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：
10442982
负责人：
David Elihu Greenberg
金额：
$ 45.92万
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/10442982
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 Lead 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 base chronic infection cloud based cohort community engagement computerized tools cost deep learning deep learning model design effective therapy emerging pathogen 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制定准确且可解释的预测来应对抗生素抗性可以在临床上用于加快细菌感染患者的护理的机器学习模型。当前诊断细菌感染的方法取决于首先从收集的标本中培养病原体然后进行多种表型测试，以确定特定细菌分离株将是哪种抗生素对敏感或抗性。在许多情况下，此过程可能需要几天的时间才能完成。开发一种准确的方法利用全基因组测序数据预测抗生素抗性，而无需表型测试是该项目的总体目标。我们的团队应用了深度学习和云计算方面的最新进展。我们将追求以下特定目标：1）策划一个大数据集并制定深入学习预测具有最新精度的模型，可用于多种细菌和抗生素组合； 2）为慢性感染开发个性化的机器学习模型； 3）创建开源可伸缩的用户 - 广泛研究社区的友好资源。这项工作的成功完成将提供我们诊断细菌感染的方式范式转移，并加快了提供正确的时间特定病原体的抗生素。