Natural Language Processing: Set to Transform Pediatric Research.
作者信息
Bannett Yair, Bassett Hannah K, Morse Keith E
机构信息
Division of Developmental-Behavioral Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Stanford, California.
Division of Pediatric Hospital Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California.
出版信息
Hosp Pediatr. 2025 Jan 1;15(1):e12-e14. doi: 10.1542/hpeds.2024-008115.
Abstract
摘要
相似文献
1
Natural Language Processing: Set to Transform Pediatric Research.自然语言处理：即将改变儿科研究。
Hosp Pediatr. 2025 Jan 1;15(1):e12-e14. doi: 10.1542/hpeds.2024-008115.
2
Examining Implicit Bias Differences in Pediatric Surgical Fellowship Letters of Recommendation Using Natural Language Processing.使用自然语言处理技术研究儿科外科住院医师培训推荐信中的隐性偏见差异
J Surg Educ. 2023 Apr;80(4):547-555. doi: 10.1016/j.jsurg.2022.12.002. Epub 2022 Dec 17.
3
Facilitating clinical research through automation: Combining optical character recognition with natural language processing.通过自动化促进临床研究：结合光学字符识别和自然语言处理。
Clin Trials. 2022 Oct;19(5):504-511. doi: 10.1177/17407745221093621. Epub 2022 May 24.
4
Classifying literature mentions of biological pathogens as experimentally studied using natural language processing.使用自然语言处理对文献中提到的生物病原体进行分类，判断其是否经过实验研究。
J Biomed Semantics. 2023 Jan 31;14(1):1. doi: 10.1186/s13326-023-00282-y.
5
Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing.设计一个基于 openEHR 的管道，使用自然语言处理提取和标准化非结构化临床数据。
Methods Inf Med. 2020 Dec;59(S 02):e64-e78. doi: 10.1055/s-0040-1716403. Epub 2020 Oct 14.
6
Estimation of racial and language disparities in pediatric emergency department triage using statistical modeling and natural language processing.利用统计建模和自然语言处理估计儿科急诊分诊中的种族和语言差异。
J Am Med Inform Assoc. 2024 Apr 3;31(4):958-967. doi: 10.1093/jamia/ocae018.
7
Development and Validation of an Automated Method to Identify Patients Undergoing Radical Cystectomy for Bladder Cancer Using Natural Language Processing.一种使用自然语言处理技术识别接受膀胱癌根治性膀胱切除术患者的自动化方法的开发与验证
Urol Pract. 2017 Sep;4(5):365-372. doi: 10.1016/j.urpr.2016.09.011. Epub 2016 Nov 1.
8
Exploring prevalence of wound infections and related patient characteristics in homecare using natural language processing.利用自然语言处理技术探索家庭护理中伤口感染的流行情况及相关患者特征。
Int Wound J. 2022 Jan;19(1):211-221. doi: 10.1111/iwj.13623. Epub 2021 Jun 9.
9
Natural language processing: state of the art and prospects for significant progress, a workshop sponsored by the National Library of Medicine.自然语言处理：现状与取得重大进展的前景，由美国国家医学图书馆主办的研讨会。
J Biomed Inform. 2013 Oct;46(5):765-73. doi: 10.1016/j.jbi.2013.06.004. Epub 2013 Jun 25.
10
A Case Demonstration of the Open Health Natural Language Processing Toolkit From the National COVID-19 Cohort Collaborative and the Researching COVID to Enhance Recovery Programs for a Natural Language Processing System for COVID-19 or Postacute Sequelae of SARS CoV-2 Infection: Algorithm Development and Validation.来自国家新冠病毒队列协作组的开放健康自然语言处理工具包案例演示以及为新冠病毒感染或新冠后综合征增强恢复计划而开展的新冠病毒自然语言处理系统研究：算法开发与验证
JMIR Med Inform. 2024 Sep 9;12:e49997. doi: 10.2196/49997.
引用本文的文献
1
ADHD Diagnosis and Timing of Medication Initiation Among Children Aged 3 to 5 Years.3至5岁儿童的注意力缺陷多动障碍诊断及药物治疗起始时间
JAMA Netw Open. 2025 Aug 1;8(8):e2529610. doi: 10.1001/jamanetworkopen.2025.29610.
2
Timing of Medication Treatment in Children 3-5-Years-old with ADHD: A PEDSnet Study.3至5岁多动症儿童药物治疗的时机：一项儿科电子数据网络（PEDSnet）研究
medRxiv. 2025 May 29:2025.05.28.25328526. doi: 10.1101/2025.05.28.25328526.
本文引用的文献
1
Natural Language Processing to Identify Infants Aged 90 Days and Younger With Fevers Prior to Presentation.利用自然语言处理技术识别90日龄及以下婴儿就诊前的发热情况。
Hosp Pediatr. 2025 Jan 1;15(1):e1-e5. doi: 10.1542/hpeds.2024-008051.
2
Harnessing Natural Language Processing to Assess Quality of End-of-Life Care for Children With Cancer.利用自然语言处理评估癌症儿童临终关怀质量。
JCO Clin Cancer Inform. 2024 Sep;8:e2400134. doi: 10.1200/CCI.24.00134.
3
Accuracy of a Proprietary Large Language Model in Labeling Obstetric Incident Reports.专有大型语言模型在标记产科事件报告中的准确性。
Jt Comm J Qual Patient Saf. 2024 Dec;50(12):877-881. doi: 10.1016/j.jcjq.2024.08.001. Epub 2024 Aug 6.
4
Evaluation of a Large Language Model to Identify Confidential Content in Adolescent Encounter Notes.评估大型语言模型以识别青少年会诊记录中的保密内容。
JAMA Pediatr. 2024 Mar 1;178(3):308-310. doi: 10.1001/jamapediatrics.2023.6032.
5
Measuring quality-of-care in treatment of young children with attention-deficit/hyperactivity disorder using pre-trained language models.使用预训练语言模型衡量注意力缺陷多动障碍幼儿的治疗质量。
J Am Med Inform Assoc. 2024 Apr 3;31(4):949-957. doi: 10.1093/jamia/ocae001.
6
Misclassification of Both Influenza Infection and Oseltamivir Exposure Status in Administrative Data.
JAMA Pediatr. 2024 Feb 1;178(2):201-203. doi: 10.1001/jamapediatrics.2023.5731.
7
A scoping review of ethics considerations in clinical natural language processing.临床自然语言处理中伦理考量的范围综述。
JAMIA Open. 2022 May 26;5(2):ooac039. doi: 10.1093/jamiaopen/ooac039. eCollection 2022 Jul.
8
Common Diagnoses and Costs in Pediatric Hospitalization in the US.美国儿科住院的常见诊断和费用。
JAMA Pediatr. 2022 Mar 1;176(3):316-318. doi: 10.1001/jamapediatrics.2021.5171.
9
Identifying Conditions With High Prevalence, Cost, and Variation in Cost in US Children's Hospitals.识别美国儿童医院中具有高患病率、高成本和高成本变异性的疾病。
JAMA Netw Open. 2021 Jul 1;4(7):e2117816. doi: 10.1001/jamanetworkopen.2021.17816.
10
Estimate the hidden deployment cost of predictive models to improve patient care.估算预测模型的隐性部署成本以改善患者护理。
Nat Med. 2020 Jan;26(1):18-19. doi: 10.1038/s41591-019-0651-8.
Zotero 插件