bluuebunny/crossref_metadata_2025 huggingface dataset:
Reconnecting People with Dementia by Using the Interactive Instrument CRDL
Authors: Teunissen Lisanne  , Luyten Tom  , de Witte Luc
Published: February 2025

Dementia is a progressive brain disease with a decline in functioning over time. CRDL (pronounced as Cradle) is an interactive instrument, developed to stimulate communication between users through sound and touch. It recognises the intention of the touch (tickle, holding, grabbing, fondling, kneading, and tapping) and will adjust the sound accordingly. This qualitative observational study explores whether this innovative product has the potential to be used in elderly care to create a moment of interaction between the person with dementia and someone else or multiple others. In a nursing home facility in the south of the Netherlands CRDL has been studied in one-to-one interaction between a person with dementia and a member of their family, a healthcare professional or a recreational therapist (n = 7), and group interaction between several people with dementia and a healthcare professional or a recreational therapist (n = 5). In the one-to-one interaction study professionals and family members reported that the interaction with CRDL and the person with dementia was a pleasant experience. They also mentioned that it felt familiar and they had the feeling that they were in better contact with the person with dementia. The exit interviews with the professionals and family members indicate that CRDL has the potential to encourage the interaction between the healthcare professional, recreational therapist or family member and the person with dementia. Healthcare professionals and recreational therapists who used CRDL in a group interaction session reported participants were generally fascinated by the object itself, but needed active encouragement to interact. The role of the healthcare professional, recreational therapist or family member seems very important.
Engineering Cyber-Physical Systems
Authors: Gruhn Volker  , Gries Stefan  , Hesenius Marc  , Ollesch Julius  , ur Rehman Shafiq  , Schwenzfeier Nils  , Wahl Christian  , Wessling Florian
Published: February 2025

Connecting digital information systems with real world objects and processes is the core of the digital transformation. New business models and opportunities thrive on the various options that the resulting information offers. Cyber-Physical Systems (CPS) are the most prominent incarnation and – in contrast to various other aspects of the digital transformation – really new: sensors and actors allow information systems to monitor the real world and will profoundly change most markets and business domains. However, developing CPS involves different specialists and various technical challenges. Software and hardware engineers, network specialists, and data scientists have to work hand in hand and combine their specialties to incorporate the different perspectives into one team. We present EngCPS, an engineering approach to develop CPS that enhances classic software engineering methods with CPS-specific extensions.
Twitter Hate Aspect Extraction Using Association Analysis and Dictionary-Based Approach
Authors: Zainuddin Nurulhuda  , Selamat Ali  , Ibrahim Roliana
Published: February 2025

Recent research regarding hate speech is in the domain of social sciences and psychology. From these trends, the dissemination of hate speech and antagonistic content in social media has not been extensively studies from the perspective of sentiment analysis. In this paper, the main studies concerned about aspect-based sentiment analysis through twitter as the most popular social media communication in the world as they have 313 million active users worldwide. This paper initiate to address the shortcomings of implied aspects specific for hate crime domain. The expected beneficial hate aspects can be extracted from twitter based on combination of both analysis. The evaluation with researcher's own Hate Crime Twitter Sentiment (HCTS) dataset and also Hate Speech Twitter Datasoft (HSTD) was shown that the proposed approach is effective and produces significantly better results than baselines method.
Usability Prioritization Using Performance Metrics and Hierarchical Agglomerative Clustering in MAR-Learning Application
Authors: Cheng Lim Kok  , Selamat Ali  , Zabil Mohd Hazli Mohamed  , Selamat Md Hafiz  , Alinda Alias Rose  , Puteh Fatimah  , Mohamed Farhan  , Krejcar Ondrej
Published: February 2025

This paper highlights the current literatures in usability studies, performance metrics and machine learning algorithm. A literature review is done in these three areas of studies to find a research gap that can be explored further. The paper will then propose a research methodology to attend to the issues of machine learning and usability. An experiment is proposed to compare the efficiency results in between data consistency, correlation between performance metrics and self-reported metrics of a Mobile Augmented Reality learning application. The methodology proposes hierarchical agglomerative clustering technique as a solution in differentiating usability issues according to priority in order to help with usability re-engineering decisions. This paper proposes two objectives through the proposed framework and present evidence on how to achieve them. Lastly, this paper will discuss the results, conclusion and future works of the proposed study.
Using Contextual Information in Music Playlist Recommendations
Authors: Gatzioura Anna  , Sànchez-Marrè Miquel
Published: February 2025

Recommender Systems have become a fundamental part of various applications supporting users when searching for items they could be interested in, at a given moment. However, the majority of Recommender Systems generate isolate item recommendations based mainly on user-item interactions, without taking into account other important information about the recommendation moment, able to deliver users a more complete experience. In this paper, a hybrid Case-based Reasoning model generating recommendations of sets of music items, based on the underlying structures found in previous playlists, is proposed. Furthermore, the described system takes into account the similarity of the basic contextual information of the current and the past recommendation moments. The initial evaluation shows that the proposed approach may deliver recommendations of equal and higher accuracy than some of the widely used techniques.
iND: A Significant Improvement of the Nearness Diagram Method for Reactive Mobile Robot Navigation
Authors: Espinosa Miller Stiven  , Antich Javier  , Ortiz Alberto
Published: February 2025

