bluuebunny/crossref_metadata_2025 huggingface dataset:
Automation of Trainable Datasets Generation for Medical-Specific Language Model: Using MIMIC-IV Discharge Notes
Authors: Lee Youngrong , Kim Chansik , Ko Taehoon
Published: August 2024

This study introduces a novel approach for generating machine-generated instruction datasets for fine-tuning medical-specialized language models using MIMIC-IV discharge records. The study created a large-scale text dataset comprising instructions, cropped discharge notes as inputs, and outputs in JSONL format. The dataset was generated through three main stages, generating instruction and output using seed tasks provided by medical experts, followed by invalid data filtering. The generated dataset consisted of 51,385 sets, with mean ROUGE between seed tasks of 0.185. Evaluation of the generated dataset were promising, with high validity rates determined by both GPT-3.5 and a human annotator (88.0% and 88.5% respectively). The study highlights the potential of automating dataset creation for NLP tasks in the medical domain.
Enhancing Asthma Self-Management with Environmental Passive-Monitoring Data and Machine Learning-Based Predictions
Authors: Tsang Kevin C.H.
Published: August 2024

Monitoring enables timely action which is critical in avoiding asthma attacks. With the abundance of local weather and pollution data, when augmented with machine learning, it is becoming possible to replace traditional tedious active monitoring in certain instances for some groups of patients. This analysis on the AAMOS-00 dataset, with 22 severe asthma patients, compared the performance of XGBoost to predict worsening asthma conditions using either passive or active monitoring on two groups of patients: individuals who were and were not triggered by outdoor environmental factors. On patients triggered by environmental factors, the performances of the passive- and the active-monitoring models were close to matching (passive: AUC = 0.83, 2.9-fold increase in precision; active: AUC = 0.89, 3.1-fold increase in precision). On the other hand, the active-monitoring model was significantly superior for patients not triggered by environmental factors (passive: AUC = 0.52, 1.2-fold increase in precision; active: AUC = 0.83, 3.7-fold increase in precision). Robustness evaluation showed it was possible to have good predictions of asthma attacks in the coming 5 to 7 days using 5 to 28 days of data.
Data for AI in Congenital Heart Defects: Systematic Review
Authors: Mayer Paula Josephine , Poyraz Rasim Atakan , Hölter Thimo , Thun Sylvia , Vorisek Carina Nina
Published: August 2024

Congenital heart disease (CHD) represents a significant challenge in prenatal care due to low prenatal detection rates. Artificial Intelligence (AI) offers promising avenues for precise CHD prediction. In this study we conducted a systematic review according to the PRISMA guidelines, investigating the landscape of AI applications in prenatal CHD detection. Through searches on PubMed, Embase, and Web of Science, 621 articles were screened, yielding 28 relevant studies for analysis. Deep Learning (DL) emerged as the predominant AI approach. Data types were limited to ultrasound and MRI sequences mainly. This comprehensive analysis provides valuable insights for future research and clinical practice in CHD detection using AI applications.
Comparative Evaluation of Pre-Trained Language Models for Biomedical Information Retrieval
Authors: Weber Franziska , Toddenroth Dennis
Published: August 2024

Finding relevant information in the biomedical literature increasingly depends on efficient information retrieval (IR) algorithms. Cross-Encoders, SentenceBERT, and ColBERT are algorithms based on pre-trained language models that use nuanced but computable vector representations of search queries and documents for IR applications. Here we investigate how well these vectorization algorithms estimate relevance labels of biomedical documents for search queries using the OHSUMED dataset. For our evaluation, we compared computed scores to provided labels by using boxplots and Spearman’s rank correlations. According to these metrics, we found that Sentence-BERT moderately outperformed the alternative vectorization algorithms and that additional fine-tuning based on a subset of OHSUMED labels yielded little additional benefit. Future research might aim to develop a larger dedicated dataset in order to optimize such methods more systematically, and to evaluate the corresponding functions in IR tools with end-users.
Towards a FHIR-Based Framework for Analyzing Nursing-Related Data in Smaller Sized Care Facilities
Authors: Netzband Steffen , Müller Dominik , Meyer Stefanie , Iseni Jeton , Kramer Frank
Published: August 2024

Data-driven decision making can improve the situation for caregivers and residents in nursing homes. However, smaller facilities often lack the technical and personnel resources required to prepare and analyze data. We introduce a conceptual framework to enable data transformation and analyzation in smaller nursing homes. We deployed a prototype pipeline at a German nursing home, where we demonstrated the feasibility and effectiveness of our approach.
Enhancing Outpatient Wound Care: Applying AI to Optimize Treatment of Patients with Diabetic Foot Syndrome – The EPWUF-KI Project
Authors: Weggen Annika , Hochlenert Dirk , Mertens Mira , Cremanns Kevin , Gündodgu Cosku , Gierschner Lars , Tromp Timo , Otten Hubert
Published: August 2024

Diabetes mellitus (DM) is a significant public health issue in Germany, affecting 8 million individuals, with projections suggesting a substantial increase in the following years. Diabetic Foot Syndrome (DFS), leading to mobility issues and limb amputations, challenging healthcare due to resource shortages and the need for specialized care. The EPWUF-KI project seeks to support outpatient caregivers by incorporating artificial intelligence (AI) into DFS wound care, focusing on optimizing treatment and automating documentation.
Exploration of Eye-Tracking Methodologies in Usability Testing of Digital Health Technology: A Rapid Review
Authors: van den Berg Liesbeth , Engelsma Thomas , Peute Linda
Published: August 2024

