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84 Publications visible to you, out of a total of 84
Towards an ontology-based phenotypic query model
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Clinical research based on data from patient or study data management systems plays an important role in transferring basic findings into the daily practices of physicians. To support study recruitment, … diagnostic processes, and risk factor evaluation, search queries for such management systems can be used. Typically, the query syntax as well as the underlying data structure vary greatly between different data management systems. This makes it difficult for domain experts (e.g., clinicians) to build and execute search queries. In this work, the Core Ontology of Phenotypes is used as a general model for phenotypic knowledge. This knowledge is required to create search queries that determine and classify individuals (e.g., patients or study participants) whose morphology, function, behaviour, or biochemical and physiological properties meet specific phenotype classes. A specific model describing a set of particular phenotype classes is called a Phenotype Specification Ontology. Such an ontology can be automatically converted to search queries on data management systems. The methods described have already been used successfully in several projects. Using ontologies to model phenotypic knowledge on patient or study data management systems is a viable approach. It allows clinicians to model from a domain perspective without knowing the actual data structure or query language.

Authors: Christoph Beger, Franz Matthies, Ralph Schäfermeier, Toralf Kirsten, Heinrich Herre, Alexandr Uciteli

Date Published: 1st May 2022

Publication Type: Journal article

Generative adversarial networks for biomedical time series forecasting and imputation
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

In the present systematic review we identified and summarised current research activities in the field of time series forecasting and imputation with the help of generative adversarial networks (GANs). … We differentiate between imputation which describes the filling of missing values at intermediate steps and forecasting defining the prediction of future values. Especially the utilisation of such methods in the biomedical domain was to be investigated. To this end, 1057 publications were identified with the help of PubMed, Web of Science and Scopus. All studies that describe the use of GANs for the imputation/forecasting of time series were included irrespective of the application domain. Finally, 33 records were identified as eligible and grouped according to the topologies, losses, inputs and outputs of the presented GANs. In combination with a summary of all described application domains, this grouping served as a basis for analysing the peculiarities of the method in the biomedical context. Due to the broad spectrum of biomedical research, nearly all recognised methodologies are also applied in this domain. We could not identify any approach that proved itself superior in the biomedical area. Although GANs were initially designed to work in the image domain, many publications show that they are capable of imputing/forecasting non-visual time series.

Authors: Sven Festag, Joachim Denzler, Cord Spreckelsen

Date Published: 1st May 2022

Publication Type: Journal article

Implementation of a clinical trial recruitment support system based on fast healthcare interoperability resources (FHIR) in a cardiology department
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Clinical Trial Recruitment Support Systems can booster patient inclusion of clinical trials by automatically analyzing eligibility criteria based on electronic health records. However, missing … interoperability has hindered introduction of those systems on a broader scale. Therefore, our aim was to develop a recruitment support system based on FHIR R4 and evaluate its usage and features in a cardiology department. Clinical conditions, anamnesis, examinations, allergies, medication, laboratory data and echocardiography results were imported as FHIR resources. Clinical trial information, eligibility criteria and recruitment status were recorded using the appropriate FHIR resources without extensions. Eligibility criteria linked by the logical operation “OR” were represented by using multiple FHIR Group resources for enrollment. The system was able to identify 52 of 55 patients included in four clinical trials. In conclusion, use of FHIR for defining eligibility criteria of clinical trials may facilitate interoperability and allow automatic screening for eligible patients at multiple sites of different healthcare providers in the future. Upcoming changes in FHIR should allow easier description of “OR”-linked eligibility criteria.

Authors: Clemens Scherer, Stephan Endres, Martin Orban, Stefan Kääb, Steffen Massberg, Alfred Winter, Matthias Löbe

Date Published: 1st May 2022

Publication Type: Journal article

Shift of radiotherapy use during the first wave of the COVID-19 pandemic? An analysis of German inpatient data
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

OBJECTIVE: To assess the change in inpatient radiotherapy related to COVID-19 lockdown measures during the first wave of the pandemic in 2020. METHODS: We included cases hospitalized between January 1 … and August 31, 2018-2020, with a primary ICD-10 diagnosis of C00-C13, C32 (head and neck cancer, HNC) and C53 (cervical cancer, CC). Data collection was conducted within the Medical Informatics Initiative. Outcomes were fractions and admissions. Controlling for decreasing hospital admissions during holidays, calendar weeks of 2018/2019 were aligned to Easter 2020. A lockdown period (LP; 16/03/2020-02/08/2020) and a return-to-normal period (RNP; 04/05/2020-02/08/2020) were defined. The study sample comprised a control (admission 2018/19) and study cohort (admission 2020). We computed weekly incidence and IR ratios from generalized linear mixed models. RESULTS: We included 9365 (CC: 2040, HNC: 7325) inpatient hospital admissions from 14 German university hospitals. For CC, fractions decreased by 19.97% in 2020 compared to 2018/19 in the LP. In the RNP the reduction was 28.57% (p < 0.001 for both periods). LP fractions for HNC increased by 10.38% (RNP: 9.27%; p < 0.001 for both periods). Admissions for CC decreased in both periods (LP: 10.2%, RNP: 22.14%), whereas for HNC, admissions increased (LP: 2.25%, RNP: 1.96%) in 2020. Within LP, for CC, radiotherapy admissions without brachytherapy were reduced by 23.92%, whereas surgery-related admissions increased by 20.48%. For HNC, admissions with radiotherapy increased by 13.84%, while surgery-related admissions decreased by 11.28% in the same period. CONCLUSION: Related to the COVID-19 lockdown in an inpatient setting, radiotherapy for HNC treatment became a more frequently applied modality, while admissions of CC cases decreased.

