2018英特爾ISEF獲獎(jiǎng)作品摘要 計(jì)算生物與生物信息學(xué) Intel Finalist Abstract Computational Biology and Bioinformatics

獲獎(jiǎng)作品基本信息

年份	2018
學(xué)科	計(jì)算生物與生物信息學(xué) Computational Biology and Bioinformatics
國(guó)家/州	United States of America

獲獎(jiǎng)作品名稱

Designing a Reinforcement Learning Controller for Insulin Delivery in an Artificial Pancreas

獲獎(jiǎng)作品摘要

With the growing prevalence of Type 1 Diabetes (T1D), the complete loss of insulin production among children and adults, the artificial pancreas is becoming increasingly necessary to combat long-term health implications due to extended periods of hyperglycemia. An artificial pancreas—which consists of a constant glucose monitor and an insulin pump controlled by an algorithm that responds to spikes in blood glucose—functions completely hands-free. The key component needed for this device to become an effective treatment option is a sophisticated control algorithm for insulin dosing. With current devices suffering from antiquated PID algorithms, the engineering goal focused on using reinforcement learning (RL), a subset of artificial intelligence (AI), to effectively administer a continuous and responsive dosage of insulin to patients that can be adapted across a varied population. An RL algorithm attempts to maximize “reward” through exploration of the environment and exploitation of learned policies.??

Beginning with glucose-insulin simulations and data from clinical diabetes patients in critical care, the most sensitive parameters for glucose levels were determined to optimize the final AI controller. A cohort of 23 virtual patients was crafted for in-silico validation of the controller, which was designed in MATLAB??. For all patients, the controller maintained a healthy glucose range of 90-130 dg/mL—demonstrating its effectiveness and adaptability—and “l(fā)earned” how to administer both basal and bolus doses of insulin. This research is a successful proof-of-concept of an advanced AI approach to the artificial pancreas and currently no marketed device can administer bolus insulin doses in response to meal intake, which would improve the quality of life for T1D patients.

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ISEF 簡(jiǎn)介

英特爾國(guó)際科學(xué)與工程大獎(jiǎng)賽，簡(jiǎn)稱 "ISEF"，由美國(guó) Society for Science and the Public（科學(xué)和公共服務(wù)協(xié)會(huì)）主辦，英特爾公司冠名贊助，是全球規(guī)模最大、等級(jí)最高的中學(xué)生的科研科創(chuàng)賽事。ISEF 的學(xué)術(shù)活動(dòng)學(xué)科包括了所有數(shù)學(xué)、自然科學(xué)、工程的全部領(lǐng)域和部分社會(huì)科學(xué)。ISEF 素有全球青少年科學(xué)學(xué)術(shù)活動(dòng)的“世界杯”之美譽(yù)，旨在鼓勵(lì)學(xué)生團(tuán)隊(duì)協(xié)作，開(kāi)拓創(chuàng)新，長(zhǎng)期專一深入地研究自己感興趣的課題。

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英特爾 ISEF 學(xué)術(shù)活動(dòng)詳細(xì)介紹

英特爾 ISEF 全程指導(dǎo)方案

· 數(shù)學(xué) · 物理 · 化學(xué) · 生物 · 計(jì)算機(jī) · 工程 ·

學(xué)科簡(jiǎn)介：計(jì)算生物與生物信息學(xué) Computational Biology and Bioinformatics

Studies that primarily focus on the discipline and techniques of computer science and mathematics as they relate to biological systems. This includes the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavior, and social systems.

Subcategories:

Computational Biomodeling?(MOD):?Studies that involve computer simulations of biological systems most commonly with a goal of understanding how cells or organism develop, work collectively and survive.

Computational Epidemiology (EPD):?The study of disease frequency and distribution, and risk factors and socioeconomic determinants of health within populations. Such studies may include gathering information to confirm existence of disease outbreaks, developing case definitions and analyzing epidemic data, establishing disease surveillance, and implementing methods of disease prevention and control.

Computational Evolutionary Biology?(EVO):?A study that applies the discipline and techniques of computer science and mathematics to explore the processes of change in populations of organisms, especially taxonomy, paleontology, ethology, population genetics and ecology.

Computational Neuroscience?(NEU):?A study that applies the discipline and techniques of computer science and mathematics to understand brain function in terms of the information processing properties of the structures that make up the nervous system.

Computational Pharmacology?(PHA):?A study that applies the discipline and techniques of computer science and mathematics to predict and analyze the responses to drugs.

Genomics?(GEN):?The study of the function and structure of genomes using recombinant DNA, sequencing, and bioinformatics.

Other?(OTH):?Studies that cannot be assigned to one of the above subcategories. If the project involves multiple subcategories, the principal subcategory should be chosen instead of Other.

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