Eye-tracking is deemed a promising methodology for usability evaluation studies in healthcare, however clear theoretical guidance and practice remains lacking. A rapid review was performed on current use of eye tracking as a usability evaluation method on digital health technologies in the period of 2019 to 2024. Usability evaluation studies were included when they described a digital health technology intervention in which eye-tracking technologies were applied. To gain insight into how eye-tracking technologies contributed to measuring digital health technologies’ usability, data was extracted on the use of eye-tracking for usability and key study findings. Seventeen papers were included in the review. Findings show that eye-tracking is frequently combined with other usability evaluation methods, with high methodological diversity, to test the usability of DHT. Future research is needed to enhance understanding of the effectiveness of eye-tracking outcomes in DHT usability testing when combined with other usability evaluation methods in order to provide (usability) researchers theoretical guidance on its application.
A Work-Sampling Approach for Assessing Adequate Availability of Computers on Hospital Wards
Authors: Prgomet Mirela , Westbrook Johanna
Published: August 2024

A key barrier to the use of health information technology on hospital wards is an insufficient number of computers to support clinical information system access. This paper reports on the development and pilot testing of a novel approach, based on work-sampling principles, for assessing adequate availability of computers.
Enhancing Pulmonary Embolism Detection in COVID-19 Patients Through Advanced Deep Learning Techniques
Authors: Feretzakis Georgios , Dalamarinis Konstantinos , Kalles Dimitris , Kiourt Chairi , Pantos Georgios , Papadopoulos Ioannis , Kouris Spyros , Verykios Vassilios S. , Ioannakis George , Loupelis Evangelos , Sakagianni Aikaterini
Published: August 2024

The intersection of COVID-19 and pulmonary embolism (PE) has posed unprecedented challenges in medical diagnostics. The critical nature of PE and its increased incidence during the pandemic underline the need for improved detection methods. This study evaluates the effectiveness of advanced deep learning techniques in enhancing PE detection in post-COVID-19 patients through Computed Tomography Pulmonary Angiography (CTPA) scans. Using a dataset of 746 anonymized CTPA images from 25 patients, we fine-tuned the state-of-the-art Ultralytics YOLOv8 object detection model, which was trained on 676 images with 1,517 annotated bounding boxes and validated on 70 images with 108 bounding boxes. After 200 epochs of training, which lasted approximately 1.021 hours, the YOLOv8 model demonstrated significant diagnostic proficiency, achieving a mean Average Precision (mAP) of 0.683 at an IoU threshold of 0.50 and a mAP of 0.246 at the IoU range of 0.50:0.95 in the validation dataset. Notably, the model reached a maximum precision of 0.85949 and a maximum recall of 0.81481, though these metrics were observed in separate epochs. These findings emphasize the model’s potential for high diagnostic accuracy and offer a promising direction for deploying AI tools in clinical settings, significantly contributing to healthcare innovation and patient care post-pandemic.
Health Data Re-Identification: Assessing Adversaries and Potential Harms
Authors: Meurers Thierry , Baum Lena , Haber Anna Christine , Halilovic Mehmed , Heinz Birgit , Milicevic Vladimir , Neves Diogo Telmo , Otte Karen , Pasquier Anna , Poikela Maija , Sheykholeslami Maryam , Wirth Felix , Prasser Fabian
Published: August 2024

Sharing biomedical data for research can help to improve disease understanding and support the development of preventive, diagnostic, and therapeutic methods. However, it is vital to balance the amount of data shared and the sharing mechanism chosen with the privacy protection provided. This requires a detailed understanding of potential adversaries who might attempt to re-identify data and the consequences of their actions. The aim of this paper is to present a comprehensive list of potential types of adversaries, motivations, and harms to targeted individuals. A group of 13 researchers performed a three-step process in a one-day workshop, involving the identification of adversaries, the categorization by motivation, and the deduction of potential harms. The group collected 28 suggestions and categorized them into six types, each associated with several of six distinct harms. The findings align with previous efforts in structuring threat actors and outcomes and we believe that they provide a robust foundation for evaluating re-identification risks and developing protection measures in health data sharing scenarios.
Use of Beacon v2 for Improving Genomics Based Research in a Clinical Setting
Authors: Naro Daniel , Delgado Jaime , Llorente Silvia , Hernaez Mikel
Published: August 2024

GA4GH has proposed the Beacon architecture as an interface to retrieve genomic information which also protects the privacy of the individuals. In this paper, we propose to adapt the Beacon Reference Implementation to the use case of a study comparing the susceptibility to the carcinogenic effects of tobacco. This analysis compares the germline of heavy smokers who have either never developed lung cancer or, on the contrary, have developed it at a young age. To adapt the Beacon Reference Implementation to the use case, we have added filtering capabilities and a new grouping of information allowing to retrieve the data by affected gene.
Enhancing Drug-Related Data Exchange: Advanced Technical Support for Interoperability
Authors: Liang James , Nikiema Jean Noel
Published: August 2024