Navigation is an essential task for any mobile robot, whose primary focus is to guide the robot from its initial position to a specific target position while avoiding collisions with obstacles along the way. When talking about reactive navigation, we are referring to the same task described above, but now some restrictions are added to the way in which that task can be performed. Basically, a robot which navigates reactively makes decisions using only the information that, at that moment, its sensors collect. Acting that way, these robots are able to react quickly to any unexpected event (for instance, an obstacle). Unfortunately, the reactive navigation paradigm also has limitations, the most important being its inability to make robots avoid certain obstacles. In this work, we take as our starting point one of the best algorithms to date to make a robot navigate reactively; specifically, this algorithm is the well-known Nearness Diagram (ND). We modify ND to provide it with the ability to overcome complex obstacles, i.e. obstacles of great size and with intricate shapes. Moreover, this modification is carried out without losing the reactive nature of the ND algorithm. improved Nearness Diagram (iND) is the name given to the algorithm resulting from this modification. We test iND under simulation in a set of environments of increasing complexity, and we compare its results with those obtained by two other reactive navigation algorithms: namely, the Virtual Force Field (VFF) algorithm and the original ND algorithm.
Integration of Different Fuzzy Rule-Induction Methods to Improve the Classification of Patients with Diabetic Retinopathy
Authors: Saleh Emran  , Valls Aida  , Moreno Antonio  , Romero-Aroca Pedro
Published: February 2025

A 10% of diabetic people may suffer from a disease called Diabetic Retinopathy that destroys the retina causing blindness. An early detection of any sign that indicates a risk of developing DR is important, because there are treatments that can significantly improve the quality of life and minimize the risk of vision loss. Very few work exists on exploiting the information stored in the Electronic Health Record for this purpose. In this work, we propose the integration of two machine learning methods that generate a set of fuzzy classification rules. We show that the combination of two methods improves the classification quality indices.
Classifier Selection with Permutation Tests
Authors: Arias Marta  , Arratia Argimiro  , Duarte-López Ariel
Published: February 2025

This work presents a content-based recommender system for machine learning classifier algorithms. Given a new data set, a recommendation of what classifier is likely to perform best is made based on classifier performance over similar known data sets. This similarity is measured according to a data set characterization that includes several state-of-the-art metrics taking into account physical structure, statistics, and information theory. A novelty with respect to prior work is the use of a robust approach based on permutation tests to directly assess whether a given learning algorithm is able to exploit the attributes in a data set to predict class labels, and compare it to the more commonly used F-score metric for evaluating classifier performance. To evaluate our approach, we have conducted an extensive experimentation including 8 of the main machine learning classification methods with varying configurations and 65 binary data sets, leading to over 2331 experiments. Our results show that using the information from the permutation test clearly improves the quality of the recommendations.
Automated Transformation of CDISC ODM to OpenClinica
Authors: Gessner Sophia  , Storck Michael  , Hegselmann Stefan  , Dugas Martin  , Soto-Rey Iñaki
Published: February 2025

Due to the increasing use of electronic data capture systems for clinical research, the interest in saving resources by automatically generating and reusing case report forms in clinical studies is growing. OpenClinica, an open-source electronic data capture system enables the reuse of metadata in its own Excel import template, hampering the reuse of metadata defined in other standard formats. One of these standard formats is the Operational Data Model for metadata, administrative and clinical data in clinical studies. This work suggests a mapping from Operational Data Model to OpenClinica and describes the implementation of a converter to automatically generate OpenClinica conform case report forms based upon metadata in the Operational Data Model.
Decision Support Systems in Health Care – Velocity of Apriori Algorithm
Authors: Somek Mario  , Hercigonja-Szekeres Mira
Published: February 2025

The amount of stored data in health information systems can reach tera- and petabytes and application of specific algorithms in the field of data mining makes finding useful information suitable for making quality business decisions. A frequently used method for determining the rules of the relationship between attributes is the Association rule by applying Apriori algorithm. Lack of basic Apriori algorithm is derived from the slow work due to multiple scanned data sets. By examining the speed of generating the basic rules in relation to the improved Apriori algorithm by using software RapidMiner confirmed that the time required to generate rules for Improved algorithm is shorter, the rules are quickly generated particularly for large data sets, which is an advantage for making decisions.
Asymptotic Behaviors of a Fuzzy Predator-Prey Model
Authors: Zhang Qianhong  , Wang Guiying  , Zhong Xiaoying
Published: February 2025

In this paper, a fuzzy predator-prey system is proposed by adopting fuzzy parameter in a predator-prey system. The steady state and linear stability of predator-prey system are determined and analyzed. Here, we show that the trivial steady state is unstable. Meanwhile the semi trivial steady state is locally asymptotically stable for all values of α under certain conditions.
The Research and Design of Question-Answering System
Authors: Song Bo  , Zhuo Yue
Published: February 2025

