bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamics in Entry and Exit Registrations in a 14-Year Follow-Up of Nationwide Electronic Prescription and Patient Data Repository Services in Finland
Authors: Jormanainen Vesa 
Published: May 2024

AbstractThere exist a need to carry out further research in order to describe implementation and adoption of nationwide healthcare information systems. This research aimed to follow-up in a 14-year period (2010–2023) of public and private healthcare service organizations’ entries to and exits from the centralized electronic Prescription and Patient Data Repository Services in Finland. Our material comes from the official Social Welfare and Healthcare Organization Registry (SOTE-organisaatiorekisteri), which is part of the national Code Server and the Kanta Services. Registry data were extracted in an excel file format in 3 January 2024. Outcomes were continuous registration of services or registered exist from the services. We found profound dynamics in the registry data. In the nationwide Prescription Services, the registered organizations provided altogether 8,884 follow-up years, during which in 2010–2023 there were in total 1,530 healthcare service organization entries and 553 exits from the national services, whereas 977 organizations had the national services in production in 2023. In Patient Data Repository Services, the registered organizations provided altogether 7,692 follow-up years, during which in 2011–2023 there were totally 1,980 healthcare service organization entries and 494 exits from the national services, whereas 1,486 organizations had the national services in production in 2023. No effects of Covid-19 epidemic were observed. Permanent legislation may explain many of the peak numbers observed in this research. Effects of the structural reform to reorganize healthcare, social welfare and rescue services to wellbeing services counties starting January 2023 were observed in this registry research on public healthcare service organizations.
Fatigue Assessment with Visualizations of Patient-Generated Data: An Evaluation with Informatics-Savvy Healthcare Professionals
Authors: Guardado Sharon , Holappa Terhi , Isomursu Minna 
Published: May 2024

AbstractSevere and chronic fatigue is a prevalent symptom in multiple chronic conditions. Its complexity, its multifaceted nature and its varied manifestations across different conditions require a nuanced approach for accurate assessment by healthcare professionals. In our research, informatics-savvy public health nurses from a Digital Health Services and Health Promotion Master’s program evaluated various visualizations of patient-generated health data which could potentially be collected through a mobile app designed for people with Multiple Sclerosis. The data visualization prototypes could be a tool to support fatigue assessment and effective communication during consultations and their design was based on prior suggestions from healthcare professionals with experience in fatigue assessment. The patient-generated health data represented in the prototypes comprised a combination of fatigue-related factors and physical activity tracked by Google Fit. This study presents the recommendations of the participants regarding various aspects linked to the visualizations of patient-generated health data, including their utility in the clinical setting, the most suitable types of data summaries, usability aspects and the possibility of meaningful interrelations between distinct types of data. The results of our study emphasize the importance of well-designed data visualizations to support healthcare professionals in decision-making and to improve patient participation in the chronic care process. The iterative design process of the prototypes ensures that the final visualizations have proper usability and the potential to become clinically relevant, and instrumental in the effective assessment of fatigue in chronic management.
Wearable Motion Sensors in the Detection of ADHD: A Critical Review
Authors: Basic Jakov , Uusimaa Johanna , Salmi Juha 
Published: May 2024

AbstractAttention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder with inattention, hyperactivity, and impulsivity as core symptoms. Current diagnostic methods of ADHD consisting of interviews and self-ratings come with a risk of subjective bias and are dependent on the limited availability of healthcare professionals. However, recent technological advances have opened new opportunities to develop objective and scalable methods for precision diagnostics. The present critical review covers the current literature concerning one of the promising technologies, the use of motion sensors or accelometers for detecting ADHD, particularly evaluating the related clinical potential. Several studies in this field, especially recent studies with advanced computational methods, have demonstrated excellent accuracy in detecting individual participants with ADHD. Machine learning methods provide several benefits in the analysis of rich sensor data, but the existing studies still have critical limitations in explaining the underlying cognitive functions and demonstrating the capacity for differential diagnostics is still underway. Clinical utility of sensor-based diagnostic methods could be improved by conducting rigorous cross-validation against other methods in representative samples and employing multi-sensor solutions with sophisticated analysis methods to improve interpretation of the symptom manifestation. We conclude that motion sensors provide cost-effective and easy-to-use solutions with strong potential to increase the precision and availability of ADHD diagnostics. Nevertheless, these methods should be employed with caution, as only a fraction of ADHD symptoms relate to hyperactivity captured by motion sensors. At best, this technique could complement the existing assessment methods or be used along with other digital tools such as virtual reality.
Influence of Arterial Vessel Diameter and Blood Viscosity on PTT in Pulsatile Flow Model
Authors: Zienkiewicz Aleksandra , Vihriälä Erkki , Myllylä Teemu 
Published: May 2024

