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Summary Objective: To identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2020. Method: A 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. Results: The 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. Conclusions. Sensors, 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.

Zusammenfassung Ziel der Studie Zahlreiche Studien weisen auf ein breites Spektrum häufig gleichzeitig auftretender psychischer Störungen wie jene der Generalisierten Angststörung bei der Behandlung von Personen mit Cannabiskonsumstörungen hin (GAD). Bisherige Internet-basierte Studien konnten zwar vereinzelt die Reduktion von Cannabiskonsum zeigen, berücksichtigten aber keine komorbide Störungen. Ziel des E-Coach gestützten Online-Selbsthilfeprogramm CANreduce 2.0 ist die Reduzierung des Cannabiskonsums und der gegebenenfalls vorhandenen GAD. Methodik Sekundäranalyse der in der Allgemeinbevölkerung rekrutierten Cannabiskonsumierenden der CANreduce 2.0 Studie der beiden aktiven Interventionsgruppen (N=367). Für die Analysen wurden diese Teilnehmende aufgrund ihres erreichten Symptomatik-Wertes der General Anxiety Disorder Scale (GAD-7) in 4 Gruppen eingeteilt: Minimale (GAD-7 Werte: 0–4; N=102), Milde (5–9; N=151), Moderate (10–14; N=78) und Schwere (15–21; N=36) Angststörung und anschliessend ihre Behandlungsverläufe mittels Intention-to-Treat Analysen bezüglich ihrer Cannabiskonsumtage und ihrer Werte auf dem Cannabis Use Disorder Identification Test (CUDIT), der Severity of Dependence Scale (SDS), der GAD-7, der Center of Epidemiologic Studies of Depression Scale (CES-D) sowie bezüglich ihrer Adhärenz verglichen. Ergebnisse Alle 4 Gruppen reduzierten unabhängig von ihrer GAD-Symptomatik die Anzahl ihrer Cannabiskonsumtage, ihren CUDIT und SDS Score sowohl 6 Wochen als auch 3 Monate nach der Baselineerhebung. Weiter haben mit Ausnahme von der minimalen GAD-7-Gruppe alle Gruppen eine Reduktion der Angstsymptomatik nach 3 Monaten aufgewiesen. Analog zeigten mit Ausnahme der minimalen GAD-7-Gruppe alle GAD-7-Gruppen eine deutliche Reduktion in der depressiven Symptomatik. Bezüglich der gemessenen Adhärenz zeigten sich keine Unterschiede zwischen den GAD-7-Gruppen. Schlussfolgerungen CANreduce 2.0 scheint sowohl den Cannabiskonsum, die Ausprägung der Cannabiskonsumstörung bzw. die Cannabisabhängigkeit wie auch eine gegebenenfalls vorhandene GAD-Symptomatik zu reduzieren.

ZusammenfassungZiel Untersuchung von Zusammenhängen zwischen der Haptoglobinkonzentration im Blut und dem Auftreten einer Ketose, ausgewählten klinischen Parametern und Lahmheit bei Milchkühen.Material und Methoden Die Datenerhebung fand in 39 bayerischen Milchviehbetrieben über 8 Monate statt. Bei den einbezogenen 712 Fleckvieh- und Braunviehkühen, deren Kalbung 10–30 Tage zurücklag, wurde eine klinische Untersuchung durchgeführt sowie eine Milch- und eine Blutprobe entnommen. Im Blut wurden die Konzentrationen von freien Fettsäuren (non-esterified fatty acids, NEFA), β-Hydroxybutyrat (BHB) und Haptoglobin (Hp) bestimmt, in der Milch die Milchinhaltsstoffe (Fett, Eiweiß, Harnstoff, Laktose, Azeton), BHB, NEFA und die somatische Zellzahl (SCC).Ergebnisse Signifikante Korrelationen ergaben sich zwischen erhöhten Hp-Konzentrationen einerseits und erhöhten NEFA-Konzentrationen in Blut und Milch (p < 0,001), erhöhter SCC (p < 0,001), Lahmheit (p < 0,001), vermindertem Laktosegehalt (p < 0,001) sowie vermindertem Proteingehalt der Milch (p = 0,001) andererseits. In den wärmeren Sommermonaten beprobte Tiere hatten signifikant höhere Hp-Konzentrationen (p < 0,001). Erstkalbinnen wiesen signifikant höhere Hp-Werte auf als multipare Tiere (p < 0,001). Durch die Einteilung der Tiere in 4 Cluster ließ sich ein Hp-Grenzwert bestimmen, der 0,18 mg/ml betrug. Kombiniert mit einem SCC-Grenzwert von 40 500 Zellen/ml Milch konnte der Großteil der subklinisch und klinisch auffälligen Tiere ermittelt werden.Schlussfolgerung und klinische Relevanz Die Messung der Hp-Konzentration im Blut stellt einen praktikablen Ansatz zur Unterstützung des Tiergesundheitsmonitorings im postpartalen Zeitraum dar. Zusammen mit der Auswertung der Milchmenge und der Milchinhaltsstoffe können Abweichungen von physiologischen Zuständen erkannt und betroffene Tiere zeitnah behandelt werden. Haptoglobin kann für die Bewertung des Gesundheitsstatus des Einzeltieres wie auch als Indikator für die Herdengesundheit genutzt werden.

