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959 Publications visible to you, out of a total of 959
Deciphering the Transcriptomic Heterogeneity of Duodenal Coeliac Disease Biopsies.
LHA - Leipzig Health Atlas

Abstract (Expand)

Coeliac disease (CD) is a clinically heterogeneous autoimmune disease with variable presentation and progression triggered by gluten intake. Molecular or genetic factors contribute to disease heterogeneity, … but the reasons for different outcomes are poorly understood. Transcriptome studies of tissue biopsies from CD patients are scarce. Here, we present a high-resolution analysis of the transcriptomes extracted from duodenal biopsies of 24 children and adolescents with active CD and 21 individuals without CD but with intestinal afflictions as controls. The transcriptomes of CD patients divide into three groups-a mixed group presenting the control cases, and CD-low and CD-high groups referring to lower and higher levels of CD severity. Persistence of symptoms was weakly associated with subgroup, but the highest marsh stages were present in subgroup CD-high, together with the highest cell cycle rates as an indicator of virtually complete villous atrophy. Considerable variation in inflammation-level between subgroups was further deciphered into immune cell types using cell type de-convolution. Self-organizing maps portrayal was applied to provide high-resolution landscapes of the CD-transcriptome. We find asymmetric patterns of miRNA and long non-coding RNA and discuss the effect of epigenetic regulation. Expression of genes involved in interferon gamma signaling represent suitable markers to distinguish CD from non-CD cases. Multiple pathways overlay in CD biopsies in different ways, giving rise to heterogeneous transcriptional patterns, which potentially provide information about etiology and the course of the disease.

Authors: J. Wolf, E. Willscher, H. Loeffler-Wirth, M. Schmidt, G. Flemming, M. Zurek, H. H. Uhlig, N. Handel, H. Binder

Date Published: 4th Mar 2021

Publication Type: Journal article

Future medical artificial intelligence application requirements and expectations of physicians in German university hospitals: Web-based survey
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

BACKGROUND: The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. … AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians’ requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals. OBJECTIVE: This study aimed to evaluate physicians’ requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany. METHODS: A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS: The online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H4=48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS: Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians’ expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.

Authors: Oliver Maassen, Sebastian Fritsch, Julia Palm, Saskia Deffge, Julian Kunze, Gernot Marx, Morris Riedel, Andreas Schuppert, Johannes Bickenbach

Date Published: 1st Mar 2021

Publication Type: Journal article

Research data management in clinical neuroscience: the national research data infrastructure initiative
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Abstract In clinical neuroscience, there are considerable difficulties in translating basic research into clinical applications such as diagnostic tools or therapeutic interventions. This gap, known … as the “valley of death,” was mainly attributed to the problem of “small numbers” in clinical neuroscience research, i.e. sample sizes that are too small (Hutson et al., 2017). As a possible solution, it has been repeatedly suggested to systematically manage research data to provide long-term storage, accessibility, and federate data. This goal is supported by a current call of the DFG for a national research data infrastructure (NFDI). This article will review current challenges and possible solutions specific to clinical neuroscience and discuss them in the context of other national and international health data initiatives. A successful NFDI consortium will help to overcome not only the “valley of death” but also promises a path to individualized medicine by enabling big data to produce generalizable results based on artificial intelligence and other methods.

Authors: Carsten M Klingner, Petra Ritter, Stefan Brodoehl, Christian Gaser, André Scherag, Daniel Güllmar, Felix Rosenow, Ulf Ziemann, Otto W Witte

Date Published: 2021

Publication Type: Journal article

Health Informatics-Learning Objectiv Navigator (HI-LONa) – eine Open-Access-Plattform für ein agiles Lernzielmanagement
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Hintergrund: Ausgangspunkt von Hi-LONa ist der Auftrag der Medizininformatik-Initiative, die Lehre in der Medizinischen Informatik (MI) zu stärken. Voraussetzung hierzu ist ein Lernzielkatalog, der das … heterogene Feld der MI abdeckt und insbesondere mit dem sich hochdynamisch entwickelnden Fachgebiet[zum vollständigen Text gelangen Sie über die oben angegebene URL]

Authors: Birgit Schneider, Ulrike Schemmann, Lo An Phan-Vogtmann, Stefan Kropf, Cord Spreckelsen