With the continuous development of the network, Question-Answering system has become a way for people to get information quickly. The QA task aims to provide precise and quick answers to user questions from a collection of documents or a database. In this paper, we briefly introduce the QA system and its related sentence similarity methods, including TF-IDF and sentence similarity computing based on semantic dependency. Experimental results show that the accuracy of the second methods is higher. In addition the QA system framework based on a specific domain is designed.
Deep Learning for Real Credit Card Data
Authors: Niimi Ayahiko
Published: February 2025

In this study, two major applications are introduced to develop advanced deep learning methods for credit card data analysis. Credit card information is contained in two data sets; credit approval dataset and card transaction dataset. The credit card dataset has two problems. One problem is using credit card approval dataset, it is necessary to combine multiple models, each referring to a different clustered group of users. The other problem is using card transaction dataset, since the actual unauthorized credit card use is very small, these imprecise solutions do not allow the appropriate detection of fraud. To solve these problems, we proposed deep learning algorithm to apply credit card dataset. The proposed methods are validated using benchmark experiments with other machine learnings. To evaluate our proposed method, we use real credit card transaction dataset from real system. The proposed methods are validated using large scale transaction dataset. Deep learning parameter adjustment is difficult. By optimizing the parameters, it is possible to increase the learning accuracy. The proposed method was able to confirm high discrimination accuracy even with less training data. Since fraud detection accuracy for each dataset is not stable, over fitting for datasets needs to be avoided. In addition, it is necessary to consider the calculation cost of deep learning.
Performance Modeling of Stochastic Loss Bound with Multi-Flow Fuse
Authors: Zhang Yi  , Zhu Shan  , Chen Cheng  , Liu Hong-Xin  , Wang Zhu-Juan
Published: February 2025

In the stochastic networks of multi-flow fuse, some intermediate node code information before forwarding them. From the source to the destinations, the coding service can achieve the maximum throughput and network capacity. However, the analysis of the burstiness in multi-flow fuse service is almost a blank. Without such core performance modeling, the service superiority of fuse under different scene is hard to show it. We design a method of performance analysis (i.e., loss) by the min-plus operators model. We prove the loss bound probability under network coding service. The target bound includes arrival traffic, service traffic and loss packets, respectively.
A RBF Neural Network Based Data Aggregation Algorithm for Wireless Sensor Networks
Authors: Wang Jin  , Wang Kai  , Cao Yiquan  , Younn Geumran  , Kim Jeong-Uk
Published: February 2025

In wireless sensor networks (WSNs), the massive node density and the diverse sensory placement will produce huge amount of sensory traffic with redundancy. This will lead to the decline of scarce network resources and will decrease lifetime of WSNs. Data aggregation is a useful way to save energy consumption and prolong lifetime of sensor network. In this paper, we propose a radial basis function (RBF) neural network based data aggregation algorithm for wireless sensor networks. In this algorithm, an improved three-layer RBF neural network is firstly applied to each cluster where nodes in each cluster are used as input layer neuron to process the preliminary data. Secondly, processing results are transferred to the cluster head. Based on the function of hidden layer neuron and output layer neuron, cluster head waits further processing and finally sends the characteristic value to remote sink node. Extensive simulation results show that energy consumption is largely reduced, network lifetime is much prolonged, and the accuracy of data aggregation is improved in our proposed algorithm than some other popular algorithms.
Interference Suppression Based on Joint Time-Frequency Analysis and Fractional Fourier Domain in LFM HF Radar
Authors: Chen Zezong  , Xie Fei  , Zhao Chen  , He Chao
Published: February 2025

Radar signal is always contaminated by interferences in the high-frequency (HF) band because of the overcrowded electromagnetic environment today. This problem is more serious for linear frequency modulation (LFM) HF radar. In this paper, we put forward a three-step method to solve this problem. The new method detects interference based on time-frequency analysis technique, transforms the signal to fractional Fourier domain (FRFD), and finally suppresses the interference. Comparison between the proposed method and the traditional N-sigma method is given using simulation, and the results show that the proposed method is superior to the N-sigma method. Experimental results that the new method is applied for multi-frequency HF radar to obtain indicate that it can effectively suppress the interference with a SNR gain of more than 10 dB.
Identifying and Synchronizing Health Information Technology (HIT) Events from FDA Medical Device Reports
Authors: Kang Hong  , Wang Frank  , Zhou Sicheng  , Miao Qi  , Gong Yang
Published: February 2025

