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[english] Medical research is subject to continuous change, and scientific literature must be timely assessable for research, diagnosis, and teaching. However, biomedical sciences have become interdisciplinary, collective, and translational. Furthermore, novel technologies such as the Internet and smart phones have been established and are available almost everywhere. Medical libraries have to follow these trends offering novel integrated services to optimize the support of their customers. In this paper, we analyse the domain from the users’ perspective and give a categorization of future bibliographic services.[german] Literatur ist die Kernkomponente der Wissenschaft, aber die medizinische Forschung ist im ständigen Wandel. Die Metapher des Elfenbeinturmes für den Arbeitsplatz des Wissenschaftlers ist längst nicht mehr gültig. Erfolgreiche Wissenschaft im biomedizinischen Bereich ist heute interdisziplinär, kollektiv und translational. Dieser Prozess wird begleitet von technologischen Innovationen wie dem Smart Phone und der Informationsressource Internet, die überall und immer verfügbar ist. Das medizinische Bibliothekswesen muss diesen Wandel mit vollziehen und mit innovativen, prozessintegrierten Diensten seine Kunden optimal unterstützen. Dieser Beitrag beleuchtet das Problemfeld aus Sicht des Anwenders und versucht, die Anforderungen an bibliothekarische Dienste zu kategorisieren und zu priorisieren.

Bei der operativen Rekonstruktion des Orbitabodens ist unter anderem die Gleichheit von linkem und rechten Orbitavolumen für das Sehvermögen entscheidend. In dieser Arbeit wird ein Planungswerkzeug entwickelt, welches mit Hilfe eines aktiven Konturmodells den Orbitainhalt in CT-Datensätzen segmentiert, ihr Volumen berechnet und dieses visualisiert, um dem operierenden Arzt die Therapieplanung und -kontrolle zu erleichtern. Neben den technischen Integrationsstufen der Funktions- und Präsentationsintegration, die durch den Einsatz des Medical Imaging Interaction Toolkit (MITK) direkt erreicht werden, sowie der Daten-, und Kontextintegration, die durch das DICOM Protokoll unterstützt werden, ist vor allem die Stabilität, Robustheit und Korrektheit der eingesetzten Algorithmen für die Routine-Integrierbarkeit wichtig. Stabilitätsuntersuchungen bei 20 Patienten und jeweils 50 zufällig gewählten Startpunkten zeigen einen mittleren Variationskoeffizienten von 0,72%, d.h. deutlich unterhalb der kritischen 5% Schwelle. Die Robustheitsuntersuchung auf 200 Orbitae ergab eine Fehlerrate von 8%. Vergleiche der Volumina der rechten und linken Seite bei 74 klinisch unauffälligen Individuen resultiert in einem mittleren Variationskoeffizienten von 0,85%, so dass das Verfahren auch als korrekt angesehen werden kann. Exemplarisch wird die Anwendbarkeit mit einer klinischen Fallstudie belegt. Aufgrund der nachgewiesenen Stabilität und Korrektheit des Verfahrens sowie der umgesetzten Daten-, Funktions-, Kontext- und Präsentationsintegration kann das Tool in den klinischen Prozess eingebunden werden, und soll nun in der praktischen Anwendung umfassend evaluiert werden.

ObjectiveTo identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2020.MethodA broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and image informatics. We only considered papers that have been published in journals providing at least three articles in the query response. Section editors then independently reviewed the titles and abstracts of preselected papers assessed on a three-point Likert scale. Papers were rated from 1 (do not include) to 3 (should be included) for each topical area (sensors, signals, and imaging informatics) and those with an average score of 2 or above were subsequently read and assessed again by two of the three co-editors. Finally, the top 14 papers with the highest combined scores were considered based on consensus.ResultsThe search for papers was executed in January 2021. After removing duplicates and conference proceedings, the query returned a set of 101, 193, and 529 papers for sensors, signals, and imaging informatics, respectively. We filtered out journals that had less than three papers in the query results, reducing the number of papers to 41, 117, and 333, respectively. From these, the co-editors identified 22 candidate papers with more than 2 Likert points on average, from which 14 candidate best papers were nominated after intensive discussion. At least five external reviewers then rated the remaining papers. The four finalist papers were found using the composite rating of all external reviewers. These best papers were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board.ConclusionsSensors, signals, and imaging informatics is a dynamic field of intense research. The four best papers represent advanced approaches for combining, processing, modeling, and analyzing heterogeneous sensor and imaging data. The selected papers demonstrate the combination and fusion of multiple sensors and sensor networks using electrocardiogram (ECG), electroencephalogram (EEG), or photoplethysmogram (PPG) with advanced data processing, deep and machine learning techniques, and present image processing modalities beyond state-of-the-art that significantly support and further improve medical decision making.

