Studies in health technology and informatics最新文献_第5页

The Creation of Intensional Medication Lists Using the NHS Dictionary of Medicines and Devices. 使用英国国家医疗服务系统（NHS）的《药品与器械词典》创建内部用药清单。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241097

Gavin Jamie, Rachel Byford, Rashmi Wimalaratna, Simon de Lusignan

{"title":"The Creation of Intensional Medication Lists Using the NHS Dictionary of Medicines and Devices.","authors":"Gavin Jamie, Rachel Byford, Rashmi Wimalaratna, Simon de Lusignan","doi":"10.3233/SHTI241097","DOIUrl":"https://doi.org/10.3233/SHTI241097","url":null,"abstract":"The identification of medications prescribed to patients in routinely collected health records is an important part of the identification of cohorts for surveillance and research. Preparations available for prescription can change frequently and this presents challenges to the maintenance of extensional or \"flat lists\" of medications, particularly in ongoing studies such as disease surveillance. The NHS publishes a Dictionary of Medicines and Devices weekly, listing almost all the medications available in the UK as an extension to the UK edition of SNOMED CT. We developed a method of creating intensional specifications of medications using specified active ingredients and the form of the medication. The specifications can be expressed using the SNOMED CT Expression Constraint Language, and can be used to form a library which may be used across multiple projects. We have developed intensional definitions of medication groups for all drugs likely to be used in primary care. We have shown that these can be shared as FHIR valuesets using the NHS Terminology Server. Here we show examples of expressions about medications used for neuropathic pain. We have created expressions which improve the specificity of the extraction by filtering on the form and number of ingredients.","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"321 ","pages":"225-229"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using Deep Learning to Suggest Treatment for Proximal Humerus Fractures. 利用深度学习为肱骨近端骨折提供治疗建议

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241080

Mohammadreza Azarpira, Ihssen Belhadj, Mohammed Khodja

引用次数: 0

Extending the Scope of Telemedicine to Podiatric Medicine. 将远程医疗的范围扩大到足病医疗。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241069

Lisa A Stojmanovski Mercieca, Cynthia Formosa, Nachiappan Chockalingam, Vincent Cassar

引用次数: 0

Use and Evaluation of GANs for Synthetic Data Generation in Pharmacogenetics. 在药物遗传学合成数据生成中使用和评估 GANs。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241100

Dominic Aeschbacher, Jessica Meisner, Marko Miletic, Murat Sariyar

{"title":"Use and Evaluation of GANs for Synthetic Data Generation in Pharmacogenetics.","authors":"Dominic Aeschbacher, Jessica Meisner, Marko Miletic, Murat Sariyar","doi":"10.3233/SHTI241100","DOIUrl":"https://doi.org/10.3233/SHTI241100","url":null,"abstract":"Pharmacogenetics (PGx) explores the influence of genetic variability on drug efficacy and tolerability. Synthetic Data Generation (SDG) has emerged as a promising alternative to the labor-intensive process of collecting real-world PGx data, which is required for high-qualitative prediction models. This study investigates the performance of two Generative Adversarial Network (GAN) models, CTGAN and CTAB-GAN+, in generating synthetic PGx data. The benchmarking is based on utility metrics (Hellinger distance and Random Forest accuracy) and ϵ-identifiability. Results demonstrate that synthetic data generated by CTAB-GAN+ can surpass the original dataset in terms of utility. For instance, CTAB-GAN+ achieves higher Random Forest accuracy compared to the original data, indicating better predictive performance. These improvements suggest that synthetic data not only capture the essential patterns of the original data but also enhance model generalization and prediction capabilities, providing a more robust training ground for machine learning models. Consequently, SDG offers a promising solution to address data scarcity and imbalance in pharmacogenetic research.","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"321 ","pages":"240-244"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preparing for Hospital at Home: A Review of the Current Landscape of Training Practices. 在家为住院做准备：当前培训做法回顾。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241060