AbstractModelling relation between Pulse Transit Time (PTT) and blood pressure (BP) is a critical step in BP estimation for wearable technology. Recognizing the limitation of assuming constant vessel and blood conditions, we developed a simplified pulsatile flow model to analyze how various factors affect PTT values. Our research focuses on the impact of mechanical characteristics, such as vessel diameter, wall thickness, blood viscosity, and pressure, on PTT measurements and subsequent BP estimation. Measurements were conducted using accelerometer sensors within a custom-designed mock circulatory loop. This setup allowed for the testing of a wide range of pressure values and pulsation rates, as well as the modification of viscosity in blood-mimicking liquids across different vessel models. We employed the Moens-Korteweg conversion model for pressure estimation, initially trained on PTT data from a specific setup parameter combination, and subsequently tested with data from varied setup parameters. We observed high correlation levels (r = 0.93 ± 0.09) paired with high error (RMSE = 163 ± 100 mHg), suggesting potential inaccuracies in pressure estimation. We present the recorded signals and discuss how alterations in physical conditions influence PTT values and the precision of BP estimation.
Enhancing Arrhythmia Diagnosis with Data-Driven Methods: A 12-Lead ECG-Based Explainable AI Model
Authors: Chukwu Emmanuel C. , Moreno-Sánchez Pedro A. 
Published: May 2024

AbstractAccurate and early prediction of arrhythmias using Electrocardiograms (ECG) presents significant challenges due to the non-stationary nature of ECG signals and inter-patient variability, posing difficulties even for seasoned cardiologists. Deep Learning (DL) methods offer precision in identifying diagnostic ECG patterns for arrhythmias, yet they often lack the transparency needed for clinical application, thus hindering their broader adoption in healthcare. This study introduces an explainable DL-based prediction model using ECG signals to classify nine distinct arrhythmia categories. We evaluated various DL architectures, including ResNet, DenseNet, and VGG16, using raw ECG data. The ResNet34 model emerged as the most effective, achieving an Area Under the Receiver Operating Characteristic (AUROC) of 0.98 and an F1-score of 0.826. Additionally, we explored a hybrid approach that combines raw ECG signals with Heart Rate Variability (HRV) features. Our explainability analysis, utilizing the SHAP technique, identifies the most influential ECG leads for each arrhythmia type and pinpoints critical signal segments for individual disease prediction. This study emphasizes the importance of explainability in arrhythmia prediction models, a critical aspect often overlooked in current research, and highlights its potential to enhance model acceptance and utility in clinical settings.
Real-Time Gait Anomaly Detection Using 1D-CNN and LSTM
Authors: Rostovski Jakob , Ahmadilivani Mohammad Hasan , Krivošei Andrei , Kuusik Alar , Alam Muhammad Mahtab 
Published: May 2024

