用药提醒系统的构建与应用:智能生成通用用药计划表。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2024-07-15 DOI:10.1186/s13040-024-00376-y
Hangxing Huang, Lu Zhang, Yongyu Yang, Ling Huang, Xikui Lu, Jingyang Li, Huimin Yu, Shuqiao Cheng, Jian Xiao
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引用次数: 0

摘要

背景:慢性病患者每天需要服用多种药物来控制病情。然而,大多数患者的依从性较差,影响了治疗效果。为解决这些难题,我们建立了一个用药提醒系统,用于智能生成通用用药时间表(UMS),提醒慢性病患者准确服药,并提高家庭用药的安全性:方法:设计每种药物的单药服药时间约束(MTCOD)和每两种药物的多药服药时间约束(MTCMD),以更好地调节患者服药的间隔和时间。建立由药物信息云数据库、医务人员操作终端和患者终端组成的用药提醒系统:云数据库共有 153,916 个药品、496,708 个药物相互作用数据和 153,390 对药品成分。MTCOD 数据为 153,916 条,MTCMD 数据为 8,552,712 条。构建了一个智能 UMS 用药提醒系统。该系统可读取患者的处方信息,并为慢性病患者提供个性化的用药指导和用药时间表。同时,患者可实时查询用药信息并获得远程药房指导:总之,用药提醒系统提供了智能用药提醒、药物相互作用自动识别和监测系统,有助于监测慢性病患者的整个治疗过程。
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Construction and application of medication reminder system: intelligent generation of universal medication schedule.

Background: Patients with chronic conditions need multiple medications daily to manage their condition. However, most patients have poor compliance, which affects the effectiveness of treatment. To address these challenges, we establish a medication reminder system for the intelligent generation of universal medication schedule (UMS) to remind patients with chronic diseases to take medication accurately and to improve safety of home medication.

Methods: To design medication time constraint with one drug (MTCOD) for each drug and medication time constraint with multi-drug (MTCMD) for each two drugs in order to better regulate the interval and time of patients' medication. Establishment of a medication reminder system consisting of a cloud database of drug information, an operator terminal for medical staff and a patient terminal.

Results: The cloud database has a total of 153,916 pharmaceutical products, 496,708 drug interaction data, and 153,390 pharmaceutical product-ingredient pairs. The MTCOD data was 153,916, and the MTCMD data was 8,552,712. An intelligent UMS medication reminder system was constructed. The system can read the prescription information of patients and provide personalized medication guidance with medication timeline for chronic patients. At the same time, patients can query medication information and get remote pharmacy guidance in real time.

Conclusions: Overall, the medication reminder system provides intelligent medication reminders, automatic drug interaction identification, and monitoring system, which is helpful to monitor the entire process of treatment in patients with chronic diseases.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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