Health information technology (HIT) events, a subtype of patient safety events, pose a major threat and barrier toward a safer healthcare system. It is crucial to gain a better understanding of the nature of the errors and adverse events caused by current HIT systems. The scarcity of HIT event-exclusive databases and event reporting systems indicates the challenge of identifying the HIT events from existing resources. FDA Manufacturer and User Facility Device Experience (MAUDE) database is a potential resource for HIT events. However, the low proportion and the rapid evolvement of HIT-related events present challenges for distinguishing them from other equipment failures and hazards. We proposed a strategy to identify and synchronize HIT events from MAUDE by using a filter based on structured features and classifiers based on unstructured features. The strategy will help us develop and grow an HIT event-exclusive database, keeping pace with updates to MAUDE toward shared learning.
Comparative Performance Analysis of Different Fingerprint Biometric Scanners for Patient Matching
Authors: Kasiiti Noah  , Wawira Judy  , Purkayastha Saptarshi  , Were Martin C.
Published: February 2025

Unique patient identification within health services is an operational challenge in healthcare settings. Use of key identifiers, such as patient names, hospital identification numbers, national ID, and birth date are often inadequate for ensuring unique patient identification. In addition approximate string comparator algorithms, such as distance-based algorithms, have proven suboptimal for improving patient matching, especially in low-resource settings. Biometric approaches may improve unique patient identification. However, before implementing the technology in a given setting, such as health care, the right scanners should be rigorously tested to identify an optimal package for the implementation. This study aimed to investigate the effects of factors such as resolution, template size, and scan capture area on the matching performance of different fingerprint scanners for use within health care settings. Performance analysis of eight different scanners was tested using the demo application distributed as part of the Neurotech Verifinger SDK 6.0.
Evaluation of a Decision Support System for Laboratory Service Patients
Authors: Kopanitsa Georgy
Published: February 2025

In Russia, many patients address laboratory services directly without a doctor's referral. This causes the problem of interpretation of laboratory test results by the patients who don't have a proper medical background. We have a situation when there is no healthcare professional between a patient and the test results. This problem can be solved if a laboratory service provides not only the results of the tests but also their interpretations. The system was implemented in the Helix laboratory service in Saint-Petersburg, Russia. At the moment it generates about 3500 reports a day. This study presents the evaluation of the system, which has shown a good performance both on correctness of generated reports and user acceptance.
User-Centered Design Improves the Usability of Drug-Drug Interaction Alerts: A Validation Study in the Real Scenario
Authors: Luna Daniel R.  , Rizzato Lede Daniel A.  , Rubin Luciana  , Otero Carlos M.  , Ortiz Juan M.  , García Mónica G.  , Rapisarda Romina P.  , Risk Marcelo R.  , González Bernaldo de Quirós Fernán
Published: February 2025

Decision support systems can alert physicians to the existence of drug interactions. The Hospital Italiano de Buenos Aires, Argentina, has an in-house electronic health record with computerized physician order entry and clinical decision support. It includes a drug-drug interaction alert system, initially developed under traditional engineering techniques. As we detected a high alert override rate, we rebuilt the knowledge database and redesigned the alert interface with User-Centered Design techniques. A laboratory crossover study using clinical vignettes showed that new alerts were more usable than traditional ones.This paper aimed to validate these results through a controlled and randomized experimental study with two branches (old vs. new design) in a real setting. We analyzed, quantitatively, every fired alert between April 2015 and September 2016. Finally, we performed user surveys and qualitative interviews to inquire about their satisfaction and perceptions.In real scenarios, user-centered design alerts were more usable, being more effective and satisfactory, but less efficient than traditional alerts. “Safe omission”, as a new concept, emerged from our stratified analyses and interviews.
Extraction and Quantification Clusters of Three-Dimensional Lorenz Plots
Authors: Hu Min  , Han Weixing  , Liu Jun  , Jang Chengfan  , Wang Suxia
Published: February 2025

Lorenz plot (LP) method gives a global view of long-time electrocardiogram signals, is an efficient simple visualization tool to analyze heart rate variability and cardiac arrhythmias. 50 records of RR-interval time series with frequent premature complexes exported from 24-hour Holter. Constructed three-dimensional LP (3DLP) with three successive RR intervals as X, Y and Z axis in Cartesian coordinate system for each record, then stereographic projection it along the space diagonal. The radii of dots distinguished eccentric clusters (ECs) points from centric clusters' with the accuracy of 94 ± 6.0%. The eccentric scatter-dots were separated and identified by the frequency distribution characteristic on azimuth, with the accuracy of 93 ± 13.3% ECs. The APF< −2.8° of CPN EC supported the ventricular extrasystoles diagnosis, with excellent sensitivity (1.0) and specificity (0.92). The transformed coordinates (polar radius and angle) of 3DLP could extract and quantify clusters to diagnose arrhythmia, and might provide additional prognosis information.
The Impact of Censoring Drug Switching in Medication Adherence Measures of Chronic Single Ingredient Oral Drugs
Authors: Zhu Vivienne J.  , Overhage J. Marc  , Ma Qianli  , Ryan Patrick B.
Published: February 2025

