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1004 Publications visible to you, out of a total of 1004
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

SNIK Quiz: A Multiple Choice Game About Information Management in Hospitals
Semantisches Netz des integrierten Informationsmanagements im Krankenhaus (SNIK)

Abstract

Not specified

Authors: Konrad Höffner, Arne Roszeitis, Max Niclas Wächtler, Franziska Jahn, Alfred Winter

Date Published: 2022

Publication Type: Journal article

Automatische Generierung komplexer Fragen zum Informationsmanagement auf der Basis der SNIK-Ontologie
Semantisches Netz des integrierten Informationsmanagements im Krankenhaus (SNIK)

Abstract

Not specified

Author: Arne Roszeitis

Date Published: 2022

Publication Type: Masters Thesis

Question Answering auf SNIK
Semantisches Netz des integrierten Informationsmanagements im Krankenhaus (SNIK)

Abstract

Not specified

Author: Hannes Raphael Brunsch

Date Published: 2022

Publication Type: Masters Thesis

Automatische Erstellung von Quizfragen aus einer Ontologie von Krankenhausinformationssystemen
Semantisches Netz des integrierten Informationsmanagements im Krankenhaus (SNIK)

Abstract

Not specified

Author: Max Niclas Wächtler

Date Published: 2022

Publication Type: Masters Thesis

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