Kerstin Denecke, Daniel Reichenpfader

{"title":"Preparing for Hospital at Home: A Review of the Current Landscape of Training Practices.","authors":"Kerstin Denecke, Daniel Reichenpfader","doi":"10.3233/SHTI241060","DOIUrl":"10.3233/SHTI241060","url":null,"abstract":"Hospital at Home (HaH) is a model of care that provides hospital-level care in the patient's home, requiring a unique set of competencies and skills from both multidisciplinary care teams and informal caregivers. These skills are often different from those required in traditional hospital settings. The aim of this paper is to consolidate the information of HaH-related education and training to support the development of standardized curricula to ensure safe hospitalization at home. We compiled relevant information from the scientific literature on HaH approaches and studies and conducted a web search. Our results indicate that healthcare professionals are trained in short training sessions, covering specific skills needed in the HaH context. These skills comprise, among others, communication, medication safety, infection control, and wound care. Patients and their families receive training in recognizing symptoms of deterioration and self-care. Concrete guidelines or standardized training programs are still missing. Future research should thus focus on developing standardized HaH training protocols and programs for both staff and patients to ensure patient safety at home.","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"321 ","pages":"48-52"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mobile Health Technologies and Their Features Affecting Medication Adherence Among Cancer Patients: A Scoping Review. 影响癌症患者坚持用药的移动医疗技术及其特点：范围综述》。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241064

Abdel Rahman Alsaify, Tourjana Islam Supti, Mahmood Alzubaidi, Mowafa Househ

引用次数: 0

Utilizing RAG and GPT-4 for Extraction of Substance Use Information from Clinical Notes. 利用 RAG 和 GPT-4 从临床笔记中提取药物使用信息。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241070

Fatemeh Shah-Mohammadi, Joseph Finkelstein

引用次数: 0

A Novel Taxonomy for Navigating and Classifying Synthetic Data in Healthcare Applications. 医疗保健应用中合成数据导航和分类的新分类标准。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241104

Bram van Dijk, Saif Ul Islam, Jim Achterberg, Hafiz Muhammad Waseem, Parisis Gallos, Gregory Epiphaniou, Carsten Maple, Marcel Haas, Marco Spruit

{"title":"A Novel Taxonomy for Navigating and Classifying Synthetic Data in Healthcare Applications.","authors":"Bram van Dijk, Saif Ul Islam, Jim Achterberg, Hafiz Muhammad Waseem, Parisis Gallos, Gregory Epiphaniou, Carsten Maple, Marcel Haas, Marco Spruit","doi":"10.3233/SHTI241104","DOIUrl":"https://doi.org/10.3233/SHTI241104","url":null,"abstract":"Data-driven technologies have improved the efficiency, reliability and effectiveness of healthcare services, but come with an increasing demand for data, which is challenging due to privacy-related constraints on sharing data in healthcare contexts. Synthetic data has recently gained popularity as potential solution, but in the flurry of current research it can be hard to oversee its potential. This paper proposes a novel taxonomy of synthetic data in healthcare to navigate the landscape in terms of three main varieties. Data Proportion comprises different ratios of synthetic data in a dataset and associated pros and cons. Data Modality refers to the different data formats amenable to synthesis and format-specific challenges. Data Transformation concerns improving specific aspects of a dataset like its utility or privacy with synthetic data. Our taxonomy aims to help researchers in the healthcare domain interested in synthetic data to grasp what types of datasets, data modalities, and transformations are possible with synthetic data, and where the challenges and overlaps between the varieties lie.","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"321 ","pages":"259-263"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging Cancer Therapy Peptide Data: A Case Study on Machine Learning Application in Accelerating Cancer Research. 利用癌症治疗肽数据：加速癌症研究的机器学习应用案例研究》。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241068

Georgios Feretzakis, Athanasios Anastasiou, Stavros Pitoglou, Aikaterini Sakagianni, Zoi Rakopoulou, Konstantinos Kalodanis, Vasileios Kaldis, Evgenia Paxinou, Dimitris Kalles, Vassilios S Verykios

引用次数: 0

Generative 3D Cardiac Shape Modelling for in-silico Trials. 生成三维心脏形状模型，用于样本内试验。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241090

Andrei Gasparovici, Alex Serban

引用次数: 0