Date Published: 2021

Publication Type: Misc

Comparative evaluation of automated information extraction from pathology reports in three German cancer registries
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Feeding cancer registries with data extracted from textual reports, while maintaining a high level of data quality, has always been a labour-intensive task, due to the heterogeneity of the sources. The … support of this task by IT solutions is expected to accelerate and optimise this process. To this end, the commercial text mining system Averbis Health Discovery was tailored to extract information from free text at the cancer registry of the federal state of Baden-Württemberg. The following entity types were extracted from German-language pathology reports: tumour localisation and morphology, pTNM, grading, (sentinel) nodes examined and affected, laterality and R-class. According to the entity type, several machine learning approaches as well as rules were used for the tumour types breast, prostate, colorectal and skin. Whereas for the pilot site, F values ranged between 0.800 and 0.996, values dropped when applying the extraction pipeline to two new sites (cancer registries Rhineland-Palatinate and Lower Saxony), for morphology from 0.950 to 0.657 and 0.933, and for localisation (topography) from 0.902 to 0.675 and 0.768. There was much less difference with R-class and lymph node counts. A thorough error analysis revealed numerous issues that explain these differences, such as different workflows between the sites, disagreements between textual and coded content as well as different handlings of missing values.

Authors: Stefan Schulz, Sonja Fix, Peter Klügl, Tamira Bachmayer, Tobias Hartz, Martin Richter, Nils Herm-Stapelberg, Philipp Daumke

Date Published: 2021

Publication Type: Journal article

Algorithmic distinction of ARDS and Heart Failure in ICU data from medical embedded systems by using a computer model
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Acute Respiratory Distress Syndrome (ARDS) is a common cause for respiratory failure and has a high mortality rate of 30-40% in most studies. The current standard for the diagnosis of ARDS was proposed … by the Berlin Definition from 2012. This article proposes an algorithmic classification to distinguish between patients with ARDS and those with heart failure (HF). Currently, the available database is not sufficient in regards to the necessary data quality to evaluate this classification. Therefore an approach of simulating data for patients with ARDS and HF by using a computer model was implemented. The model and classification are evaluated using selected patient data, which is recorded with medical embedded systems in intensive care units, as an input for the simulation. The included scores provide a retrospective assessment of whether or not a patient has developed an ARDS.

Authors: Simon Fonck, Sebastian Fritsch, Stefan Kowalewski, Raimund Hensen, André Stollenwerk

Date Published: 2021

Publication Type: Journal article

Ontological representation, classification and data-driven computing of phenotypes.
LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

BACKGROUND: The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the recruitment of participants for clinical and … epidemiological studies. The development of computable phenotype algorithms to solve these tasks is a challenging problem, caused by various reasons. Firstly, the term 'phenotype' has no generally agreed definition and its meaning depends on context. Secondly, the phenotypes are most commonly specified as non-computable descriptive documents. Recent attempts have shown that ontologies are a suitable way to handle phenotypes and that they can support clinical research and decision making. The SMITH Consortium is dedicated to rapidly establish an integrative medical informatics framework to provide physicians with the best available data and knowledge and enable innovative use of healthcare data for research and treatment optimisation. In the context of a methodological use case 'phenotype pipeline' (PheP), a technology to automatically generate phenotype classifications and annotations based on electronic health records (EHR) is developed. A large series of phenotype algorithms will be implemented. This implies that for each algorithm a classification scheme and its input variables have to be defined. Furthermore, a phenotype engine is required to evaluate and execute developed algorithms. RESULTS: In this article, we present a Core Ontology of Phenotypes (COP) and the software Phenotype Manager (PhenoMan), which implements a novel ontology-based method to model, classify and compute phenotypes from already available data. Our solution includes an enhanced iterative reasoning process combining classification tasks with mathematical calculations at runtime. The ontology as well as the reasoning method were successfully evaluated with selected phenotypes including SOFA score, socio-economic status, body surface area and WHO BMI classification based on available medical data. CONCLUSIONS: We developed a novel ontology-based method to model phenotypes of living beings with the aim of automated phenotype reasoning based on available data. This new approach can be used in clinical context, e.g., for supporting the diagnostic process, evaluating risk factors, and recruiting appropriate participants for clinical and epidemiological studies.

Authors: A. Uciteli, C. Beger, T. Kirsten, F. A. Meineke, H. Herre

Date Published: 21st Dec 2020

Publication Type: Journal article

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