A modular approach to knowledge graphs and FAIR data in healthcare

In healthcare, and more specifically cancer treatment, data sharing is essential yet difficult. 1 in 5 people diagnosed with cancer have a rare type of cancer, which means considerable time is needed to collect sufficient data for research. Combining data from multiple centres is therefore vital, unfortunately, linking this data is not straightforward. There are various ways healthcare centres store their data, due to for instance differences in treatment protocols and clinical systems. This means different variables and annotations are used. Consequently before we can solve any medical problems, we first need to solve this data integration challenge

A Knowledge graph representation of baseline characteristics for the Dutch proton therapy research registry

Cancer registries collect multisource data and provide valuable information that can lead to unique research opportunities. In the Netherlands, a registry and model-based approach (MBA) are used for the selection of patients that are eligible for proton therapy. We collected baseline characteristics including demographic, clinical, tumour and treatment information. These data were transformed into a machine readable format using the FAIR (Findable, Accessible, Interoperable, Reusable) data principles and resulted in a knowledge graph with baseline characteristics of proton therapy patients. With this approach, we enable the possibility of linking external data sources and optimal flexibility to easily adapt the data structure of the existing knowledge graph to the needs of the clinic

Distributed radiomics as a signature validation study using the Personal Health Train infrastructure

Contains fulltext : 209427.pdf (publisher's version ) (Open Access

A comparison of machine learning models for predicting urinary incontinence in men with localized prostate cancer

IntroductionUrinary incontinence (UI) is a common side effect of prostate cancer treatment, but in clinical practice, it is difficult to predict. Machine learning (ML) models have shown promising results in predicting outcomes, yet the lack of transparency in complex models known as “black-box” has made clinicians wary of relying on them in sensitive decisions. Therefore, finding a balance between accuracy and explainability is crucial for the implementation of ML models. The aim of this study was to employ three different ML classifiers to predict the probability of experiencing UI in men with localized prostate cancer 1-year and 2-year after treatment and compare their accuracy and explainability. MethodsWe used the ProZIB dataset from the Netherlands Comprehensive Cancer Organization (Integraal Kankercentrum Nederland; IKNL) which contained clinical, demographic, and PROM data of 964 patients from 65 Dutch hospitals. Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms were applied to predict (in)continence after prostate cancer treatment. ResultsAll models have been externally validated according to the TRIPOD Type 3 guidelines and their performance was assessed by accuracy, sensitivity, specificity, and AUC. While all three models demonstrated similar performance, LR showed slightly better accuracy than RF and SVM in predicting the risk of UI one year after prostate cancer treatment, achieving an accuracy of 0.75, a sensitivity of 0.82, and an AUC of 0.79. All models for the 2-year outcome performed poorly in the validation set, with an accuracy of 0.6 for LR, 0.65 for RF, and 0.54 for SVM. ConclusionThe outcomes of our study demonstrate the promise of using non-black box models, such as LR, to assist clinicians in recognizing high-risk patients and making informed treatment choices. The coefficients of the LR model show the importance of each feature in predicting results, and the generated nomogram provides an accessible illustration of how each feature impacts the predicted outcome. Additionally, the model’s simplicity and interpretability make it a more appropriate option in scenarios where comprehending the model’s predictions is essential

The role of mHealth intervention to improve maternal and child health: A provider-based qualitative study in Southern Ethiopia.

IntroductionMaternal and child mortality remained higher in developing regions such as Southern Ethiopia due to poor maternal and child health. Technologies such as mobile applications in health may be an opportunity to reduce maternal and child mortality because they can improve access to information. Therefore, the main aim of this study was to explore the role of mHealth in improving maternal and child health in Southern Ethiopia.MethodsThis study employed a qualitative study design to explore the role of mHealth in improving maternal and child health among health professionals in Southern Ethiopia from December 2022 to March 2023. We conducted nine in-depth interviews, six key informants' in-depth interviews, and four focused group discussions among health professionals. This is followed by thematic analyses to synthesize the collected evidence.ResultsThe results are based on 226 quotations, 5 major themes, and 24 subthemes. The study participants discussed the possible acceptance of mHealth in terms of its fitness in the existing health system, its support to health professionals, and its importance in improving maternal and child health. The participants ascertained the importance of awareness creation before the implementation of mHealth among women, families, communities, and providers. They reported the importance of mHealth for mothers and health professionals and the effectiveness of mHealth services. The participants stated that the main challenges related to acceptance, awareness, negligence, readiness, and workload. However, they also suggested strategic solutions such as using family support, provider support, mothers' forums, and community forums.ConclusionThe evidence generated during this analysis is important information for program implementations and can inform policy-making. The planned intervention needs to introduce mHealth in Southern Ethiopia. Planners, decision-makers, and researchers can use it in mobile technology-related interventions. For challenges identified, we recommend solution-identified-based interventions and quality studies

Effect of mobile phone messaging on uptake of maternal and child health service in southern Ethiopia: Protocol for cluster randomized controlled trial

Summary: Introduction: Over the last two decades, there have been some efforts to improve maternal and child health around the globe. However, the efforts are still lacking longstanding outcomes. Lack of awareness and access to information for decision-making has been an obstacle for women, especially in developing countries. This study aims to find potential strategies to improve maternal and child health using mHealth in Ethiopia to enhance service uptake and future decisions. Methods: This study applies cluster randomized controlled trials to test if mobile phone (mHealth) text messages can improve the uptaking of postpartum family planning and child feeding practices such as prelacteal feeding, initiation of breastfeeding, exclusive breastfeeding, and complementary feeding. Repeated data collections during the first and fifth months enhance understanding of child feeding practice and its improvement compared to baseline information. It can also help to understand postpartum family planning practices among mothers. The sample size includes 672 mothers who are in the third trimester, have access to a mobile phone, can use mobile phones, and can read and understand text messages. Multilevel logistic regression, generalized estimation equation, and survival analysis are the models for final analysis. The models enable us to determine the percentage differences between baseline and intervention groups. Ethics: The team has collected ethical clearance to conduct the study from the Arba Minch University Board of Institutional Ethical Review. Dissemination: As part of open science communication, the team is committed to publishing the protocol, procedures, findings of the trial, and then the data. Trial registration number: PACTR202211547107725 Pan African Clinical Trial Registry

A systematic review of the effect of dynamic informed consent on patient data privacy, ownership, trust, and willingness to disclose

This paper is a comprehensive study that looks into the impact of dynamic informed consent on patient data privacy, ownership, trust, and willingness to disclose. The study includes a systematic review of existing literature to identify and evaluate relevant studies that have been published on the topic. The review process is thorough and systematic, with pre-determined inclusion and exclusion criteria used to ensure that only high-quality studies are included in the review. The study's findings indicate that dynamic informed consent has a significant effect on patient data privacy, ownership, trust, and willingness to disclose. Dynamic informed consent, in particular, was found to increase patient trust and willingness to disclose personal health information. Furthermore, dynamic informed consent improves patient understanding of their data privacy rights and ownership, according to the study. The paper provides useful information about the potential benefits of dynamic informed consent for patients and healthcare providers. The study emphasizes the significance of utilizing dynamic informed consent in healthcare settings to improve patient data privacy, ownership, trust, and willingness to disclose. Overall, the paper adds to the growing body of literature on the application of dynamic informed consent in healthcare