This paper describes the technical support services developed to facilitate the usage of the Canadian Drug Ontology (OCRx). These services include a lookup service named PaperRx, which retrieves OCRx drug entities using description logic queries (DLqueries) conducted within OCRx’s classified structure. Additionally, the services feature a SPARQL endpoint, an OWL file downloader, and a RESTful Application Programming Interface. This suite of services supports the application of the Findable, Accessible, Interoperable, and Reusable (FAIR) principles to OCRx.
Comparing the Accuracy of Traditional vs. FHIR®-Based Extraction of Electronic Health Record Data for Two Clinical Trials
Authors: Garza Maryam Y. , Spencer Christina , Hamidi Muayad , Liss Michael , Bikkanuri Manju , Syed Mahanazuddin , Yadav Sunita , Bhardwaj Gurtejpal , Chahal Amar P.S. , Goodman Keith , Choi Byeong Yeob , Eisenstein Eric L. , Zozus Meredith
Published: August 2024

While pilots and production use of software based on the Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®) standard are increasing in clinical research, we lack consistent evaluative data on important outcomes, such as data accuracy. We compared the accuracy of EHR collected, FHIR® extracted data (called EHR-to-eCRF data collection) to traditional clinical trial data collection. The accuracy rate for EHR-collected data was significantly higher than for the same data collected through traditional methods. It is possible that EHR-collected (FHIR® extracted) data can substantially improve data quality in clinical studies while decreasing the burden on study sites.
Linked Care: A Harmonized Workflow for Medication Ordering, Prescription and Dispensing in Mobile Care Settings
Authors: Urbauer Philipp , Huber Gerda , Berens Timothy , Salzbrunn Benedikt , Frohner Matthias , Sauermann Stefan
Published: August 2024

The growing demand for care amid changing demographics poses significant challenges exacerbated by decreasing healthcare professional availability. In Austria, the Linked Care project aims to address these challenges by developing an intersectoral, harmonized IT-supported workflow for medication ordering, prescription, and dispense in mobile care settings. A human centered design approach, with user-focused interviews and workshops was used to identify requirements and analyze the workflows. Activity diagrams were used represent workflows. The resulting harmonized workflow, developed through iterative collaboration with care organizations, integrates the LC platform into existing care software. To test and demonstrate the harmonized workflow, mockups were created and evaluated for usability, resulting in positive feedback and suggestions for enhancements. Current workflows revealed media breaches and inefficiencies, which the proposed harmonized workflow seeks to address. The paper concludes with implications for future developments, including the subsequent adoption of a HL7 FHIR Implementation Guide for Austria, based on the defined harmonized workflow, to streamline intersectoral communication and improve efficiency in mobile care settings.
Incorrect and Sex-Inconsistent Mapping of Disorders: Identification of Sex Biases in the ICD-10, ICD-11 and SNOMED CT and How to Work Around Them
Authors: Haffer Nina , Thun Sylvia
Published: August 2024

In the international classifications ICD-10-WHO and ICD-11-WHO, many sex-specific diseases have incomplete coding. It is possible to further enhance semantic interoperability using SNOMED CT additionally to ICD. Part of the analysis of semantic interoperability of diagnoses in the ICD are Sexual Dysfunctions, Postpartum Depression, Sexual Assault, Premenstrual Tension Syndrome and Premenstrual Dysphoric Disorder, Female Genital Mutilation and Cutting, Gender Incongruence and Disorders of Breast. Labeling biases have been identified in all diagnoses, either in SNOMED CT or ICD. For mental disorders associated with pregnancy, gender incongruence and sexual violence the use of the GPS of SNOMED CT can help enhance semantic interoperability additionally to ICD.
Protocol for a Validation Study of an Ontology of Administrable Dose Forms of Medicinal Products
Authors: Roumier Joseph , Vander Stichele Robert H. , van Nimwegen Dirk , Kalra Dipak
Published: August 2024

Administrable dose form can be obtained after (no-)transformation from pharmaceutical dose form. Building on the creation of a small ontology of 428 pharmaceutical dose forms from EDQM to support alignment with other dose form ontologies (SNOMED-CT, RxNorm), the present study is focused on a simple ontology of 308 administrable dose forms, 27 Intended Sites and an intermediary level of 65 dose form groupers. The ontology was created after 432 pharmaceutical dose forms, 65 combined pharmaceutical dose forms and 73 combined terms were linked by EDQM to administrable dose forms during the UNICOM project. The article describes these resources, the resulting ontology, the differences between its top-level concepts and the source’s. It presents the protocol for a validation study through expert review, as a preparation for use case studies.
Monitoring Variability of Laboratory Results in a Clinical Data Warehouse Using Automatic Dashboard
Authors: Pierre-Jean Morgane , Rabhi Dalila , Delamarre Denis , Bendavid Claude , Guillet Benoit , Cuggia Marc , Bouzille Guillaume
Published: August 2024

