Antibiotic Resistance Microbiology Dataset (ARMD): A de-identified resource for studying antimicrobial resistance using electronic health records

The Antibiotic Resistance Microbiology Dataset (ARMD) is a structured and de-identified resource developed using electronic health records (EHR) from Stanford Healthcare. It provides a comprehensive overview of microbiological cultures including urine, respiratory, and blood cultures. This dataset includes 283,715 unique adult patients and features detailed information on culture results, identified organisms, antibiotic susceptibility, and associated demographic and clinical data. The dataset was meticulously constructed through a multi-step process designed to enhance data quality and relevance. By enabling the study of antimicrobial resistance patterns and supporting antimicrobial stewardship efforts, ARMD offers a valuable resource for researchers and clinicians seeking to improve the management of infectious diseases and combat the growing threat of antimicrobial resistance., Cohort Selection
The ARMD was created using de-identified EHR data from Stanford Healthcare to address this need. This dataset provides microbiological cultures from adult patients (≥18 years old) and includes key clinical data points relevant to studying antimicrobial resistance. The cohort construction involved the following features and processes:

Culture Types: Microbiological cultures were included, specifically urine, respiratory, and blood cultures.

Temporal Adjustment: The timing of culture orders was adjusted for data privacy through jittering, ensuring patient confidentiality while retaining meaningful temporal relationships.

Culture Positivity: Each culture is flagged as either positive or negative, indicating whether an organism was identified. Cultures flagged as negative are represented by a null value in the susceptibility field.

Organism Identification and Susceptibility: For positive cultures, the identified organism and its antibiotic susceptibility are recorde..., , ## Antibiotic Resistance Microbiology Dataset (ARMD): A de-identified resource for studying antimicrobial resistance using electronic health records

## Background

Antimicrobial resistance (AMR) represents a pressing global health challenge, exacerbated by the overuse and misuse of antibiotics. Efforts to mitigate AMR require high-quality datasets to analyze trends in microbial susceptibility, guide clinical decision-making, and inform stewardship programs. Electronic health records (EHR) are a rich source of real-world data that can be leveraged to study antimicrobial use and resistance patterns. However, constructing meaningful datasets from EHR data requires rigorous curation and preprocessing to ensure accuracy, relevance, and usability. ARMD aims to facilitate research in antimicrobial stewardship, with applications in identifying resistance patterns, evaluating treatment practices, and informing public health interventions. By leveraging de-identified EHR data from Stanford Healt...,

抗生素耐药性微生物数据集（Antibiotic Resistance Microbiology Dataset，ARMD）是依托斯坦福医疗体系的电子健康记录（electronic health records, EHR）构建的结构化去标识化资源。该数据集全面涵盖尿液、呼吸道及血液微生物培养检测的相关信息，包含283,715名独特成年患者的样本数据，提供培养结果、鉴定菌株、抗生素敏感性以及配套的人口统计学与临床详细信息。本数据集通过多步骤流程精心构建，旨在提升数据质量与相关性。借助该数据集，研究人员与临床医师可开展抗菌药物耐药模式研究、支持抗菌药物管理工作，进而为优化感染性疾病诊疗、应对日益严峻的抗菌药物耐药威胁提供宝贵的研究资源。

## 队列筛选
本数据集依托斯坦福医疗体系的去标识化EHR数据构建，以满足相关研究需求。ARMD收录了成年患者（年龄≥18岁）的微生物培养数据，并包含与抗菌药物耐药性研究相关的关键临床数据点。队列构建涉及以下特征与流程：
- 培养类型：纳入的微生物培养检测具体包括尿液、呼吸道及血液培养。
- 时间调整：为保护数据隐私，对培养医嘱的时间信息进行了抖动处理（jittering），在保留有效时间关联关系的同时保障患者隐私。
- 培养阳性状态：每份培养样本均标记为阳性或阴性，以指示是否检出菌株。对于标记为阴性的培养样本，其敏感性字段以空值表示。
- 菌株鉴定与敏感性检测：针对阳性培养样本，记录其鉴定出的菌株及其抗生素敏感性……

## 抗生素耐药性微生物数据集（ARMD）：基于电子健康记录研究抗菌药物耐药性的去标识化资源

## 背景
抗菌药物耐药性（antimicrobial resistance, AMR）是亟待解决的全球性公共卫生挑战，抗生素的过度使用与不当使用进一步加剧了这一问题。缓解抗菌药物耐药性的工作需要高质量数据集，用于分析微生物敏感性趋势、指导临床决策以及为抗菌药物管理项目提供依据。电子健康记录是可用于研究抗菌药物使用与耐药模式的优质真实世界数据源，但从EHR数据中构建有意义的数据集需要经过严格的整理与预处理流程，以确保数据的准确性、相关性与可用性。ARMD旨在推动抗菌药物管理相关研究，其应用场景包括识别耐药模式、评估治疗实践以及为公共卫生干预措施提供参考。本数据集依托斯坦福医疗体系的去标识化EHR数据构建……