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

Future mobile device usage, requirements, and expectations of physicians in German university hospitals: Web-based survey
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Background The use of mobile devices in hospital care constantly increases. However, smartphones and tablets have not yet widely become official working equipment in medical care. Meanwhile, the parallel … use of private and official devices in hospitals is common. Medical staff use smartphones and tablets in a growing number of ways. This mixture of devices and how they can be used is a challenge to persons in charge of defining strategies and rules for the usage of mobile devices in hospital care. Objective Therefore, we aimed to examine the status quo of physicians’ mobile device usage and concrete requirements and their future expectations of how mobile devices can be used. Methods We performed a web-based survey among physicians in 8 German university hospitals from June to October 2019. The online survey was forwarded by hospital management personnel to physicians from all departments involved in patient care at the local sites. Results A total of 303 physicians from almost all medical fields and work experience levels completed the web-based survey. The majority regarded a tablet (211/303, 69.6%) and a smartphone (177/303, 58.4%) as the ideal devices for their operational area. In practice, physicians are still predominantly using desktop computers during their worktime (mean percentage of worktime spent on a desktop computer: 56.8%; smartphone: 12.8%; tablet: 3.6%). Today, physicians use mobile devices for basic tasks such as oral (171/303, 56.4%) and written (118/303, 38.9%) communication and to look up dosages, diagnoses, and guidelines (194/303, 64.0%). Respondents are also willing to use mobile devices for more advanced applications such as an early warning system (224/303, 73.9%) and mobile electronic health records (211/303, 69.6%). We found a significant association between the technical affinity and the preference of device in medical care (\chis2=53.84, P Conclusions Physicians in German university hospitals have a high technical affinity and positive attitude toward the widespread implementation of mobile devices in clinical care. They are willing to use official mobile devices in clinical practice for basic and advanced mobile health uses. Thus, the reason for the low usage is not a lack of willingness of the potential users. Challenges that hinder the wider adoption of mobile devices might be regulatory, financial and organizational issues, and missing interoperability standards of clinical information systems, but also a shortage of areas of application in which workflows are adapted for (small) mobile devices.

Authors: Oliver Maassen, Sebastian Fritsch, Julia Gantner, Saskia Deffge, Julian Kunze, Gernot Marx, Johannes Bickenbach

Date Published: 1st Dec 2020

Publication Type: Journal article

Ontological representation, classification and data-driven computing of phenotypes
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: Alexandr Uciteli, Christoph Beger, Toralf Kirsten, Frank A Meineke, Heinrich Herre

Date Published: 1st Dec 2020

Publication Type: Journal article

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