We explored how drug switching impacts adherence measures for common chronic oral medications. Switching between ingredients with the same indication was detected within a 30-day grace period. The proportion of days covered (PDC) and adherent status (cutoff 0.8) for each ingredient was calculated and compared between different censoring approaches: censoring drug switching (PDCswitch), censoring the end of dispensing (PDCend), and fixed 365-day period (PDC365). Overall, 854,380 (15.9%) patients in the Optum ClinFormatics (Optum) and 150,785 (22.0%) patients in the MarketScan Multi-state Medicaid (MDCD) had at least one switch within one year. Compared with PDC365 in Optum, PDCswitch means were higher: 0.85 vs. 0.41 for antihypertensive, 0.82 vs. 0.46 for antihyperglycemics, and 0.84 vs. 0.33 for antihyerlipidemia. Further, the percentages of adherent patients were higher: 95.8% vs. 17.9% for antihypertensive, 85.5% vs. 18.9% for antihyperglycemics, and 72.1% vs. 5.3% for antihyerlipidemia. Significant and modest changes were observed between PDCswitch and PDCend.
Development and Deployment of an e-Health System in UNRWA Healthcare Centers (HCs): The Experience and Evidence
Authors: Ballout Ghada  , Al-Shorbaji Najeeb  , Shishtawi Amin  , Abuzabaida Fathia  , Shahin Yousef  , Khader Ali  , Zeidan Wafa'a  , Turki Yassir  , Al-Natour Ahmad  , Saadeh Rawan  , Hababeh Majed  , Seita Akihiro
Published: February 2025

UNRWA recognized the need to develop and introduce eHealth programme based on needs assessment. The web-based e-Health application was developed and deployed in 116 out of 143 HCs in the five fields of operation. Evaluation of the application revealed positive outcomes in terms of reporting requirements, statistics, time saving, efficiency, drug compliance and workflow in general.
Use of Person-Generated Health Data in Kinect-Based Stroke Rehabilitation Systems: A Systematic Review
Authors: Dimaguila Gerardo Luis  , Gray Kathleen  , Merolli Mark
Published: February 2025

It is important for patients to have access to the health information they generate, for them to be actively engaged in their own healthcare. This is relevant to Kinect-based post-stroke rehabilitation systems; as such there is a need to review the literature based on person-generated management and utilisation. Previous systematic reviews on Kinect for stroke rehabilitation have not used this as part of their criteria. This systematic review fills that gap.
How Are Doctors Using Mobile Electronic Medical Records? An In-Depth Analysis of the Usage Pattern
Authors: Kim Juntae  , Lee Yura  , Lim Sanghee  , Lee Jae-Ho
Published: February 2025

In recent years, major hospitals have started using mobile electronic medical records (m-EMR). However, usage patterns based on actual log data have rarely been discussed. In this study, we investigated usage patterns of a m-EMR in several dimensions to understand the m-EMR in depth.
A Content Analysis of Mobile Apps for Chronic Kidney Disease Patient Care: Searching in English and Chinese
Authors: Lee Ying-Li  , Cui Yan-Yan  , Chang Polun
Published: February 2025

Chronic kidney disease (CKD) is a global public health issue. Mobile technology is pervasive and widely used in chronic disease care. More and more, CKD mobile applications (apps) can be found on popular mobile application platforms, especially in Chinese. We aim to explore current mobile apps for CKD patient care (searching in English and Chinese) through content analysis to identify the app functions that health professionals can use in CKD patient care and to help CKD patients with self-management.
Personal Health Self-Management in a Data Perspective
Authors: Yu Yiqin  , Li Xiang  , Hu Gang  , Liu Haifeng  , Mei Jing  , Ni Yuan  , Tang Guoyu  , Xie Guotong  , Xu Weiming
Published: February 2025

Along with the growth of numbers of patients with chronic diseases, personal health self-management becomes critical. The heterogeneity of self-management requirements makes the detail design and implementation of self-management program a non-trivial work. In this paper we address the problem with the Personal Health Advisor (PHA) application by introducing a personal health data flow mechanism, as well as modules including personal health risk assessment, similar patients profiling, and health question answering.
Modeling an IT Support for Handling Serious Adverse Events in Clinical Trials
Authors: Fonck Simon  , Deserno Thomas
Published: February 2025

Serious adverse events (AE) or reactions (AR) may occur in clinical trials and require particularly regulated reporting. Manual management is inefficient and ineffective. Based on a description of regulations, we have developed a data model with class-, state-, use-case- and activity diagrams, which can be used for automatic code generation of an assisting software tool for AE / AR data management.
Generation of openEHR Test Datasets for Benchmarking
Authors: El Helou Samar  , Karvonen Tuukka  , Yamamoto Goshiro  , Kume Naoto  , Kobayashi Shinji  , Kondo Eiji  , Hiragi Shusuke  , Okamoto Kazuya  , Tamura Hiroshi  , Kuroda Tomohiro
Published: February 2025

openEHR is a widely used EHR specification. Given its technology-independent nature, different approaches for implementing openEHR data repositories exist. Public openEHR datasets are needed to conduct benchmark analyses over different implementations. To address their current unavailability, we propose a method for generating openEHR test datasets that can be publicly shared and used.
Patient's Adherence Level Determination System
Authors: Maksim Taranik  , Kopanitsa Georgy
Published: February 2025

