لهذا النموذج ، ما يتم الحصول عليه هو نموذج (۲,۰,۱) ARIMA. مع معامل (۱)AR من ۹٤۲,۰، ومعامل(۱)MA ٥٢٨,۰، ومعامل (۲) MA من ٤۳۷,۰. من نتائج النموذج ، ستكون توقعات مرضى السكري في مستشفى جوندانجليجي الإسلامي للفترة المقبلة لمدة ۱۲ فترة ، أي من يناير ۲۰۲۱ - ديسمبر ۲۰۲۱ مع نتائج تميل إلى أن تكون ثابتة ، أي ما يصل إلى 448 مريضًا في يناير ٢۰٢۱ حتى أكتوبر ٢۰٢۱ و ٤٤٩ مريضًا في نوفمبر من ٢۰٢۱ إلى ديسمبر ٢۰٢۱. مع وجود خطأ باستخدام MAPE (متوسط الخطأ النسبي المطلق) على نتائج بيانات التنبؤ لمرضى السكري بنسبة% ٤۷,۲۲. لذلك ، يمكن القول أن البيانات مناسبة للاستخدام مع نموذج ARIMA الذي تم اختياره. لأن نتائج التنبؤ التي تم الحصول عليها من قيمة MAPE جيدة جدًا

Artificial Intelligence for Data Analysis and Signal Processing

Artificial intelligence, or AI, currently encompasses a huge variety of fields, from areas such as logical reasoning and perception, to specific tasks such as game playing, language processing, theorem proving, and diagnosing diseases. It is clear that systems with human-level intelligence (or even better) would have a huge impact on our everyday lives and on the future course of evolution, as it is already happening in many ways. In this research AI techniques have been introduced and applied in several clinical and real world scenarios, with particular focus on deep learning methods. A human gait identification system based on the analysis of inertial signals has been developed, leading to misclassification rates smaller than 0.15%. Advanced deep learning architectures have been also investigated to tackle the problem of atrial fibrillation detection from short length and noisy electrocardiographic signals. The results show a clear improvement provided by representation learning over a knowledge-based approach. Another important clinical challenge, both for the patient and on-board automatic alarm systems, is to detect with reasonable advance the patterns leading to risky situations, allowing the patient to take therapeutic decisions on the basis of future instead of current information. This problem has been specifically addressed for the prediction of critical hypo/hyperglycemic episodes from continuous glucose monitoring devices, carrying out a comparative analysis among the most successful methods for glucose event prediction. This dissertation also shows evidence of the benefits of learning algorithms for vehicular traffic anomaly detection, through the use of a statistical Bayesian framework, and for the optimization of video streaming user experience, implementing an intelligent adaptation engine for video streaming clients. The proposed solution explores the promising field of deep learning methods integrated with reinforcement learning schema, showing its benefits against other state of the art approaches. The great knowledge transfer capability of artificial intelligence methods and the benefits of representation learning systems stand out from this research, representing the common thread among all the presented research fields