Hospital laboratory results are a significant data source in Clinical Data Ware-houses (CDW). To ensure comparability across healthcare organizations and for use in research studies, the results need to be interoperable. The LOINC (Logical Observation Identifiers, Names, and Codes) terminology provides a unique identifier for local codes for lab tests, enabling interoperability. However, in real-world, events occur over time and can disrupt the distribution of lab result values. For example, new equipment may be added to the analysis pipeline, a machine may be replaced, formulas may evolve due to new scientific knowledge, and legacy terminologies may be adopted. This article proposes a pipeline for creating an automated dashboard to monitor these events and data quality. We used automatic change point detection methods such as PELT for event detection in lab results. For a given LOINC code, we create a dashboard that summarizes the number of local codes mapped, and the number of patients (by sex, age, and hospital service) associated with the code. Finally, the dashboard enables the visualization of time events that disrupt the signal distribution. The biologists were able to explain to us the changes for several biological assays.
Multiple Logistic Regression Model for Assessing the Risk Factors of Traffic Accidents: Khon Kaen Model
Authors: Sujayanont Patcharawan , Muttitanon Wutjanun , Chemin Yann , Som-Ard Jaturong , Tippayanate Nantawan
Published: August 2024

Background: Thailand has consistently held the highest global ranking in traffic accidents since 2017, with Khon Kaen displaying the highest mortality rate in the Department of Disease Control Region 7. Objectives: This study aims to utilize Injury Surveillance (IS) data to identify risk factors associated with emergency room (ER) outcomes at the Emergency Department of Khon Kaen hospital in Khon Kaen Municipality. Methods: Data from the Injury Surveillance system’s (IS system) database were collected, focusing on severity outcomes, time of events, and risk behaviors from January 1, 2008, to December 31, 2021. Data analysis was conducted using the R program, employing the Chi-square or independent T test to compare results and analyze associations between potential risk factors and ER outcomes. Multiple logistic regression (MLR) was used for classification analysis, and a confusion matrix was applied to evaluate the performance of the models. Results: MLR analysis revealed that being male, age, alcohol consumption, and nighttime driving were more likely to increase the probability of severity outcomes. Conclusion: Being male, age, alcohol consumption, and nighttime driving are identified as potential risk factors contributing to the development of severity outcomes following traffic accidents.
SIMpat: A Synthetic Benchmark for Similarity Metrics on Patient Representations
Authors: Voegeli Jean-Virgile , Bjelogrlic Mina , Gaudet-Blavignac Christophe , Dubos Richard , Zimmermann Myriam , Bensahla Talet Adel , Zheng Yuanyuan , Ehrsam Julien , Lovis Christian
Published: August 2024

Similarity and clustering tasks based on data extracted from electronic health records on the patient level suffer from the curse of dimensionality and the lack of inter-patient data comparability. Indeed, for many health institutions, there are many more variables, and ways of expressing those variables to represent patients than patients sharing the same set of data. To lower redundancy and increase interoperability one strategy is to map data to semantic-driven representations through medical knowledge graphs such as SNOMED-CT. However, patient similarity metrics based on this knowledge-graph information lack quantitative evaluation and comparisons with pure data-driven methods. The reasons are twofold, firstly, it is hard to conceptually assess and formalize a gold-standard similarity between patients resulting in poor inter-annotator agreement in qualitative evaluations. Secondly, the community has been lacking a clear benchmark to compare existing metrics developed by scientific communities coming from various fields such as ontology, data science, and medical informatics. This study proposes to leverage the known challenges of evaluating patient similarities by proposing SIMpat, a synthetic benchmark to quantitatively evaluate available metrics, based on controlled cohorts, which could later be used to assess their sensibility regarding aspects such as the sparsity of variables or specificities of patient disease patterns.
Factors Influencing Facial Paralysis Recovery: A Retrospective Cohort Study Using Electronic Medical Records
Authors: Ahn Eunkyoung , Choi Yujin , Lee Suji
Published: August 2024

This study aims to report on treatment outcomes and related factors in Bell’s palsy patients by analyzing Electronic Medical Records (EMR) data. To assess the impact on the duration until improvement in House-Brackmann grading post-onset, we conducted an analysis of inpatient records for Bell’s palsy patients admitted to a traditional Korean medicine hospital from 2018 to 2022. A total of 1049 subjects were included in the analysis. Contributing factors to a duration of more than 14 days until improvement in HB Grade included age 40 or above, male gender, an initial period of more than 7 days from onset to admission, a history of past recurrence, and the indication for adjunctive therapy. To generalize the adaptation of adjunctive traditional Korean medicine treatment indications to treatment outcomes, larger-scale studies encompassing a broader patient population will be necessary.
Intelligent Diabetes Telemonitoring for Patients on Insulin Therapy: A Randomized Controlled Trial Protocol
Authors: Thomsen Camilla H.N. , Nørlev Jannie T.D. , Hangaard Stine , Jensen Morten H. , Kristensen Sika N.S. , Aradóttir Tinna B. , Vestergaard Peter , Hejlesen Ole , Kronborg Thomas
Published: August 2024

Protocol for a three-arm randomized controlled trial investigating telemonitoring with decision support in insulin-treated people with type 2 diabetes.
An Integrated Pipeline for Phenotypic Characterization, Clustering and Visualization of Patient Cohorts in a Rare Disease-Oriented Clinical Data Warehouse
Authors: Chen Xiaoyi , Wang Junyuan , Faviez Carole , Wang Xiaomeng , Vincent Marc , Tsopra Rosy , Burgun Anita , Garcelon Nicolas
Published: August 2024