The present paper deals with intellectual systems for patient's level adherence determination. The most popular novadays technique for adherence evaluation is interview. We advise new automation approach, which compares direct pation data with treatment plan using fuzzy logic and stanard ISO 13606. For this research data entered by users. The results can be useful for medical organisation with lind-term patients.
Prerequisites for International Exchanges of Health Information for Record Research: Comparison of Australian, Austrian, Finnish, Swiss, and US Policies
Authors: Suominen Hanna  , Müller Henning  , Ohno-Machado Lucila  , Salanterä Sanna  , Schreier Günter  , Hanlen Leif
Published: February 2025

The policies that address health information exchanges for research purposes in Australia, Austria, Finland, Switzerland, and the USA apply accountability and/or adequacy to protect privacy. Specific requirements complicate the exchanges: inform data subjects of data use purposes; assure that the subjects are no longer identifiable; destroy the data in the end; and not to use cloud computing without specific permission.
Secure Electronical Communications and Data Transfers in a Clinical Environment
Authors: Xu T.  , Thiemann V.S.  , Röhrig R.  , Brammen D.  , Majeed R.W.
Published: February 2025

As part of the German Project AKTIN, data security arises as an important issue. The context of this issue was detailed and the requirements were determined, with special focus on the hospital's point of view. The solution is illustrated in this poster. Further use cases in other medical or study context are also discussed.
Considerations of Human Factors in the Design and Implementation of Clinical Decision Support Systems for Tumor Boards
Authors: Gaudioso Carmelo  , Elkin Peter
Published: February 2025

Critical decisions are made at tumor boards (TB). Further research is needed to guide the design and implementation of clinical decision support systems (CDSS) for highly interactive, fast-paced, and complex TB decision making. We highlight two key human factors that should be considered in the design and Implementation of CDSS for multidisciplinary deliberations: cognitive burden and trust.
Challenges in Archetypes Terminology Binding Using SNOMED-CT Compositional Grammar: The Norwegian Patient Summary Case
Authors: Marco-Ruiz Luis  , Pedersen Rune
Published: February 2025

In order to cover the requirements for interoperability in the Norwegian context, we studied the terminology binding of archetypes to terminology expressions created with the SNOMED-CT compositional grammar. As a result we identified important challenges categorized as technical, expressivity, human, and models mismatch.
The Analysis of Medical Adverse Events Related to Electronic Health Records in Nursing Services
Authors: Tomita Mika  , Kishi Natsumi  , Iwasawa Mariko
Published: February 2025

This pilot study investigated problems of electronic health records (EHR), which have been used by nurses as a clinical decision tool. The investigation was conducted based on case records retrieved from the national database of medical adverse events. Detailed data related to nursing services must continue to be collected to establish a clearer linkage of EHR data and scholary information.
Integrated Care Processes Designed for the Future Healthcare System
Authors: Bürkle Thomas  , Denecke Kerstin  , Lehmann Michael  , Zetz Erwin  , Holm Jürgen
Published: February 2025

Previous studies showed that a stronger focus on integrated care models is required, targeting at a seamless coordinated care reaching patients even at home and thus establish patient centered and patient driven innovation activities. Such integrated care model is important among others for ensuring traceability that is necessary when problems during treatment occur or in cases of product counterfeiting for ensuring patient safety. The objective of our work is to realize an efficient, continuous care process, considering an optimal, end-to-end treatment path. In this paper, we describe the process how to pave the way for developing technologies for realizing a cross-sector treatment pathway. Following a multi-stakeholder principle and by applying requirement analysis and world café methodology we designed first concepts and strategies.
General Symptom Extraction from VA Electronic Medical Notes
Authors: Divita Guy  , Luo Gang  , Tran Le-Thuy T.  , Workman T. Elizabeth  , Gundlapalli Adi V.  , Samore Matthew H.
Published: February 2025

There is need for cataloging signs and symptoms, but not all are documented in structured data. The text from clinical records are an additional source of signs and symptoms. We describe a Natural Language Processing (NLP) technique to identify symptoms from text. Using a human-annotated reference corpus from VA electronic medical notes we trained and tested an NLP pipeline to identify and categorize symptoms. The technique includes a model created from an automatic machine learning model selection tool. Tested on a hold-out set, its precision at the mention level was 0.80, recall 0.74 and an overall f-score of 0.80. The tool was scaled-up to process a large corpus of 964,105 patient records.
A Personalized Adviser-Interpreter System for Monitoring Diabetic Patients' Weight Loss
Authors: Kamel Ghalibaf Azadeh  , Tara Seyed Mahmood
Published: February 2025