AbstractAnomaly detection and fall prevention represent one of the key research areas within gait analysis for patients suffering from neurological disorders. Deep Learning has penetrated into healthcare applications, encompassing disease diagnosis and anomaly prediction. Connected wearable medical sensors are emerging due to computationally expensive machine learning tasks, which traditionally require use of remote PC or cloud computing. However, to reduce needs for wireless communication channel throughput, for data processing latency, and increase service reliability and safety, on device machine learning is gaining attention. This paper presents an innovative approach that leverages one dimensional convolutional neural network (1D-CNN) and long-short term memory (LSTM) neural network for the real-time detection of abnormal gait patterns during the step. Real-time anomaly detection pertains to the algorithm’s ability to promptly detect true gait abnormality occurrence during the swing phase of an ongoing step.For the experiments, we have collected eight different common gait anomalies, simulated by 22 persons, using motion sensors containing multidimensional inertial measurement units (IMUs).Results have demonstrated that the proposed 1D-CNN-AD algorithm achieves an average accuracy of 95% and an average F1-score of 88% for all gait types and can run in true real-time. Average earliness for 1D-CNN-AD algorithm was 0.6 s, which is mid-swing phase of the step. Proposed LSTM-AD algorithm achieved average accuracy of 87% and average F1-score of 70% for all gait types.
Research for JYU: An AI-Driven, Fully Remote Mobile Application for Functional Exercise Testing
Authors: Cronin Neil , Lehtiö Ari , Talaskivi Jussi 
Published: May 2024

AbstractAs people live longer, the incidence and severity of health problems increases, placing strain on healthcare systems. There is an urgent need for resource-wise approaches to healthcare. We present a system built using open-source tools that allows health and functional capacity data to be collected remotely. The app records performance on functional tests using the phone’s built-in camera and provides users with immediate feedback. Pose estimation is used to detect the user in the video. The x, y coordinates of key body landmarks are then used to compute further metrics such as joint angles and repetition durations. In a proof-of-concept study, we collected data from 13 patients who had recently undergone knee ligament or knee replacement surgery. Patients performed the sit-to-stand test twice, with an average difference in test duration of 1.12 s (range: 1.16–3.2 s). Y-coordinate locations allowed us to automatically identify repetition start and end times, while x, y coordinates were used to compute joint angles, a common rehabilitation outcome variable. Mean difference in repetition duration was 0.1 s (range: −0.4–0.4 s) between trials 1 and 2. Bland-Altman plots confirmed general test-retest consistency within participants. We present a mobile app that enables functional tests to be performed remotely and without supervision. We also demonstrate real-world feasibility, including the ability to automate the entire process, from testing to analysis and the provision of real-time feedback. This approach is scalable, and could form part of national health strategies, allowing healthcare providers to minimise the need for in-person appointments whilst yielding cost savings.
Finnish Digi-HTA Assessment Model for Digital Health and an International Comparison
Authors: Haverinen Jari , Suominen Jarno , Kaksonen Rauli , Veikkolainen Paula , Voutilainen Merja , Reponen Jarmo , Röning Juha , Falkenbach Petra 
Published: May 2024

AbstractNew health technology assessment (HTA) models for digital health are continuously being developed and are already in use. In Finland, the HTA model for digital health, named Digi-HTA, has been employed since 2020. Internationally and also in Finland, the need for harmonization of these HTA models has been recognized. In order to harmonize the models, it is necessary to first identify the key features and requirements of existing models. In this study, three key assessment models for digital health identified as central in the Finnish context were analyzed. After the analysis, the results were compared to the Finnish Digi-HTA assessment model, and a final synthesis was created regarding the similarities and differences between the assessment models. The comparison includes German DiGA model, the global CEN-ISO/TS 82304-2:2021 technical specification, and the Nordic-designed NordDEC assessment model. There was a great deal of similarity in the evaluated models, although certain differences in emphasis were found. The key differences relate to reimbursement process, maturity of the assessment process and supported product categories as well as cost and effectiveness evaluation. The results of this study can be utilized in harmonizing assessment models for digital health.
Method to Monitor Cough by Employing Piezoelectric Energy Harvesting Configurations
Authors: Palosaari Jaakko , Virta Eetu , Miinala Miika , Bai Yang 
Published: May 2024