Rare diseases pose significant challenges due to their heterogeneity and lack of knowledge. This study develops a comprehensive pipeline interoperable with a document-oriented clinical data warehouse, integrating cohort characterization, patient clustering and interpretation. Leveraging NLP, semantic similarity, machine learning and visualization, the pipeline enables the identification of prevalent phenotype patterns and patient stratification. To enhance interpretability, discriminant phenotypes characterizing each cluster are provided. Users can visually test hypotheses by marking patients exhibiting specific keywords in the EHR like genes, drugs and procedures. Implemented through a web interface, the pipeline enables clinicians to navigate through different modules, discover intricate patterns and generate interpretable insights that may advance rare diseases understanding, guide decision-making, and ultimately improve patient outcomes.
Assessment of Follow-Up for Pulmonary Nodules from Radiology Reports with Natural Language Processing
Authors: Sugimoto Kento , Wada Shoya , Konishi Shozo , Okada Katsuki , Nakashima Keisuke , Matsumura Yasushi , Takeda Toshihiro
Published: August 2024

Radiology reports are an essential communication method for ensuring smooth workflow in healthcare. However, many of these reports are described in free text, and findings documented by radiologists may not be adequately addressed. In this study, focusing on pulmonary nodules, we evaluated whether cases in which radiologists described follow-up as recommended were receiving appropriate treatment. Reports recommending follow-up for pulmonary nodules were automatically extracted using natural language processing. In our evaluation, out of 10,507 reports, 1,501 cases (14.3%) were classified as “reports recommending follow-up for pulmonary nodules.” Among these, 958 cases underwent additional imaging tests within 400 days. From the remaining 543 cases, we randomly sampled 42 cases and conducted chart reviews by clinicians to confirm patient care status. Our assessment found that follow-up was not documented in 17 of the 42 cases (40.5%), indicating a high likelihood that appropriate care was not provided.
Understanding Emotional Dynamics in Autism Social Media Communities
Authors: Gabarron Elia , Dorronzoro Enrique , Rivera-Romero Octavio , Denecke Kerstin
Published: August 2024

Searches for autism on social media have soared, making it a top topic. Social media posts convey not only plain text, but also sentiments and emotions that provide insight into the experiences of the autism community. While sentiment analysis categorizes overall sentiment, emotion analysis provides nuanced insights into specific emotional states. The objective of this study is to identify emotions in posts related to autism and compare the emotions specifically contained in posts that include the hashtag #ActuallyAutistic with those that do not. Methods: We extracted a sample of X’ posts related to autism and used DistilBERT to assign one out of six emotions (sadness, joy, love, anger, fear, surprise) to each post. Results: We have analyzed a total of 414,287 posts, 98,602 (23.8%) of those included the hashtag #ActuallyAutistic. The most common expressed emotion was joy, which was expressed in 52.5% of the posts, followed by sadness, identified in 28.6% of the posts. 12% of the posts expressed fear, 4.9% reflected anger, 1.1% showed love, and 0.9% expressed surprise. Posts tagged as #ActuallyAutistic showed less joy (27.1% vs. 60.4% in posts without this hashtag, p<0.001) and more sadness (52.7% vs. 21.1% in those without the hashtag, p<0.001). Conclusions: The use of the hashtag #ActuallyAutistic is associated with a different emotional tone, characterized by less joy and more sadness. These results suggest the need for greater support and acceptance towards the autistic community, both online and in society in general. Insights from our study can be valuable for policy makers, health, educational or other programmes aiming at enhancing well-being, inclusiveness, improve services, and create a more compassionate and understanding atmosphere for autistic people.
A Microbiology Analytics Tool to Evaluate the Burden of Antibiotic-Resistant Infections
Authors: Grob Moritz , Watzinger Christian , Willinger Birgit , Thalhammer Florian , Koller Walter , Rappelsberger Andrea , Adlassnig Klaus-Peter
Published: August 2024

Leveraging the capabilities of a microbiological clinical analytics tool, this study delves into quantifying the public health impact of antibiotic-resistant bacteria. Focusing on eight predominant antibiotic-resistant bacteria, the study utilizes University Hospital Vienna’s data to calculate the burden of antibiotic-resistant infections in disability-adjusted life years. The results highlight the potential of extended analytics tools in epidemiological research and underscore the pressing challenge of antimicrobial drug resistance.
An Open Information Model-Based Repository for Sustainable Re-Use of Heterogeneous Pandemics Research Data
Authors: Scheuermann Kristina , Hufeland Philip , Haarbrandt Birger , Hanss Sabine , Joseph Mareike , Kohler Severin , Krefting Dagmar , Richter Jendrik , Tute Erik , Wolf Klaus-Hendrik , Marschollek Michael
Published: August 2024