Diabetes and obesity are chronic diseases that need continuous follow-up. Analysis of the large volumes of patient data collected over time is time consuming and effortful for the physician. Automatic data abstraction and providing a summary of important events in the patient's follow-up history saves the physician's time and helps in making more informed decisions. The purpose of this study was to design an interpreter system that generates a tailored textual report about patients' adherence to a therapeutic regimen. The system has been designed in three phases: determining report structure, interpreting data, and generating textual-graphical reports for the patient and physician. The output report was evaluated by 12 diabetes nutrition specialists and eight diabetic patients. We used a questionnaire and a semi-structured interview approach to assess the quality of the report in terms of content validity, understandability, and practicality.
Analysis of the Existence of Patient Care Team Using Social Network Methods in Physician Communities from Healthcare Insurance Companies
Authors: Ito Márcia  , Appel Ana Paula  , de Santana Vagner Figueredo  , Moyano Luis G.
Published: February 2025

Care teams are formed by physicians of different specialties who take care of the same patient. Hence, if we find physicians that share patients with each other probably they configure an informal care team. Thus, the objective of this work is to explore the possibility of finding care teams using Social Network Analysis techniques in physician-physician networks where the physicians have patients in common. For this, we used healthcare insurance claims to build the network. There was the agreement on the metrics of degree and eigenvalue and of betweenness and closeness, also physicians with the 5 highest eigenvalues are highly interconnected. We discuss that the analysis of the physician-physician network with metrics of centrality is promising to reveal informal care teams. The high potential in calculating these metrics is verified from the results to evaluate member's performance and with that how to take actions to improve the work of the team.
Creation of the First French Database in Primary Care Using the ICPC2: Feasibility Study
Authors: Lacroix-Hugues V.  , Darmon D.  , Pradier C.  , Staccini P.
Published: February 2025

The objective of our study was to assess the feasibility of gathering data stored in primary care Electronic Health records (EHRs) in order to create a research database (PRIMEGE PACA project). The software for EHR models of two office and patient data management systems were analyzed; anonymized data was extracted and imported into a MySQL database. An ETL procedure to code text in ICPC2 codes was implemented. Eleven general practitioners (GPs) were enrolled as “data producers” and data were extracted from 2012 to 2015. In this paper, we explain the ways to make this process feasible as well as illustrate its utility for estimating epidemiological indicators and professional practice assessments. Other software is currently being analyzed for integration and expansion of this panel of GPs. This experimentation is recognized as a robust framework and is considered to be the technical foundation of the first regional observatory of primary care data.
Using Machine Learning Models to Predict In-Hospital Mortality for ST-Elevation Myocardial Infarction Patients
Authors: Li Xiang  , Liu Haifeng  , Yang Jingang  , Xie Guotong  , Xu Meilin  , Yang Yuejin
Published: February 2025

Acute myocardial infarction is a major cause of hospitalization and mortality in China, where ST-elevation myocardial infarction (STEMI) is more severe and has a higher mortality rate. Accurate and interpretable prediction of in-hospital mortality is critical for STEMI patient clinical decision making. In this study, we used interpretable machine learning approaches to build in-hospital mortality prediction models for STEMI patients from Chinese Acute Myocardial Infarction (CAMI) registry data. We first performed cohort construction and feature engineering on CAMI data to generate an available dataset and identify potential predictors. Then several supervised learning methods with good interpretability, including generalized linear models, decision tree models, and Bayes models, were applied to build prediction models. The experimental results show that our models achieve higher prediction performance (AUC = 0.80~0.85) than the previous in-hospital mortality prediction STEMI models and are also easily interpretable for clinical decision support.
Prescription Opioid Dependence in Western New York: Using Data Analytics to Find an Answer to the Opioid Epidemic
Authors: Sinha Shyamashree  , Burstein Gale R.  , Leonard Kenneth E.  , Murphy Timothy F.  , Elkin Peter L.
Published: February 2025

Opioid dependence and overdose is on the rise. One indicator is the increasing trends of prescription buprenorphine use among patient on chronic pain medication. In addition to the New York State Department of Health's prescription drug monitoring programs and training programs for providers and first responders to detect and treat a narcotic overdose, further examination of the population may provide important information for multidisciplinary interventions to address this epidemic. This paper uses an observational database with a Natural Language Processing (NLP) based Not Only Structured Query Language architecture to examine Electronic Health Record (EHR) data at a regional level to study the trends of prescription opioid dependence. We aim to help prioritize interventions in vulnerable population subgroups. This study provides a report of the demographic patterns of opioid dependent patients in Western New York using High Throughput Phenotyping NLP of EHR data.
A Deep Learning-Based Method for Similar Patient Question Retrieval in Chinese
Authors: Tang Guo Yu  , Ni Yuan  , Xie Guo Tong  , Fan Xin Li  , Shi Yan Ling
Published: February 2025

