Antibiotic Resistance Determination Utilizing Machine Learning

利用机器学习确定抗生素耐药性

基本信息

批准号：
10663905
负责人：
David Elihu Greenberg
金额：
$ 47.87万
依托单位：
UT SOUTHWESTERN MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-12 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10663905
关键词：
Algorithms Antibiotic Resistance Antibiotic susceptibility Antibiotics Antimicrobial susceptibility Artificial Intelligence Bacteria Bacterial Antibiotic Resistance Bacterial Drug Resistance Bacterial Genome Bacterial Infections Biological Assay Clinical Collection Combined Antibiotics Communicable Diseases Communities Culture Techniques Custom Cystic Fibrosis Data Data Collection Data Science Development Diagnosis Diagnostic Electronic Health Record Ensure Epidemiology Face Feedback Foundations Generations Goals Individual Infrastructure Laboratories Machine Learning Mass Spectrum Analysis Methodology Methods Modeling Molecular Online Systems Organism Output Patient Care Patients Pattern Performance Pharmaceutical Preparations Phenotype Physicians Procedures Process Proteins Public Health Pulmonary Cystic Fibrosis Reproducibility Research Research Personnel Resistance Resistance development Resources Sampling Solid Specimen Speed Techniques Technology Test Result Testing Time Validation Variant Visualization software Work algorithm development antimicrobial chronic infection cloud based cohort community engagement computerized tools cost deep learning deep learning model design effective therapy emerging pathogen empowerment falls genome sequencing genomic data in silico infectious disease treatment informatics tool innovation interest large datasets machine learning algorithm machine learning model machine learning prediction microbial novel online resource open source pathogen pathogenic bacteria phenotypic data prediction algorithm predictive modeling programs prospective resistance mechanism tool tool development transfer learning usability user-friendly web site whole genome

项目摘要

Summary Our long-term objective is to tackle antibiotic resistance by developing accurate and interpretable prediction machine learning models that could be used clinically to speed up the care of patients with bacterial infections. Current approaches to diagnosing bacterial infections rely on first culturing a pathogen from a collected specimen followed by a variety of phenotypic tests to determine what antibiotic a particular bacterial isolate would be sensitive or resistant to. This process can, in many cases, take days to finish. Developing an accurate way to predict antibiotic resistance utilizing whole-genome sequencing data without the need for phenotypic testing is the overall goal of this project. Our team applies the latest advances in deep learning and cloud computation. We will pursue the following Specific Aims: 1) Curate a large dataset and develop a deep-learning prediction model with state-of-the-art accuracies for a wide range of bacterial species and antibiotic combinations; 2) Develop personalized machine learning models for chronic infections; 3) create open-sourced scalable user- friendly resources for the broad research community. The successful completion of this work will provide a paradigm shift in the way we diagnose bacterial infections and speed up the time to providing the correct antibiotic for a specific pathogen.

概括我们的长期目标是通过开发准确且可解释的预测来解决抗生素耐药性问题机器学习模型可用于临床，以加快细菌感染患者的护理速度。目前诊断细菌感染的方法首先依赖于从收集的样本中培养病原体随后进行各种表型测试，以确定特定细菌分离株是什么抗生素敏感或有抵抗力。在许多情况下，此过程可能需要几天时间才能完成。制定准确的方法利用全基因组测序数据预测抗生素耐药性，无需进行表型测试该项目的总体目标。我们的团队应用深度学习和云计算方面的最新进展。我们将追求以下具体目标：1）整理大型数据集并开发深度学习预测针对各种细菌种类和抗生素组合具有最先进精度的模型； 2）开发针对慢性感染的个性化机器学习模型； 3）创建开源可扩展的用户- 为广大研究界提供友好的资源。这项工作的顺利完成将为我们诊断细菌感染的方式发生了范式转变，并加快了提供正确诊断的时间针对特定病原体的抗生素。