ObjectiveTo highlight noteworthy papers that are representative of 2019 developments in the fields of sensors, signals, and imaging informatics.MethodA broad literature search was conducted in January 2020 using PubMed. Separate predefined queries were created for sensors/signals and imaging informatics using a combination of Medical Subject Heading (MeSH) terms and keywords. Section editors reviewed the titles and abstracts of both sets of results. Papers were assessed on a three-point Likert scale by two co-editors, rated from 3 (do not include) to 1 (should be included). Papers with an average score of 2 or less were then read by all three section editors, and the group nominated top papers based on consensus. These candidate best papers were then rated by at least six external reviewers.ResultsThe query related to signals and sensors returned a set of 255 papers from 140 unique journals. The imaging informatics query returned a set of 3,262 papers from 870 unique journals. Based on titles and abstracts, the section co-editors jointly filtered the list down to 50 papers from which 15 candidate best papers were nominated after discussion. A composite rating after review determined four papers which were then approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. These best papers represent different international groups and journals.ConclusionsThe four best papers represent state-of-the-art approaches for processing, combining, and analyzing heterogeneous sensor and imaging data. These papers demonstrate the use of advanced machine learning techniques to improve comparisons between images acquired at different time points, fuse information from multiple sensors, and translate images from one modality to another.

Objective To summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017.Methods PubMed® and Web of Science® were searched to identify the scientific publications published in 2017 that addressed sensors, signals, and imaging in medical informatics. Fifteen papers were selected by consensus as candidate best papers. Each candidate article was reviewed by section editors and at least two other external reviewers. The final selection of the four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook.Results The selected papers of 2017 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information.ConclusionThe growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics. This synopsis highlights cutting-edge contributions to the science of Sensor, Signal, and Imaging Informatics.

Cardiopulmonary coupling (CPC) analysis links heart and respiration rates to assess sleep-related parameters. Typically, the CPC is measured using multi-lead electrocardiography (ECG) and ECG-derived respiration (EDR). Novel textile shirts with embedded ECG sensors offer convenient and continuously monitored sleep at home. We investigate the feasibility of a shirt with textile sensors (Pro- Kit, Hexoskin, Quebec, Canada) for CPC analysis by mobile computing. ECG data is continuously transmitted from the shirt to a smartphone via Bluetooth Low Energy (BLE). We customize a CPC algorithm and use twelve whole-night recordings from four volunteers to perform qualitative and quantitative analysis. We compare EDR with respiratory inductive plethysmography (RIP). In average, EDR and RIP differ 17.22%. After one night, the batteries are reduced to approx. 70% (shirt) and 90% (smartphone). The run time for CPC processing is approx. 3 min. Hence, smart wearables in combination with mobile computing show technical feasibility for CPC analysis. Eventually, this could yield a useful solution for sleep analysis of non-expert users in a private environment.

Due to the importance of COVID-19 control, innovative methods for predicting cases using social network data are increasingly under attention. This study aims to predict confirmed COVID-19 cases using X (Twitter) social network data (tweets) and deep learning methods. We prepare data extracted from tweets by natural language processing (NLP) and consider the daily G-value (growth rate) as the target variable of COVID-19, collected from the worldometer. We develop and evaluate a time series mixer (TSMixer) predictive model for multivariate time series. The mean squared error (MSE) loss on the test dataset was 0.0063 for 24-month Gvalue prediction when using the MinMax normalization with recursive feature elimination (RFE) and average or min aggregation method. Our findings illuminate the potential of integrating social media data to enhance daily COVID-19 case predictions and are applicable also for epidemiological forecasting purposes. [ABSTRACT FROM AUTHOR]

Rescue sheets enable rescue personnel to timely extricate trapped victims of road traffic accidents and increase their chance of survival. However, in the year 2024, these rescue sheets are still paper based DIN A4 documents. The digital transformation of the rescue process through new reporting technologies, such as eCall or the International Standard Accident Number (ISAN) facilitates digital rescue sheets, providing benefits for availability and functionality. This work addresses design considerations raised by previous research to suggest a process for the creation of digital rescue sheets. Our process transforms high-resolution models provided by car manufacturers and vendors into small files by shape abstraction of the components. The system maps the body of the car to generic representative models of defined car body types reducing the number of models that need to be stored. We develop a hierarchical tree data structure with three levels that allows appending new components of the increasingly complex cars. Our data format for transmission of the rescue sheet sends all relevant data for visualization while still retaining a small file size to account for a poor internet connection. In the future, we aim to evaluate our approach involving car manufacturers and other stakeholders. [ABSTRACT FROM AUTHOR]

Continuous monitoring of physiological signals such as electrocardiogram (ECG) in driving environments has the potential to reduce the need for frequent health check-ups by providing real-time information on cardiovascular health. However, capturing ECG from sensors mounted on steering wheels creates difficulties due to motion artifacts, noise, and dropouts. To address this, we propose a novel method for reliable and accurate detection of heartbeats using sensor fusion with a bidirectional long short-term memory (BiLSTM) model. Our dataset contains reference ECG, steering wheel ECG, photoplethysmogram (PPG), and imaging PPG (iPPG) signals, which are more feasible to capture in driving scenarios. We combine these signals for R-wave detection. We conduct experiments with individual signals and signal fusion techniques to evaluate the performance of detected heartbeat positions. The BiLSTMs model achieves a performance of 62.69% in the driving scenario city. The model can be integrated into the system to detect heartbeat positions for further analysis. [ABSTRACT FROM AUTHOR]