Authors: Daniel Medenwald, Thomas Brunner, Hans Christiansen, Ulrich Kisser, Sina Mansoorian, Dirk Vordermark, Hans-Ulrich Prokosch, Susanne A Seuchter, Lorenz A Kapsner, our MII research group

Date Published: 1st Apr 2022

Publication Type: Journal article

Multi-institutional breast cancer detection using a secure on-boarding service for Distributed Analytics
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

The constant upward movement of data-driven medicine as a valuable option to enhance daily clinical practice has brought new challenges for data analysts to get access to valuable but sensitive data … due to privacy considerations. One solution for most of these challenges are Distributed Analytics (DA) infrastructures, which are technologies fostering collaborations between healthcare institutions by establishing a privacy-preserving network for data sharing. However, in order to participate in such a network, a lot of technical and administrative prerequisites have to be made, which could pose bottlenecks and new obstacles for non-technical personnel during their deployment. We have identified three major problems in the current state-of-the-art. Namely, the missing compliance with FAIR data principles, the automation of processes, and the installation. In this work, we present a seamless on-boarding workflow based on a DA reference architecture for data sharing institutions to address these problems. The on-boarding service manages all technical configurations and necessities to reduce the deployment time. Our aim is to use well-established and conventional technologies to gain acceptance through enhanced ease of use. We evaluate our development with six institutions across Germany by conducting a DA study with open-source breast cancer data, which represents the second contribution of this work. We find that our on-boarding solution lowers technical barriers and efficiently deploys all necessary components and is, therefore, indeed an enabler for collaborative data sharing.

Authors: Sascha Welten, Lars Hempel, Masoud Abedi, Yongli Mou, Mehrshad Jaberansary, Laurenz Neumann, Sven Weber, Kais Tahar, Yeliz Ucer Yediel, Matthias Löbe, Stefan Decker, Oya Beyan, Toralf Kirsten

Date Published: 1st Apr 2022

Publication Type: Journal article

Reduction of platelet outdating and shortage by forecasting demand with statistical learning and deep neural networks: Modeling study
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

BACKGROUND: Platelets are a valuable and perishable blood product. Managing platelet inventory is a demanding task because of short shelf lives and high variation in daily platelet use patterns. … Predicting platelet demand is a promising step toward avoiding obsolescence and shortages and ensuring optimal care. OBJECTIVE: The aim of this study is to forecast platelet demand for a given hospital using both a statistical model and a deep neural network. In addition, we aim to calculate the possible reduction in waste and shortage of platelets using said predictions in a retrospective simulation of the platelet inventory. METHODS: Predictions of daily platelet demand were made by a least absolute shrinkage and selection operator (LASSO) model and a recurrent neural network (RNN) with long short-term memory (LSTM). Both models used the same set of 81 clinical features. Predictions were passed to a simulation of the blood inventory to calculate the possible reduction in waste and shortage as compared with historical data. RESULTS: From January 1, 2008, to December 31, 2018, the waste and shortage rates for platelets were 10.1% and 6.5%, respectively. In simulations of platelet inventory, waste could be lowered to 4.9% with the LASSO and 5% with the RNN, whereas shortages were 2.1% and 1.7% with the LASSO and RNN, respectively. Daily predictions of platelet demand for the next 2 days had mean absolute percent errors of 25.5% (95% CI 24.6%-26.6%) with the LASSO and 26.3% (95% CI 25.3%-27.4%) with the LSTM (P=.01). Predictions for the next 4 days had mean absolute percent errors of 18.1% (95% CI 17.6%-18.6%) with the LASSO and 19.2% (95% CI 18.6%-19.8%) with the LSTM (P<.001). CONCLUSIONS: Both models allow for predictions of platelet demand with similar and sufficient accuracy to significantly reduce waste and shortage in a retrospective simulation study. The possible improvements in platelet inventory management are roughly equivalent to US $250,000 per year.

Authors: Maximilian Schilling, Lennart Rickmann, Gabriele Hutschenreuter, Cord Spreckelsen

Date Published: 1st Feb 2022

Publication Type: Journal article

Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Objective: The attitudes about the usage of artificial intelligence in healthcare are controversial. Unlike the perception of healthcare professionals, the attitudes of patients and their companions … have been of less interest so far. In this study, we aimed to investigate the perception of artificial intelligence in healthcare among this highly relevant group along with the influence of digital affinity and sociodemographic factors. Methods: We conducted a cross-sectional study using a paper-based questionnaire with patients and their companions at a German tertiary referral hospital from December 2019 to February 2020. The questionnaire consisted of three sections examining (a) the respondents’ technical affinity, (b) their perception of different aspects of artificial intelligence in healthcare and (c) sociodemographic characteristics. Results: From a total of 452 participants, more than 90% already read or heard about artificial intelligence, but only 24% reported good or expert knowledge. Asked on their general perception, 53.18% of the respondents rated the use of artificial intelligence in medicine as positive or very positive, but only 4.77% negative or very negative. The respondents denied concerns about artificial intelligence, but strongly agreed that artificial intelligence must be controlled by a physician. Older patients, women, persons with lower education and technical affinity were more cautious on the healthcare-related artificial intelligence usage. Conclusions: German patients and their companions are open towards the usage of artificial intelligence in healthcare. Although showing only a mediocre knowledge about artificial intelligence, a majority rated artificial intelligence in healthcare as positive. Particularly, patients insist that a physician supervises the artificial intelligence and keeps ultimate responsibility for diagnosis and therapy.

Authors: Sebastian J Fritsch, Andrea Blankenheim, Alina Wahl, Petra Hetfeld, Oliver Maassen, Saskia Deffge, Julian Kunze, Rolf Rossaint, Morris Riedel, Gernot Marx, Johannes Bickenbach

Date Published: 2022

Publication Type: Journal article

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