Studies in health technology and informatics最新文献_第10页

PROSurvival: A Technical Case Report on Creating and Publishing a Dataset for Federated Learning on Survival Prediction of Prostate Cancer Patients. PROSurvival：关于创建和发布前列腺癌患者生存预测联合学习数据集的技术案例报告。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241096

Tingyan Xu, Timo Wolters, Johannes Lotz, Tom Bisson, Tim-Rasmus Kiehl, Nadine Flinner, Norman Zerbe, Marco Eichelberg

{"title":"PROSurvival: A Technical Case Report on Creating and Publishing a Dataset for Federated Learning on Survival Prediction of Prostate Cancer Patients.","authors":"Tingyan Xu, Timo Wolters, Johannes Lotz, Tom Bisson, Tim-Rasmus Kiehl, Nadine Flinner, Norman Zerbe, Marco Eichelberg","doi":"10.3233/SHTI241096","DOIUrl":"https://doi.org/10.3233/SHTI241096","url":null,"abstract":"The PROSurvival project aims to improve the prediction of recurrence-free survival in prostate cancer by applying federated machine learning to whole slide images combined with selected clinical data. Both the image and clinical data will be aggregated into an anonymized dataset compliant with the General Data Protection Regulation and published under the principles of findable, accessible, interoperable, and reusable data. The DICOM standard will be used for the image data. For the accompanying clinical data, a human-readable, compact and flexible standard is yet to be defined. From the set of existing standards, mostly extendable with varying degrees of modifications, we chose oBDS as a starting point and modified it to include missing data points and to remove mandatory items not applicable to our dataset. Clinical and survival data from clinic-specific spreadsheets were converted into this modified standard, ensuring on-site data privacy during processing. For publication of the dataset, both image and clinical data are anonymized using established methods. The key challenges arose during the clinical data anonymization and in identifying research repositories meeting all of our requirements. Each clinic had to coordinate the publication with their responsible data protection officers, requiring different approval processes due to the individual states' differing interpretations of the legal regulations. The newly established German Health Data Utilization Act is expected to simplify future data sharing in a responsible and powerful way.","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"321 ","pages":"220-224"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142688780","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

Macro vs Micro Skin Imaging: Finding an Affordable Approach for Dermatological Care Access in Rural/Remote Areas. 宏观与微观皮肤成像：为农村/偏远地区的皮肤病治疗找到经济实惠的方法。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241078

Adela-Vasilica Gudiu, Lăcrămioara Stoicu-Tivadar

{"title":"Macro vs Micro Skin Imaging: Finding an Affordable Approach for Dermatological Care Access in Rural/Remote Areas.","authors":"Adela-Vasilica Gudiu, Lăcrămioara Stoicu-Tivadar","doi":"10.3233/SHTI241078","DOIUrl":"https://doi.org/10.3233/SHTI241078","url":null,"abstract":"The present study explored alternative methods for photographing skin lesions in the absence of specialized instruments like dermatoscopes, aiming to enhance remote diagnostic capabilities, particularly in light of the increasing incidence of melanoma cases annually. Using two lenses attached to a smartphone camera, one macroscopic and the other microscopic, study images of nevus formations from one individual were captured, and, in the absence of a collaboration with a dermatologist, subsequently labeled as melanoma or non-melanoma using a Convolutional Neural Network (CNN) which was trained, with dermoscopic images of melanoma and non-melanoma formations, to see on which image set better performances would be attained. The CNN demonstrated better performance on microscopic images, with 75% of the dataset being labeled correctly, compared to the macroscopic one, with 63% of the dataset being labeled correctly. These findings highlight the potential of smartphone-based imaging with specialized micro lenses to improve diagnostic accuracy for melanoma and other dermatological conditions in remote healthcare settings.","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"321 ","pages":"134-138"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690289","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

The Pediatric Growth Hormone Deficiency Patient Journey: Identifying Opportunities for Digital Health Interventions. 小儿生长激素缺乏症患者之旅：确定数字健康干预的机会。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241066

Guido Giunti, Fulvio Michelis, Ammar Halabi, Ekaterina Koledova, Jamie Harvey, Paul Dimitri

{"title":"The Pediatric Growth Hormone Deficiency Patient Journey: Identifying Opportunities for Digital Health Interventions.","authors":"Guido Giunti, Fulvio Michelis, Ammar Halabi, Ekaterina Koledova, Jamie Harvey, Paul Dimitri","doi":"10.3233/SHTI241066","DOIUrl":"https://doi.org/10.3233/SHTI241066","url":null,"abstract":"Pediatric growth hormone deficiency (PGHD) is a chronic condition where the pituitary gland fails to produce sufficient growth hormone, leading to delayed growth and developmental challenges. Patient journey maps can provide insight into pain points and potential opportunities for new or improved interventions to enhance care. However, a patient journey map does not yet exist for PGHD. Secondary data analysis was performed on interviews and focus groups from five cohorts in Sweden, the United Kingdom, Luxembourg, France, and The Netherlands. Participants included 62 patients and caregivers who used a prototype digital health solution, which was used to guide discussions. Grounded theory was used to analyze the data, resulting in a patient journey map comprising six stages: awareness, diagnosis, treatment planning, treatment initiation, treatment maintenance and transition. This provides the first detailed PGHD patient journey map, revealing emotional sensitivities and challenges at each stage, and suggesting areas for targeted interventions to improve adherence and long-term outcomes.","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"321 ","pages":"74-78"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690230","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

