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228 Publications visible to you, out of a total of 228
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

Towards GFO 2.0: Architecture, Modules and Applications
Onto-Med Research Group

Abstract

Not specified

Authors: Patryk Burek, Frank Loebe, Heinrich Herre

Date Published: 22nd Oct 2020

Publication Type: Journal article

Medical Information Extraction in the Age of Deep Learning
SMITH - Smart Medical Information Technology for Healthcare

Abstract

Not specified

Authors: Udo Hahn, Michel Oleynik

Date Published: 21st Aug 2020

Publication Type: Journal article

Effect size estimates from umbrella designs: Handling patients with a positive test result for multiple biomarkers using random or pragmatic subtrial allocation
SMITH - Smart Medical Information Technology for Healthcare

Abstract

Not specified

Authors: Miriam Kesselmeier, Norbert Benda, André Scherag

Date Published: 14th Aug 2020

Publication Type: Journal article

What Makes a Top-Performing Precision Medicine Search Engine?
SMITH - Smart Medical Information Technology for Healthcare

Abstract

Not specified

Authors: Jimmy Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, Yiqun Liu, Erik Faessler, Michel Oleynik, Udo Hahn

Date Published: 25th Jul 2020

Publication Type: InProceedings

GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

The lack of publicly available text corpora is a major obstacle for progress in clinical natural language processing, for non-English speaking countries in particular. In this work, we present GGPONC … (German Guideline Program in Oncology NLP Corpus), a freely distributable German language corpus based on clinical practice guidelines in the field of oncology. The corpus is one of the largest corpora of German medical text to date. It does not contain any patient-related data and can therefore be used without data protection restrictions. Moreover, it is the first corpus for the German language covering diverse conditions in a large medical subfield. In addition to the textual sources, we provide a large variety of metadata, such as literature references and evidence levels. By applying and evaluating existing medical information extraction pipelines for German text, we are able to draw comparisons for the use of medical language to other medical text corpora.

Authors: F. Borchert, C. Lohr, L. Modersohn, T. Langer, M. Follmann, J. P. Sachs, U. Hahn, M. P. Schapranow

Date Published: 13th Jul 2020

Publication Type: Misc

Problems in FAIRifying Medical Datasets.
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Despite their young age, the FAIR principles are recognised as important guidelines for research data management. Their generic design, however, leaves much room for interpretation in domain-specific … application. Based on practical experience in the operation of a data repository, this article addresses problems in FAIR provisioning of medical data for research purposes in the use case of the Leipzig Health Atlas project and shows necessary future developments.

Authors: M. Lobe, F. Matthies, S. Staubert, F. A. Meineke, A. Winter

Date Published: 16th Jun 2020

Publication Type: Journal article

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