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85 Publications visible to you, out of a total of 85
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

Towards harmonized data quality in the medical informatics initiative - current state and future directions
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Health data from hospital information systems are valuable sources for medical research but have known issues in terms of data quality. In a nationwide data integration project in Germany, health care … data from all participating university hospitals are being pooled and refined in local centers. As there is currently no overarching agreement on how to deal with errors and implausibilities, meetings were held to discuss the current status and the need to develop consensual measures at the organizational and technical levels. This paper analyzes the discovered similarities and differences. The result shows that although data quality checks are carried out at all sites, there is a lack of both centrally coordinated data quality indicators and a formalization of plausibility rules as well as a repository for automatic querying of the rules, for example in ETL processes.

Authors: Matthias Löbe, Gaetan Kamdje-Wabo, Adriana Carina Sinza, Helmut Spengler, Marcus Strobel, Erik Tute

Date Published: 2022

Publication Type: Journal article

Using machine learning to estimate the calendar age based on autonomic cardiovascular function
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Introduction: Aging is accompanied by physiological changes in cardiovascular regulation that can be evaluated using a variety of metrics. In this study, we employ machine learning on autonomic … cardiovascular indices in order to estimate participants’ age. Methods: We analyzed a database including resting state electrocardiogram and continuous blood pressure recordings of healthy volunteers. A total of 884 data sets met the inclusion criteria. Data of 72 other participants with an BMI indicating obesity (>30 kg/m²) were withheld as an evaluation sample. For all participants, 29 different cardiovascular indices were calculated including heart rate variability, blood pressure variability, baroreflex function, pulse wave dynamics, and QT interval characteristics. Based on cardiovascular indices, sex and device, four different approaches were applied in order to estimate the calendar age of healthy subjects, i.e., relevance vector regression (RVR), Gaussian process regression (GPR), support vector regression (SVR), and linear regression (LR). To estimate age in the obese group, we drew normal-weight controls from the large sample to build a training set and a validation set that had an age distribution similar to the obesity test sample. Results: In a five-fold cross validation scheme, we found the GPR model to be suited best to estimate calendar age, with a correlation of r=0.81 and a mean absolute error of MAE=5.6 years. In men, the error (MAE=5.4 years) seemed to be lower than that in women (MAE=6.0 years). In comparison to normal-weight subjects, GPR and SVR significantly overestimated the age of obese participants compared with controls. The highest age gap indicated advanced cardiovascular aging by 5.7 years in obese participants. Discussion: In conclusion, machine learning can be used to estimate age on cardiovascular function in a healthy population when considering previous models of biological aging. The estimated age might serve as a comprehensive and readily interpretable marker of cardiovascular function. Whether it is a useful risk predictor should be investigated in future studies.

Authors: Andy Schumann, Christian Gaser, Rassoul Sabeghi, P Christian Schulze, Sven Festag, Cord Spreckelsen, Karl-Jürgen Bär

Date Published: 2022

Publication Type: Journal article

Challenges and experiences extending the cBioPortal for cancer genomics to a molecular tumor board platform
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

In Molecular Tumor Boards (MTBs), therapy recommendations for cancer patients are discussed. To aid decision-making based on the patient’s molecular profile, the research platform cBioPortal was extended … based on users’ requirements. Additionally, a comprehensive dockerized workflow was developed to support the deployment of cBioPortal and connected services. In this work, we present the challenges and experiences of nearly two years of implementing and deploying an MTB platform based on cBioPortal and compare those to findings of a previous study.

Authors: Niklas Reimer, Philipp Unberath, Hauke Busch, Melanie Börries, Patrick Metzger, Arsenij Ustjanzew, Christopher Renner, Hans-Ulrich Prokosch, Jan Christoph

Date Published: 1st Nov 2021

Publication Type: InCollection

Pseudonymization of PHI Items in German Clinical Reports
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

We describe the adaptation of a non-clinical pseudonymization system, originally developed for a German email corpus, for clinical use. This tool replaces previously identified Protected Health Information … (PHI) items as carriers of privacy-sensitive information (original names for people, organizations, places, etc.) with semantic type-conformant, yet, fictitious surrogates. We evaluate the generated substitutes for grammatical correctness, semantic and medical plausibility and find particularly low numbers of error instances (less than 1%) on all of these dimensions.

Editor:

Date Published: 27th May 2021

Publication Type: Journal article

Ontological Modelling and Execution of Phenotypic Queries in the Leipzig Health Atlas.
LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Sharing data is of great importance for research in medical sciences. It is the basis for reproducibility and reuse of already generated outcomes in new projects and in new contexts. FAIR data principles … are the basics for sharing data. The Leipzig Health Atlas (LHA) platform follows these principles and provides data, describing metadata, and models that have been implemented in novel software tools and are available as demonstrators. LHA reuses and extends three different major components that have been previously developed by other projects. The SEEK management platform is the foundation providing a repository for archiving, presenting and secure sharing a wide range of publication results, such as published reports, (bio)medical data as well as interactive models and tools. The LHA Data Portal manages study metadata and data allowing to search for data of interest. Finally, PhenoMan is an ontological framework for phenotype modelling. This paper describes the interrelation of these three components. In particular, we use the PhenoMan to, firstly, model and represent phenotypes within the LHA platform. Then, secondly, the ontological phenotype representation can be used to generate search queries that are executed by the LHA Data Portal. The PhenoMan generates the queries in a novel domain specific query language (SDQL), which is specific for data management systems based on CDISC ODM standard, such as the LHA Data Portal. Our approach was successfully applied to represent phenotypes in the Leipzig Health Atlas with the possibility to execute corresponding queries within the LHA Data Portal.

Authors: A. Uciteli, C. Beger, J. Wagner, A. Kiel, F. A. Meineke, S. Staubert, M. Lobe, R. Hansel, J. Schuster, T. Kirsten, H. Herre

Date Published: 24th May 2021

Publication Type: Journal article

Concept and implementation of data usage proposal process based on international standards in SMITH
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Accessing secondary-use healthcare data in Germany requires contracting with each organization that acts as a data provider. The SMITH Service Platform offers a central access point for scientists, … facilitating contracting as part of an integrated data use and access process with several Data Integration Centers (DIC) at once. Process support is realized by a central Business Process Engine (BPE), which manages process definition and process control, combined with a central IHE infrastructure. The use of IHE XDS and IHE XDW profiles enables the exchange of process instance information with multiple distributed visualization and user interaction tools for provided user tasks based on international standards. User task information include structured forms for submitting instructions and results as task input and output for the users, and are synchronized between the shared process instance and the BPE. A reference user interface is also provided with the SMITH Marketplace. In the future, further standardization efforts regarding the structured forms and the use of the IHE XDW profile should be pursued.

Authors: Marcel Klötgen, Eric Fiege, Salima Houta

Date Published: 1st May 2021

Publication Type: InCollection

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