2017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	2017
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

Using Machine Learning to Predict Postprandial Blood Glucose in Type 1 Diabetics

獲獎(jiǎng)作品摘要

People with Type 1 Diabetes must compute an insulin dose for every meal. Dosing errors can cause both acute and chronic complications.? The current dosing method (i.e., "carb counting") considers only the amount of carbohydrates in a meal. This project used machine learning techniques to examine the effects of prior blood glucose dysregulation, exercise, and food composition on postprandial blood glucose levels. Accurate prediction of postprandial blood glucose will enable accurate insulin dosing.?

During a 30-day period, insulin infusion rates, blood glucose levels, and heart rate were continuously measured using an insulin pump, continuous glucose monitor, and a smartwatch. The size and composition of each meal was recorded. Using this data, variables were created to characterize prior blood glucose dysregulation, exercise, and meal composition. The Weka machine learning toolkit was used to train models that used these variables to predict postprandial blood glucose levels.?

The models predicted two-hour postprandial blood glucose levels with a correlation of R = 0.74.? By contrast, carb counting achieved a correlation of only R = 0.35. Combining carb counting with a one-hour postprandial blood glucose measurement achieved R = 0.64.

This project created models for postprandial blood glucose prediction that will enable patients to optimize insulin bolus doses to achieve target postprandial blood glucose levels. This project demonstrated that prior blood glucose dysregulation, exercise, and food composition all have significant effects on postprandial blood glucose levels and that one-hour postprandial blood glucose measurements can enable tight glycemic control.

---

高中生科研 英特爾 Intel ISEF
資訊 · 課程 · 全程指導(dǎo)
請(qǐng)掃碼添加微信好友

[vc_btn title="聯(lián)系電話：(021) 63526628" color="black" align="center" css_animation="fadeIn" link="url:tel%3A02163526628||target:%20_blank|rel:nofollow"]

---

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，開拓創(chuàng)新，長(zhǎng)期專一深入地研究自己感興趣的課題。

>>> 實(shí)用鏈接匯總 <<<

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.