The online patient question and answering (Q&A) system, either as a website or a mobile application, attracts an increasing number of users in China. Patients will post their questions and the registered doctors then provide the corresponding answers. A large amount of questions with answers from doctors are accumulated. Instead of awaiting the response from a doctor, the newly posted question could be quickly answered by finding a semantically equivalent question from the Q&A achive. In this study, we investigated a novel deep learning based method to retrieve the similar patient question in Chinese. An unsupervised learning algorithm using deep neural network is performed on the corpus to generate the word embedding. The word embedding was then used as the input to a supervised learning algorithm using a designed deep neural network, i.e. the supervised neural attention model (SNA), to predict the similarity between two questions. The experimental results showed that our SNA method achieved P@1 = 77% and P@5 = 84%, which outperformed all other compared methods.
Knowledge Management in Health Technology SMEs
Authors: Huusko Juhamatti  , Kuusisto-Niemi Sirpa  , Saranto Kaija
Published: February 2025

The purpose of this study was to examine knowledge management's (KM) role in small and medium-sized (SMEs) health technology enterprises, which employ fewer than 250 employees. In this study, KM is understood as the ability to achieve competitive advantage by utilizing management knowledge and making it profitable. The health technology enterprises use modern technology to resolve health-related issues. The research data was acquired from Finnish health technology SMEs. The questionnaire was sent to 140 enterprises, generating 25 responses, or a 17.9% response rate. According to the results, health technology enterprises have not adopted KM concepts, nor do they have the necessary resources to do so. SMEs' KM use is informal: information is transferred informally through human interaction, rather than through information systems. In the SMEs, KM is not perceived as important, although it is seen as associated with the enterprise's financial performance through the potential in making the knowledge profitable.
A Semi-Automatic Framework to Identify Abnormal States in EHR Narratives
Authors: Ma Xiaojun  , Imai Takeshi  , Shinohara Emiko  , Sakurai Ryota  , Kozaki Kouji  , Ohe Kazuhiko
Published: February 2025

Disease ontology, defined as a causal chain of abnormal states, is believed to be a valuable knowledge base in medical information systems. Automatic mapping between electronic health records (EHR) and disease ontology is indispensable for applying disease ontology in real clinical settings. Based on an analysis of ontologies of 148 chronic diseases, approximately 41% of abnormal states require information extraction from clinical narratives. This paper presents a semi-automatic framework to identify abnormal states in clinical narratives. This framework aims to effectively build mapping modules between EHR and disease ontology. We show that the proposed method is effective in data mapping for 18%–33% of the abnormal states in the ontologies of chronic diseases. Moreover, we analyze the abnormal states for which our method is invalid in extracting information from clinical narratives.
KQA: A Knowledge Quality Assessment Model for Clinical Decision Support Systems
Authors: Zolhavarieh Seyedjamal  , Parry Dave
Published: February 2025

Informatics researchers have developed many methods for using computers to utilize knowledge in decision making in the form of clinical decision support systems (CDSSs). These systems can enhance human decision making in the healthcare domain. The knowledge acquisition bottleneck is one of the well-known issues in developing knowledge-based systems such as CDSS. It can be considered as a flow of knowledge from different knowledge sources to the main system. Most existing methods for extracting knowledge from knowledge resources suffer from the lack of a proper mechanism for extracting high-quality knowledge. In this paper, we propose a framework to discover high-quality knowledge by utilizing Semantic Web technologies.
A Conceptual Model for Systems Engineering and Its Formal Foundation
Authors: Buga Andreea  , Nemes Sorana Tania  , Schewe Klaus-Dieter  , Songprasop Pornpan
Published: February 2025

Systems Engineering concerns the complete process for the development of complex systems comprising hardware, software, facilities and personnel. Such systems are hybrid, as some components are characterised by continuous behaviour, whereas the behaviour of others is discrete. SysML provides graphical notations capturing requirements for systems engineering. Here we present a concise conceptual model for systems engineering that is capable to capture the gist of SysML. The structural part is based on the common higher-order Entity Relationship model that is extended by continuous functions and various flow constraints. The behavioural part is then formalised by a hybrid extension of Abstract State Machines. The usage of the conceptual model is illustrated by a working example concerning the landing gear equipment of a plane.
Dimensions and Values for Legal CBR
Authors: Bench-Capon Trevor  , Atkinson Katie
Published: February 2025

We build on two recent attempts to formalise reasoning with dimensions which effectively map dimensions into factors. These enable propositional reasoning, but sometimes a balance between dimensions needs to be struck, and to permit trade offs we need to keep the magnitudes and so reason more geometrically. We discuss dimensions and values, arguing that values can play several distinct roles, both explaining preferences between factors and indicating the purposes of the law.
Detecting Agent Mentions in U.S. Court Decisions
Authors: Šavelka Jaromír  , Ashley Kevin D.
Published: February 2025

Case law analysis is a significant component of research on almost any legal issue and understanding which agents are involved and mentioned in a decision is integral part of the analysis. In this paper we present a first experiment in detecting mentions of different agents in court decisions automatically. We defined a light-weight and easily extensible hierarchy of agents that play important roles in the decisions. We used the types from the hierarchy to annotate a corpus of US court decisions. The resulting data set enabled us to test the hypothesis that the mentions of agents in the decisions could be detected automatically. Conditional random fields models trained on the data set were shown to be very promising in this respect. To support research in automatic case-law analysis we release the agent mentions data set with this paper.