Author(s): Hsu, William; Baumgartner, Christian; Deserno, Thomas M; Section Editors for the IMIA Yearbook Section on Sensors, Signals, and Imaging Informatics | Abstract: 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 identify research works that exemplify recent developments in the field of sensors, signals, and imaging informatics. Method: A broad literature search was conducted using PubMed and Web of Science, supplemented with individual papers that were nominated by section editors. A predefined query made from a combination of Medical Subject Heading (MeSH) terms and keywords were used to search both sources. Section editors then filtered the entire set of retrieved papers with each paper having been reviewed by two section editors. Papers were assessed on a three-point Likert scale by two section editors, rated from 0 (do not include) to 2 (should be included). Only papers with a combined score of 2 or above were considered. Results: A search for papers was executed at the start of January 2019, resulting in a combined set of 1,459 records published in 2018 in 119 unique journals. Section editors jointly filtered the list of candidates down to 14 nominations. The 14 candidate best papers were then ranked by a group of eight external reviewers. Four papers, representing different international groups and journals, were selected as the best papers by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. Conclusions: The fields of sensors, signals, and imaging informatics have rapidly evolved with the application of novel artificial intelligence/machine learning techniques. Studies have been able to discover hidden patterns and integrate different types of data towards improving diagnostic accuracy and patient outcomes. However, the quality of papers varied widely without clear reporting standards for these types of models. Nevertheless, a number of papers have demonstrated useful techniques to improve the generalizability, interpretability, and reproducibility of increasingly sophisticated models.

Summary Objectives: In this work, a cellular automaton software package for simulating different infectious diseases, storing the simulation results in a data warehouse system and analyzing the obtained results to generate prediction models as well as contingency plans, is proposed. The Brisbane H3N2 flu virus, which has been spreading during the winter season 2009, was used for simulation in the federal state of Tyrol, Austria. Methods: The simulation-modeling framework consists of an underlying cellular automaton. The cellular automaton model is parameterized by known disease parameters and geographical as well as demographical conditions are included for simulating the spreading. The data generated by simulation are stored in the back room of the data warehouse using the Talend Open Studio software package, and subsequent statistical and data mining tasks are performed using the tool, termed Knowledge Discovery in Database Designer (KD3). Results: The obtained simulation results were used for generating prediction models for all nine federal states of Austria. Conclusion: The proposed framework provides a powerful and easy to handle interface for parameterizing and simulating different infectious diseases in order to generate prediction models and improve contingency plans for future events.

Summary Objectives: Presently, the protein interaction information concerning different signaling pathways is available in a qualitative manner in different online protein interaction databases. The challenge here is to derive a quantitative way of modeling signaling pathways from qualitative way of modeling signaling pathways from a qualitative level. To address this issue we developed a database that includes mathematical modeling knowledge and biological knowledge about different signaling pathways. Methods: The database is part of an integrative environment that includes environments for pathway design, visualization, simulation and a knowledge base that combines biological and modeling information concerning pathways. The system is designed as a client-server architecture. It contains a pathway designing environment and a simulation environment as upper layers with a relational knowledge base as the underlying layer. Results: DMSP – Database for Modeling Signaling Pathways incorporates biological datasets from online databases like BIND, DIP, PIP, and SPiD. The modeling knowledge that has been incorporated is based on a literature study. Pathway models can be designed, visualized and simulated based on the knowledge stored in the DMSP. The user can download the whole dataset and build pathway models using the knowledge stored in our database. As an example, the TNF? pathway model was implemented and tested using this approach. Conclusion: DMSP is an initial step towards the aim of combining modeling and biological knowledge concerning signaling pathways. It helps in understanding pathways in a qualitative manner from a qualitative level. Simulation results enable the interpretation of a biological system from a quantitative and systemtheoretic point of view.