Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Integrating a Fragmented Pharmacy Network into a Unified Supply Chain System: A Case Study from Qatar's Primary Healthcare.

Journal of healthcare leadership·2026
Same author

Exploring the Landscape of Operating Room Scheduling: A Bibliometric Analysis of Recent Advancements and Future Prospects.

Biomedical engineering and computational biology·2025
Same author

Advancements and challenges in robotic surgery: A holistic examination of operational dynamics and future directions.

Surgery in practice and science·2025
Same author

Diagnosis Challenges in Adult Leukemia: Insights From a Single-Center Retrospective Study in Qatar (2016-2021).

Cancer control : journal of the Moffitt Cancer Center·2025
Same author

A comparative retrospective study of pre-fibrotic primary myelofibrosis <i>versus</i> overtly fibrotic stage in Qatar: clinicopathological, genetic landscape, risk stratification and survival data (2008-2021) - a single center experience.

Hematology (Amsterdam, Netherlands)·2024
Same author

Venetoclax in the treatment of secondary plasma cell leukemia with translocation t(11;14): a case report and literature review.

Frontiers in oncology·2024

相关实验视频

Updated: Jul 25, 2025

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up
09:01

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up

Published on: March 26, 2018

14.0K

一个基于人工智能的诊断系统,用于检测急性淋巴细胞白血病.

Yousra El Alaoui1, Regina Padmanabhan1, Adel Elomri1

  • 1College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.

Studies in health technology and informatics
|June 30, 2023
PubMed
概括

对急性淋巴细胞白血病 (ALL) 的新诊断模型仅使用完整血清 (CBC) 数据. 与其他机器学习算法相比,决策树模型在检测ALL方面表现优越.

关键词:
所有的,所有的,所有的.在CBC中,CBC就是CBC.早期检测 早期检测机器学习是机器学习.

更多相关视频

Comprehensive Protocol to Sample and Process Bone Marrow for Measuring Measurable Residual Disease and Leukemic Stem Cells in Acute Myeloid Leukemia
09:57

Comprehensive Protocol to Sample and Process Bone Marrow for Measuring Measurable Residual Disease and Leukemic Stem Cells in Acute Myeloid Leukemia

Published on: March 5, 2018

29.5K
Intracellular Phosphoflow Cytometry of Acute Myeloid Leukemia Patient-Derived Xenotransplants
07:39

Intracellular Phosphoflow Cytometry of Acute Myeloid Leukemia Patient-Derived Xenotransplants

Published on: June 6, 2025

180

相关实验视频

Last Updated: Jul 25, 2025

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up
09:01

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up

Published on: March 26, 2018

14.0K
Comprehensive Protocol to Sample and Process Bone Marrow for Measuring Measurable Residual Disease and Leukemic Stem Cells in Acute Myeloid Leukemia
09:57

Comprehensive Protocol to Sample and Process Bone Marrow for Measuring Measurable Residual Disease and Leukemic Stem Cells in Acute Myeloid Leukemia

Published on: March 5, 2018

29.5K
Intracellular Phosphoflow Cytometry of Acute Myeloid Leukemia Patient-Derived Xenotransplants
07:39

Intracellular Phosphoflow Cytometry of Acute Myeloid Leukemia Patient-Derived Xenotransplants

Published on: June 6, 2025

180

科学领域:

  • 血液学 血液学 血液学
  • 医疗信息学 医疗信息学
  • 机器学习在医学中的应用

背景情况:

  • 急性淋巴细胞白血病 (ALL) 是一种严重的血液性恶性瘤.
  • 准确和早期诊断ALL对于有效治疗至关重要.
  • 目前的诊断方法可能是侵入性的或昂贵的.

研究的目的:

  • 开发一种新的ALL诊断模型,只使用完整血清 (CBC) 数据.
  • 为了确定特定于ALL诊断的关键CBC参数.
  • 基于CBC数据,比较不同机器学习算法的ALL检测效率.

主要方法:

  • 使用了86名ALL患者和86名对照者的数据集.
  • 采用特征选择技术来确定ALL特定的CBC参数.
  • 随机森林,XGBoost和决策树算法使用网格搜索和5倍交叉验证进行训练和调整.
  • 对ALL检测的模型性能进行了评估.

主要成果:

  • 特定的CBC参数被确定为高度表明ALL.
  • 与XGBoost和随机森林相比,决策树分类器在ALL检测方面取得了更高的性能.
  • 开发的模型证明了CBC数据对于非侵入性ALL诊断的潜力.

结论:

  • 仅使用CBC数据可以构建ALL的新型和有效的诊断模型.
  • 决策树算法显示了基于CBC资料的准确ALL检测的希望.
  • 这种方法为ALL查和诊断提供了一种潜在的更简单,更容易获得的方法.