A Collection of Data Quality Indicators for Health Research: Rationale for an Update. 健康研究数据质量指标集》：更新的理由。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241103

Jürgen Stausberg, Sonja Harkener, Solveig Bünz

引用次数: 0

Integrating Clinical Data and Patient-Reported Outcomes for Analyzing Gender Differences and Progression in Multiple Sclerosis Using Machine Learning. 整合临床数据和患者报告结果，利用机器学习分析多发性硬化症的性别差异和病情进展。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241053

Minerva Viguera Moreno, Maria Eugenia Marzo Sola, Ricardo Sanchez de Madariaga, Fernando Martin-Sanchez

{"title":"Integrating Clinical Data and Patient-Reported Outcomes for Analyzing Gender Differences and Progression in Multiple Sclerosis Using Machine Learning.","authors":"Minerva Viguera Moreno, Maria Eugenia Marzo Sola, Ricardo Sanchez de Madariaga, Fernando Martin-Sanchez","doi":"10.3233/SHTI241053","DOIUrl":"https://doi.org/10.3233/SHTI241053","url":null,"abstract":"Multiple sclerosis (MS) is a complex neurodegenerative disease with a variable prognosis that complicates effective management and treatment. This study leverages machine learning (ML) to enhance the understanding of disease progression and uncover gender-based differences in MS by analyzing clinical data integrated with patient-reported outcomes (PROMs). We conducted a prospective cohort study involving 250 MS patients at a secondary care hospital in Spain over an 18-month period. Using REDCap for data management, we collected comprehensive demographic, clinical, and PROMs data. Our analysis utilized Decision Trees, Random Forest, and Support Vector Machine algorithms to classify patients based on disease evolution and infer Expanded Disability Status Scale (EDSS) levels. Additionally, we employed propensity score matching to analyze gender differences, focusing on clinical outcomes and quality of life measures. The results could indicate that integrating diverse data sets through ML would significantly improve the diagnostic accuracy and serve as a support for clinician's decision making. Our models achieved high accuracy in classifying MS types and predicting disability levels, demonstrating the potential of ML in personalized treatment planning. Furthermore, our findings suggest notable gender differences in disease progression and response to treatment. These insights advocate for a gender-specific approach in MS management and highlight the importance of personalized medicine. This study underscores the transformative potential of ML in enhancing the understanding and management of MS through integrated data analysis.","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"321 ","pages":"17-21"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690287","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

Organizing an Interdisciplinary Platform for Knowledge Sharing on a Class of Compounds of Natural Origin. 组织跨学科平台，分享有关一类天然化合物的知识。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241061

Ylenia Murgia, Valeria Iobbi, Angela Bisio, Nunziatina de Tommasi, Mauro Giacomini

引用次数: 0

Handwritten Data Extraction Using OpenAI ChatGPT4o and Robotic Process Automation. 使用 OpenAI ChatGPT4o 和机器人流程自动化提取手写数据。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241101

Norbert Gal-Nadasan, Vasile Stoicu-Tivadar, Emanuela Gal-Nadasan, Anca Raluca Dinu

引用次数: 0

Use Technology to Help Medical Staff Treat "New Health Problems" Arising Constantly. 利用技术帮助医务人员治疗不断出现的 "新健康问题"。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241067

Nikitas N Karanikolas

引用次数: 0

Mapping of Health System Performance Indicators to the WHO HSPA Framework. 卫生系统绩效指标与世界卫生组织 HSPA 框架的映射。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241050

Lucien Adam, Anthéa Helene Leung, Murat Sariyar

{"title":"Mapping of Health System Performance Indicators to the WHO HSPA Framework.","authors":"Lucien Adam, Anthéa Helene Leung, Murat Sariyar","doi":"10.3233/SHTI241050","DOIUrl":"https://doi.org/10.3233/SHTI241050","url":null,"abstract":"Healthcare systems worldwide face escalating costs and demographic changes, necessitating effective evaluation tools to understand their underlying challenges. Switzerland's high-quality yet costly healthcare system underscores the need for robust assessment methods. Existing international rankings often lack transparency and comparability, highlighting the value of structured frameworks like the Health System Performance Assessment (HSPA) by the World Health Organization (WHO). This framework evaluates healthcare systems across multiple dimensions including governance, resource generation, financing, and service delivery. This paper aims to integrate Swiss healthcare indicators from the Swiss Health Observatory (Obsan) into the HSPA framework, addressing the central research question: How can these indicators be mapped to the HSPA framework, and what insights does this integration provide? Our methodology includes selecting and categorizing Obsan indicators, manually mapping them to HSPA sub-functions, and validating these mappings using word embeddings and cosine similarity. An R Shiny application was developed for interactive visualization. Results demonstrate accurate indicator assignment, enabling intuitive visualization and enhancing data structuring.","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"321 ","pages":"2-6"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690290","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

Development of an AI Platform for Advanced Breast Cancer Management. 开发用于晚期乳腺癌管理的人工智能平台。

Studies in health technology and informatics Pub Date : 2024-11-22 DOI: 10.3233/SHTI241095

Thomas Alassane Ouattara, Seydou Golo Barro, Pascal Staccini

引用次数: 0