The COVID-19 Research Network Lower Saxony (COFONI) is a German state network of experts in Coronavirus research and development of strategies for future pandemics. One of the pillars of the COFONI technology platform is its established research data repository (Available at https://forschungsdb.cofoni.de/), which enables provision of pseudonymised data and cross-location data retrieval for heterogeneous datasets. The platform consistently uses open standards (openEHR) and open source components (EHRbase) for its data repository, taking into account the FAIR criteria. Available data include both clinical and socio-demographic patient information. A comprehensive AQL query builder interface and an integrated research request process enable new research approaches, rapid cohort assembly and customized data export for researchers from participating institutions. Our flexible and scalable platform approach can be regarded as a blueprint. It contributes, to pandemic preparedness by providing easily accessible cross-location research data in a fully standardised and open representation.
Security and Privacy in Physical Activity Chatbots on Social Media: A Scoping Review
Authors: Larbi Dillys , Gabarron Elia , Zanaboni Paolo , Wynn Rolf , Årsand Eirik , Denecke Kerstin
Published: August 2024

Background and objective: Social media physical activity chatbots use both chatbots and social media platforms for physical activity promotion and, thus, could face privacy and security challenges inherent in both technologies. This study aims to provide an overview of physical activity chatbot interventions delivered via social media platforms, specifically focusing on security and privacy measures. Methods: We conducted a scoping review on this topic across 4 databases: PubMed, PsycINFO, ACM Digital Library, and IEEE Xplore. We extracted and summarised information on the author, population, country of study, social media platform, intervention, data processed (i.e. gathered or stored), and security/privacy measures. Results: Out of 1299 identified articles, 12 were included in the analysis reporting about 9 different chatbots. Although all chatbots applied data processing methods, only a few considered anonymisation. One paper stated compliance with the General Data Protection Regulations. Other studies enforced some verification procedures before chatbot use. Conclusion: Current research fails to adequately report security considerations in social media physical activity chatbot design. However, integration of chatbots into social media platforms seems to be declining, possibly due to security concerns.
Understanding WHO SMART Guidelines: Narrative Review of an Innovative Global Digital Health Approach
Authors: Saban Martin , Zavala Denise , Lopez Osornio Alejandro , Kaminker Diego , Díaz Martin , Rubinstein Adolfo , Esteban Santiago , Rizzato Lede Daniel A.
Published: August 2024

The growing challenges of healthcare systems pose a unique opportunity to leverage evidence-based digital health interventions. The WHO’s SMART (Standards-based, Machine-readable, Adaptive, Requirements-based, and Testable) guidelines represent a significant advancement in this domain. This paper aims to summarize SMART guidelines authoring and implementation process, drawing on a comprehensive literature analysis. Our findings highlight critical success factors for national implementation, including stakeholder engagement, customization to local contexts, and leveraging international standards and digital technologies. We conclude with recommendations for countries aiming to implement WHO SMART guidelines, underscoring the need for a multi-disciplinary approach and the potential challenges to be navigated.
Path-based and triplification approaches to mapping data into RDF: User behaviours and recommendations
Authors: Warren Paul , Mulholland Paul , Daga Enrico , Asprino Luigi
Published: August 2024

Mapping complex structured data to RDF, e.g. for the creation of linked data, requires a clear understanding of the data, but also a clear understanding of the paradigm used by the mapping tool. We illustrate this with an empirical study comparing two different mapping tools, in particular considering the likelihood of user error. One tool uses path descriptions, e.g. JSONPath or XPath, to access data elements; the other uses a default triplification which can be queried, e.g. with SPARQL. As an example of the former, the study used YARRRML, to map from CSV, JSON and XML to RDF. As an example of the latter, the study used an extension of SPARQL, SPARQL Anything, to query the same data and CONSTRUCT a set of triples. Our study was a qualitative one, based on observing the kinds of errors made by participants using the two tools with identical mapping tasks, and using a grounded approach to categorize these errors. Whilst there are difficulties common to the two tools, there are also difficulties specific to each tool. For each tool, we present recommendations which help ensure that the mapping code is consistent with the data and the desired RDF. We propose future developments to reduce the difficulty users experience with YARRRML and SPARQL Anything. We also make some general recommendations about the future development of mapping tools and techniques. Finally, we propose some research questions for future investigation.
Ensuring Quality in Clinical Research: The Impact of Quality Assurance and Quality Control in the Field of Good Clinical Practice
Authors: Petrushevska Marija , Zendelovska Dragica , Atanasovska Emilija
Published: August 2024

Good Clinical Practice (GCP) compliance delivers assurance that the study participants' safety is protected and that the obtained data are legitimate and credible. One of the tools to obtain quality in clinical research is to assure robust quality assurance and quality control as part of the quality management system. This is considered essential for sponsors to assure that the data from the clinical trial have integrity and reliability. One of the purposes of this article was to provide information to allow clinical research, quality assurance professionals, academics, and members of ethics committees to stay up to date with clinical research and good practice developments. This article investigates the enormous value of quality assurance and control in clinical trials, highlighting their pivotal role in ensuring the integrity, safety, and efficacy of research findings.
Bioconversion of Coffee Fruit Bark Waste into Commercially Valued, Healthy, and High-Quality Compost Fertilizer as a Solution for Community Economic Independence and Environmental Sustainability
Authors: Rozi Maulana Fahrul , Reswita Reswita , Bahri Samsul , Faris Ahmad , Mushlih M. Habib , Ansori Ahmad , Chaknur Budi , Alvina Janna
Published: August 2024