Integrating continuous monitoring into everyday objects enables the early detection of diseases. This paper presents a novel approach to heartbeat monitoring on eScooters using multi-modal signal fusion. We explore heartbeat monitoring using electrocardiography (ECG) and photoplethysmography (PPG) and evaluate four signal fusion approaches based on convolutional neural network (CNN) and long short-term memory (LSTM) architectures. We perform an evaluation study using skin-attached ECG electrodes for ground truth generation. The CNN+LSTM late fusion accurately measures the heartbeat for 76.17% of the driving time. [ABSTRACT FROM AUTHOR]

Clinical deterioration (CD) is the physiological decompensation that incurs care escalation, protracted hospital stays, or even death. The early warning score (EWS) calculates the occurrence of CD based on five vital signs. However, there are limited reports regarding EWS monitoring in smart home settings. This study aims to design a CD detection system for health monitoring at home (HM@H) that automatically identifies unstable vital signs and alarms the medical emergency team. We conduct a requirement analysis by interviewing experts. We use unified modeling language (UML) diagrams to define the behavioral and structural aspects of HM@H. We developed a prototype using a SQL-based database and Python to calculate the EWS in the front end. A team of five experts assessed the accuracy and validity of the designed system. The requirement analysis for four main users yielded 30 data elements and 10 functions. Three main components of HM@H are the graphical user interface (GUI), the application programming interface (API), and the server. Results show the possibility of using unobtrusive sensors to collect smart home residents' vital signs and calculate their EWS scores in real-time. However, further implementation with real data, for frail elderly and hospital-discharged patients is required. [ABSTRACT FROM AUTHOR]

Smart wearables support continuous monitoring of vital signs for early detection of deteriorating health. However, the devices and sensors require sufficient quality to produce meaningful signals, in particular, if data is acquired in motion. In this study, we equipped 48 subjects with smart shirts recording one-lead electrocardiography (ECG), thoracic and abdominal respiratory inductance plethysmography, and three-axis acceleration. For 10 min each, the subjects sit, stand, walk, and run, with a resting period of 5 min in between each activity. We preprocessed the electrocardiogram and applied a signal quality index. We analyzed the signal quality index grouped by the activity and participants. For sitting, standing, walking, and running, the ECG signals provide acceptable quality over 73.20 %, 91.85 %, 12.26 %, and 13.14 % of the recording time. In conclusion, smart wearables may be useful for continuous health monitoring of people with a sedentary lifestyle, but rather not for sportive activities. [ABSTRACT FROM AUTHOR]

Automatic alerting systems (AASs) can identify adverse health events but emergency communication relies on human operators and natural languages. For complete automation, we need to code the diversity of adverse events in a granularity that supports optimal dispatches. Hence, AAs shall integrate with the International Classification of Diseases (ICD). The ICD-11 coding system includes chapters for external causes of injury. However, ICD-11 supports coding injury incidents in electronic health records (EHRs) after they have occurred, while disregarding integrating real-time injury reporting within its framework. We explore the potential challenges associated with integrating ICD-11 into AAS by analyzing external causes of morbidity or mortality and the dimensions of external causes as potential areas of integration. We recognize the themes: (i) incident of injury, (ii) mode of transport, (iii) indoor location, (iv) outdoor location, and (v) type of building, and identify four challenges: (i) conceptual differences between the two systems, (ii) injury identification, (iii) presence of entities below the shoreline in ICD-11, and (iv) lack of specificity in certain ICD-11 codes related to AASs. For easy integration of ICD-11 into AASs, we recommend an AAS data dictionary and propose ICD-11 updates related to external causes of injury. [ABSTRACT FROM AUTHOR]

Airbag pressure determines the restraint effect during a vehicle crash. The pressure required to restrain the occupant depends on pre-crash detection, collision parameters and the occupant's mass and position. This work modulated airbag pressure for optimum safety using a novel airbag control valve for cold-gas inflators. This paper evaluates the valve's stationary and dynamic performances for Helium by 3D flow simulations using a pressure-based solver in ANSYS Fluent® and SAE J2238 laboratory tank tests. The predicted and measured tank pressures for the fully open (stationary) valve were agreed by an average 93.73% with an excellent correlation (correlation coefficient, R = 0.9995). For the first dynamic operation with 10 ms switching time, the results agreed by 92.78% with R = 0.9975. In the second test with 30 ms switching, 83.67% agreement was observed with R = 0.9893. The research concluded that the valve modulates the bag pressure and is implementable in vehicles. [ABSTRACT FROM AUTHOR]