AbstractCough is the most common symptom prompting individuals to seek medical advice. However, the widespread adoption of autonomous cough monitoring using wearable devices remains limited. This paper introduces a wireless cough monitoring device utilizing piezoelectric energy harvesting technology. The design emphasizes cost-effectiveness and energy efficiency, allowing simple attachment onto human skin using medical-grade tapes. The device's standout feature lies in its departure from continuously recording real-time acoustic data at a high sampling rate, as commonly employed in prior works. Instead, it capitalizes on the energy harvesting capability, utilizing harvested energy from muscle movements induced by coughing as crucial information. The energy harvested within specific intervals translates into a historical record of cough occurrences during that timeframe.
This Energy-as-Data protocol substantially reduces the device's duty cycle, resulting in a remarkable extension of battery life by up to 2100%. Notably, this extension is achieved while maintaining reasonable accuracy in cough monitoring. With this capability, the device can autonomously monitor and analyze cough data from both in- and outpatients, serving daily, research, and clinical purposes. Its potential extends to enhancing prediction and management of severe respiratory diseases.
Microwave Technique Based Noninvasive Monitoring of Intracranial Pressure Using Realistic Phantom Models
Authors: Singh Daljeet , Vihriälä Erkki , Särestöniemi Mariella , Myllylä Teemu 
Published: May 2024

AbstractMicrowave technology is emerging as a promising candidate in the field of medical diagnosis and imaging and has paved the way for a transition from invasive to non-invasive methods of monitoring various biological phenomena inside the human body. Intracranial Pressure (ICP) is considered to be a very important parameter by medical practitioners for assessing the health of a subject. Accurate, prolonged, and noninvasive measurement of ICP is still an open area of research with no clinical success so far. Therefore, in this paper, a microwave-based method for non-invasive monitoring of ICP is proposed. The setup utilizes flexible, thin, small, and lightweight planner antennas that are very suitable for non-invasive monitoring of ICP from the skin without compromising the comfort of subject. The proposed microwave method is tested on a realistic head phantom model which imitates the functioning of hydrodynamics in a real human head. The measurement results from the proposed method are verified using invasive pressure sensors. It is deduced from numerous trials that the proposed microwave system can detect small changes in ICP pressure and its response is analogous to actual pressure values measured by invasive pressure sensors.
Study on Fat as the Propagation Medium in Optical-Based In-Body Communications
Authors: Fuada Syifaul , Särestöniemi Mariella , Katz Marcos , Soderi Simone , Hämäläinen Matti 
Published: May 2024

AbstractThis paper investigates fat tissue as a medium for communication in implantable/ingestible medical device (IMD) systems based on optical wireless communication (OWC). The findings emphasize the importance of tissue characteristics (temperature in particular) for optimizing OWC performance. This study considered Near-infrared (NIR) light with 810 nm wavelength and fresh porcine samples to mimic the human tissue. The study employs a realistic measurement approach in an ex vivo setting using various porcine samples: pure fat and flesh tissues and samples with different thicknesses. This study also investigates the influence of porcine temperature on the optical communication channels, which are measured by comparing the received optical power at 23 °C and 37 °C. In general, tissue samples at warmer temperatures (37 °C) receive higher optical power than colder samples. The results also demonstrate the superior optical power transmission capabilities of pure fat compared to pure flesh in porcine tissue samples in warm conditions. We also found that porcine with multiple layers of fat (fatty sample) yields higher received optical power than porcine with multiple layers of flesh (muscular). The results of this study provide valuable insights and relevant considerations for OWC-based in-body communication conducted using porcine samples.
Preliminary Studies on mm-Wave Radar for Vital Sign Monitoring of Driver in Vehicular Environment
Authors: Singh Daljeet , Eleonye Theresa , Surazynski Lukasz , Ferdinando Hany , Kumar Atul , Joshi Hem Dutt , Särestöniemi Mariella , Myllylä Teemu 
Published: May 2024