Coffee is one of the plantation commodities in Indonesia which has quite high economic value among other plantation commodities such as cocoa and tea. Ulubelu District is one of the largest coffee producing areas in Lampung Province with a land area of 7,549 hectares with a production capacity of 4,970 tons/year which has the potential to produce 7.8 tons/year of coffee fruit skin waste. So there is a need for a solution to process coffee fruit skin waste into a product that has sales value and use value for the community. The aim of this community service activity is as an alternative to increasing community knowledge and skills in utilizing local potential into local products. The method applied in this service is to carry out several stages such as pre-activity, outreach, implementation and evaluation of activities. The result of carrying out this service activity is an increase in the community's knowledge and skills in utilizing and processing the potential of coffee fruit skin waste into healthy and quality compost. Apart from that, the aim of carrying out this service is to create a new business community so that it can improve community welfare from the community's ability to utilize local potential into local products that have marketable value.
Financial Feasibility of Patin Fish Fillet Processing Business in Belitang District, East OKU Regency
Authors: Nasir Nasir , Sabrina Leni
Published: August 2024

This research aims to: 1) Calculate the investment value and operational costs, receipts and income of the catfish fillet processing business, and 2) Analyzing the financial feasibility of the catfish fillet processing business. The research was carried out in Belitang District, East OKU Regency, South Sumatra Province from July to August 2023. The population in this study were owners/managers of fish fillet making businesses, 12 fish farmers. This research uses a survey method.  The data collected consists of primary and secondary data. Sampling uses a saturated sampling method. Data collection was carried out using direct interviews with fish fillet processing industry players and catfish cultivating farmers. The data processing method uses quantitative analysis by calculating investment, operational, income and revenue costs. Financial feasibility is carried out through NPV, IRR, B/C Ratio and Payback Period analysis.  The research results provide conclusions: 1) The fish processing business is profitable in both the long term (10 years and the short term (1 year). During the economic life period (10 years), the total revenue is IDR 60,793,991,291.41,-, the total costs incurred are IDR 44,786,986,293.77 (investment costs IDR 1,781,010,000,- and operational costs IDR 43,005,976,293) so that income net IDR 16,007,004,997.64,-.  In the first (1st) year, total revenues were IDR 6,804,000,000,-, total costs IDR 6,013,629,500,- (investment depreciation costs IDR 148,417,500,- and operational costs IDR 5,865,212,000,-) so that income net amounting to IDR 790,370,500,- 2) The catfish fillet processing business is feasible to carry out with an NPV value of IDR 16,007,004,997.64, a B/C value of 0.35 and an R/C of 1.35, an IRR value of 82.09% and an investment payback period of 1.9 years  (1 year, 10 months and 4 days).
Comparative Feasibility of Shifting Cultivation and Direct Seed System Rice Farming in Yaba Maru Village, Tanah Miring District, Merauke Regency, South Papua Province
Authors: Sutarman Sutarman , Ginting Nina Maksimiliana , Widyantari Ineke Nursih
Published: August 2024

The research objective was to compare production costs, revenue, income, and farm feasibility using the transplanting system method (tapin, a traditional method of planting rice seedlings) and direct seed planting system (tabela, a modern method of planting rice seeds directly into the field) in Yaba Maru Village, Tanah Miring District. The research location was chosen purposively by considering Yaba Maru Village as a rice center. The research population was 611 farmers, and the number of research samples was 80 farmers consisting of 40 tapin farmers and 40 tabela farmers. The data used in the study were primary data from observation, documentation, and questionnaires, as well as secondary data from BPS, village heads, literature, and the internet. The data analysis method used quantitative descriptive analysis. The results showed that the average total cost incurred by the Tapin system was higher than that of the Tabela system. The tapin system cost was Rp 15,714,237, consisting of fixed costs of Rp 3,378,353 and variable costs of Rp 12,335,884, and the tabela system was Rp 13,989,800 consisting of fixed costs of Rp 3,667,588, and variable costs of Rp 10,322,213. Farming receipts for the tapin system are higher than those for the tabela system, at Rp 29,061,450 for tapin and Rp 26,670,600 for tabela. The income received by the tapin system rice farming is higher than the tabela system, namely 13,347,213 for tapin and Rp 12,680,800 for tabela. The farm feasibility value of the tabela system is higher than the tapin system, with an R/C ratio of 1.91 for the tabela system and 1.85 for the tapin system.
The Role of Women in Farming Activities and the Coffee Supply Chain in Cigalontang District, Tasikmalaya Regency
Authors: Apriyani Dwi , Cahrial Eri , Rofatin Betty , Risanto Bahar Rizki
Published: August 2024

