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

Drug Therapy01:28

Drug Therapy

68
The advent of drug therapy has profoundly shaped modern mental health care, providing targeted treatments for a range of psychological disorders. Psychotherapeutic drugs, classified into antianxiety, antidepressant, and antipsychotic medications, address symptoms across anxiety disorders, mood disorders, and schizophrenia. While these medications have transformed patient outcomes, they require careful management due to their potential side effects and limitations.
Antianxiety Medications
68
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

111
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
111
Antidepressant Drugs: MAOIs and Other Agents01:23

Antidepressant Drugs: MAOIs and Other Agents

270
Atypical antidepressants, including bupropion (Wellbutrin), mirtazapine (Remeron), nefazodone (Serzone), trazodone (Desyrel), and vilazodone (Viibryd), offer unique mechanisms of action. Bupropion weakly inhibits dopamine and norepinephrine reuptake, aiding depression treatment and smoking cessation, with a low risk of sexual dysfunction. Mirtazapine enhances serotonin and norepinephrine neurotransmission, leading to sedation, increased appetite, and weight gain. As a result, it helps treat...
270
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

64
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
64
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

764
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
764
Pharmacovigilance01:19

Pharmacovigilance

884
Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
884

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

A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district.

PLOS global public health·2023
Same author

Research evidence on strategies enabling integration of electronic health records in the health care systems of low- and middle-income countries: A literature review.

The International journal of health planning and management·2019
Same author

Research gaps in routine health information system design barriers to data quality and use in low- and middle-income countries: A literature review.

The International journal of health planning and management·2017
Same journal

Chaotic and Stochastic Components in an Influenza Surveillance Series: Nonlinear Dynamics and Predictive Modeling Study.

JMIRx med·2026
Same journal

Interpreting the Estimand Framework From a Causal Inference Perspective.

JMIRx med·2026
Same journal

The Performance of DeepSeek R1 and Gemini 3 in Complex Medical Scenarios: Comparative Study.

JMIRx med·2026
Same journal

Awareness, Experiences, and Attitudes Toward Preprints Among Medical Academics: Convergent Mixed Methods Study.

JMIRx med·2026
Same journal

Author's Response to Peer Review Reports on "Investigating the Variable Component of the Systematic Error, a Neglected Error Parameter: Theoretical Reevaluation Study".

JMIRx med·2026
Same journal

Investigating the Variable Component of the Systematic Error, a Neglected Error Parameter: Theoretical Reevaluation Study.

JMIRx med·2026
查看所有相关文章

相关实验视频

Updated: Jul 16, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

机器学习和药物坚持:范围审查

Aaron Bohlmann1, Javed Mostafa1, Manish Kumar1,2

  • 1Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.

JMIRx med
|September 19, 2023
PubMed
概括
此摘要是机器生成的。

机器学习使用20个预测器准确预测药物坚持. 监控系统对吸入器使用和帕金森病药物显示高准确性,人工智能提醒显著改善了坚持.

关键词:
合规性监测 合规性监测 合规性监测坚持性预测 坚持性预测医疗技术 医疗技术 医疗技术机器学习是机器学习.药物治疗的坚持 药物治疗的坚持符合药物规定的药物合规性

更多相关视频

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

相关实验视频

Last Updated: Jul 16, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

科学领域:

  • * 生物医学信息学
  • * 医疗保健中的人工智能

背景情况:

  • *这是第一个广泛检查机器学习 (ML) 应用在药物坚持方面的范围审查.
  • *现有文献强调了利用ML提高患者对处方治疗的坚持度的日益兴趣.

研究的目的:

  • *系统地分类,总结和分析现有的关于机器学习用于药物坚持的使用文献.
  • * 确定ML驱动的药物坚持研究中的关键预测因素,方法和结果.

主要方法:

  • * 进行了主要科学数据库 (PubMed,Scopus,ACM,IEEE,Web of Science) 的全面搜索.
  • *采用了包括标准,根据PRISMA-ScR指南分析了43项相关研究.
  • *研究被系统地绘制图表,并根据它们对药物坚持行为的方法进行分类.

主要成果:

  • *在研究中确定了20个强有力的药物坚持预测因素,自我报告问卷和药房声明是常见的数据来源.
  • *经常使用机器学习模型,如物流回归,神经网络,随机森林和支持矢量机器,预测准确度高达77.6%.
  • * 监测系统的准确性很高 (例如,吸入器使用时>93%),人工智能驱动的提醒显著提高了与传统方法相比的坚持率.

结论:

  • * 机器学习显示了准确预测药物坚持的巨大潜力,使得有针对性的干预措施能够防止不坚持.
  • * 监测系统,特别是用于吸入器使用和帕金森病,达到高精度,为药物管理提供了宝贵的见解.
  • *对话式人工智能提醒有效地提高了坚持,尽管上下文感知系统可能会引起用户侵入性的担忧.