AbstractThe last decade has witnessed significant improvements in vehicular technology, especially in providing a safer and more enjoyable environment for drivers and passengers. Fully autonomous vehicles are no longer a dream but are now a successful technology across the globe. Features such as autopilot, assisted parking, speed warning, and lane change assistance have improved the quality of user experience while using an automobile. Apart from this, e-health services have also become a prime aspect of the modern vehicular industry. Therefore, this research presents preliminary studies on mm-wave radar setup based on Frequency Modulated Continuous Wave (FMCW) technology in the 76 to 81 GHz band for vital sign monitoring of drivers and passengers in a vehicular environment. The effect of system parameters and the driver’s location with respect to radar is studied using human subjects to determine the optimum setup for vital sign monitoring. Measurement results showcase that mm-wave radars can be utilized for accurate and efficient measurement of the vital signs of drivers in vehicular environments.
Dual-Perspective Modeling of Patient Pathways: A Case Study on Kidney Cancer
Authors: Larsen Anna Grøndahl , Halvorsrud Ragnhild , Berg Rolf Eigil , Vesinurm Märt 
Published: May 2024

AbstractPatient pathway has become a key concept in the organization of healthcare. However, the materialization and operationalization of pathways often focus on work processes of health personnel, clinical decision-making, and deadlines, contradicting the strong patient-oriented perspective that is inherent in their definition. In this paper, we introduce a patient-centered perspective of kidney cancer pathways, reporting on a dual-perspective strategy to map and model patient pathways. Utilizing a multi-method approach, we map and model pathways from the perspectives of both healthcare personnel and patients and investigate the feasibility of the Customer Journey Modeling Language (CJML) for modeling patient pathways. To prevent confusion, the planned pathway as seen from the hospital perspective and the actual pathway experienced by the patient are referred to as ‘pathway’ and ‘journey’, respectively. In the paper, we describe methods to engage with healthcare professionals and patients to collect the necessary information to create precise models, and we show how precise modeling of patient pathways requires the integration of several information sources. Moreover, the study underlines the value of examining pathways from a dual perspective, as the two perspectives corroborate and supplement each other, illustrating the complexity of patient journeys. Finally, the findings provide insights into the feasibility of CJML, firstly underlining that the usefulness of visual models is context-dependent, and secondly, suggesting that the methods and subsequent visualizations may be useful as organizational, instructional, and communicative tools.
Effectiveness of Robot-Assisted Lower Limb Rehabilitation on Balance in People with Stroke: A Systematic Review, Meta-analysis, and Meta-regression
Authors: Yli-Ikkelä Riku , Rintala Aki , Köyhäjoki Anna , Hakonen Harto , Korpi Hilkka , Kantola Mirjami , Honkanen Sari , Ilves Outi , Sjögren Tuulikki , Karvanen Juha , Aartolahti Eeva 
Published: May 2024

AbstractThe objective of this study was to evaluate the effectiveness of robot-assisted lower-limb rehabilitation on balance in stroke patients and to explore the covariates associated with these effects.A systematic literature search was carried out in four databases (MEDLINE (Ovid), CINAHL, PsycINFO, and ERIC) for studies published from inception to 25th of March 2022. Studies on robot-assisted lower-limb rehabilitation with a randomized controlled trial (RCT) design, participants with stroke, a comparison group with conventional training, and balance-related outcomes were included. Studies were assessed for Cochrane Risk of Bias 2 and quality of evidence. Meta-analysis and meta-regression were performed.A total of 48 (RCT) with 1472 participants were included. The overall risk of bias in the included studies was unclear (n = 32), high (n = 15) or low (n = 1). Compared to conventional rehabilitation, robot-assisted lower-limb rehabilitation interventions were more effective for balance improvement (Hedges’ g = 0.25, 95% CI: 0.10 0.41). In meta-regression, a relationship between the training effect was observed with the time since stroke, explaining 56% of the variance (p = 0.001), and with the ankle robots, explaining 16% of the variance (p = 0.048). No serious adverse events related to robot-assisted training were reported.Robot-assisted lower-limb rehabilitation may improve balance more than conventional training in people with stroke, especially in the acute stage. Robot-assisted lower-limb rehabilitation seems to be a safe rehabilitation method for patients with stroke. To strengthen the evidence, more high-quality RCTs with adequate sample sizes are needed.