Gender involvement in agricultural cultivation cannot be avoided, even though it has the risk of leading to inequality and even injustice. Few ordinary people understand the importance of gender equality and dynamics in the agricultural sector. The research aims to describe the characteristics of coffee farmers in Cigalontang District and map the allocation of women's work time in social, economic and domestic activities in coffee farming. The research method used is qualitative descriptive analysis and mathematical formulas for calculating women's work time in the economic, social and domestic fields, according to the Harvard framework. The research results show that the majority of coffee farmers are over 50 years old with the majority's education level being elementary school graduates. The land ownership status is mostly sharecroppers with an average of 0.18 ha to 2.6 ha of total cultivated land. Meanwhile, most farmers have 2.5 to 8.4 years of cultivation experience. As for the time spent by women coffee farmers as housewives, the most dominant time is allocated to domestic activities. Meanwhile, coffee farmers who work as traders and service providers spend most of their time on economic activities. Women coffee farmers with trading and service professions allocate less time for social activities than women coffee farmers who work as housewives.
Analysis of Water Quality and Plankton Biodiversity in Conservation Area of PT PHE Raja Tempira
Authors: Muslimin Boby , Khotimah Khusnul , Nizar Muhammad , Helmizuryani Helmizuryani , Yowono Henri , Pinto Neri Wahyu , Sundara Alhusna Indra , Abdul Latif Al-Ansori Muhamamd , Heryadi Heryadi
Published: August 2024

Identification of water quality before fish farming is needed to determine the characteristics of water as a medium for freshwater fish cultivation. This study investigated water quality parameters for pH, temperature, Dissolve Oxygen (DO), Total Dissolved Solid (TDS), types and abundance of phytoplankton and zooplankton in the conservation area of PT PHE Raja Tempirai, Penukal Abab Lematang Ilir Regency (PALI), South Sumatra. Water samples were collected from four stations with codes A0, D1 and D2 in the form of earthen ponds and A1, a tributary of the Muara Air Itam river and ex-situ analyzed at laboratory of aquaculture study program, Sriwijaya University and Center for Environmental Health and Disease Control Engineering (BTKLPP) Class I Palembang. All stations have categories very suitable for DO (>6 mg/L) and temperature (29-32 °C) parameters. Station A1 is categorized as less feasible at pH and TDS (5.06±0.72 and 6.80±0.00 mg/L), stations D1 and D2 are suitable for cultivation at pH parameters (6.36±0.11 and 6.71±0.17), and less feasible at TDS (38.63±0.03 mg/L and 34.50±1.90 mg/L). While at station A0 it is very possible on all parameters, but phytoplankton of the genus Peridinium at this station dominate, so in the future, it is necessary to mitigate the abundance of this type of phytoplankton to avoid water quality degradation, especially in different seasons.
Intensity and Incidence of Pest Disease Attacks on Rice Plants in Enggal Rejo Village, Air Salek Subdistrict
Authors: Wagiyanti Wagiyanti , Hamidson Harman , Suwandi Suwandi
Published: August 2024

Rice (Oryza sativa L.) is the main food crop widely cultivated by the people of Indonesia which has an important role in economic improvement. However, in the cultivation of rice plants, there are obstacles to the attack of pests and diseases that can reduce productivity both in quality and quantity. The purpose of this study was to determine the main pests and diseases that exist in rice plants and some incidence and intensity of attacks to minimize the control process. The research was conducted from November 2023 to February 2024. The observation method used diagonal sampling with 5 subplot points with one plot of 10 clumps of rice plants. Data were analyzed descriptively. The results showed that the main pest that attacked rice plants was the Rice Stem Borer with an attack rate of 13% categorized as a mild attack. While the main diseases found are panicle blast in the medium category, and leaf blast with the level of attack there are various phases of rice plant growth with mild to moderate categories, and midrib rot with mild categories.
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Authors: Muka Cover
Published: August 2024

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Pengelolaan Konten Media Sosial Sebagai Sarana Pemasaran Usaha Kecil Warga Kampung Anyar, Kabupaten Bogor
Authors: Hamzah Radja Erland , Sungkono Nono , Santoso Prasetya Yoga
Published: August 2024

Community service activities aim to empower the community through social media content management training for marketing tools. Providing information to the public how big the role of social media is as a tool in marketing. This training activity was carried out in Tegal Village, Kemang District, Bogor Regency. Most of the residents who attended owned small businesses so this training was effective because it targeted people who wanted to take advantage of social media to increase online sales. The method used in this activity is in the form of lectures and training in using social media such as Facebook and Instagram. Residents are also taught how to use the Whatsapp Business application for free. The results of the activity provide sufficient benefits to have an impact on the use of social media marketing which is a medium that has low costs and is very effective in disseminating information online in marketing small businesses belonging to the people of Kampung Anyar, Desa Tegal, Bogor.
Implementasi Social Media Optimization (SMO) Bagi Perusahaan Swasta di Jakarta
Authors: Putri Citra Eka
Published: August 2024

SMO (Social Media Optimization) is one of the methods used to increase online traffic targeted at social media activities. SMO is one way for SMM (Social Media Marketing) to attract visitors through social media activities. But unfortunately the management of social media by utilizing SMO is still not ideal among many social media managers, especially business owners in Indonesia. The purpose of this activity is to provide training for companies that focus on managing social media among private companies in Jakarta to be able to improve the optimization and performance of their social media. The material presented is related to how to optimize social media, create SMO strategies, focus on visuals on social media, optimize profiles, optimize content, optimize hashtags, create content production timelines, monitor social media performance, utilize social media insight metrics and optimize human resources by creating KPI managers. social media. The results of this training social media managers are able to understand how to optimize social media, be able to directly manage and create quality product content, understand and understand reading social media graphics, be able to make progress reports or insights on social media, and manage social media professionally able to create product content timelines on social media.