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85 Publications visible to you, out of a total of 85
IL4I1 is a metabolic immune checkpoint that activates the AHR and promotes tumor progression
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Aryl hydrocarbon receptor (AHR) activation by tryptophan (Trp) catabolites enhances tumor malignancy and suppresses anti-tumor immunity. The context specificity of AHR target genes has so far impeded … systematic investigation of AHR activity and its upstream enzymes across human cancers. A pan-tissue AHR signature, derived by natural language processing, revealed that across 32 tumor entities, interleukin-4-induced-1 (IL4I1) associates more frequently with AHR activity than IDO1 or TDO2, hitherto recognized as the main Trp-catabolic enzymes. IL4I1 activates the AHR through the generation of indole metabolites and kynurenic acid. It associates with reduced survival in glioma patients, promotes cancer cell motility, and suppresses adaptive immunity, thereby enhancing the progression of chronic lymphocytic leukemia (CLL) in mice. Immune checkpoint blockade (ICB) induces IDO1 and IL4I1. As IDO1 inhibitors do not block IL4I1, IL4I1 may explain the failure of clinical studies combining ICB with IDO1 inhibition. Taken together, IL4I1 blockade opens new avenues for cancer therapy.

Authors: Ahmed Sadik, Luis F Somarribas Patterson, Selcen Öztürk, Soumya R Mohapatra, Verena Panitz, Philipp F Secker, Pauline Pfänder, Stefanie Loth, Heba Salem, Mirja Tamara Prentzell, Bianca Berdel, Murat Iskar, Erik Faessler, Friederike Reuter, Isabelle Kirst, Verena Kalter, Kathrin I Foerster, Evelyn Jäger, Carina Ramallo Guevara, Mansour Sobeh, Thomas Hielscher, Gernot Poschet, Annekathrin Reinhardt, Jessica C Hassel, Marc Zapatka, Udo Hahn, Andreas von Deimling, Carsten Hopf, Rita Schlichting, Beate I Escher, Jürgen Burhenne, Walter E Haefeli, Naveed Ishaque, Alexander Böhme, Sascha Schäuble, Kathrin Thedieck, Saskia Trump, Martina Seiffert, Christiane A Opitz

Date Published: 1st Sep 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

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

Abstract (Expand)

OBJECTIVES: We survey recent developments in medical Information Extraction (IE) as reported in the literature from the past three years. Our focus is on the fundamental methodological paradigm shift … from standard Machine Learning (ML) techniques to Deep Neural Networks (DNNs). We describe applications of this new paradigm concentrating on two basic IE tasks, named entity recognition and relation extraction, for two selected semantic classes-diseases and drugs (or medications)-and relations between them. METHODS: For the time period from 2017 to early 2020, we searched for relevant publications from three major scientific communities: medicine and medical informatics, natural language processing, as well as neural networks and artificial intelligence. RESULTS: In the past decade, the field of Natural Language Processing (NLP) has undergone a profound methodological shift from symbolic to distributed representations based on the paradigm of Deep Learning (DL). Meanwhile, this trend is, although with some delay, also reflected in the medical NLP community. In the reporting period, overwhelming experimental evidence has been gathered, as illustrated in this survey for medical IE, that DL-based approaches outperform non-DL ones by often large margins. Still, small-sized and access-limited corpora create intrinsic problems for data-greedy DL as do special linguistic phenomena of medical sublanguages that have to be overcome by adaptive learning strategies. CONCLUSIONS: The paradigm shift from (feature-engineered) ML to DNNs changes the fundamental methodological rules of the game for medical NLP. This change is by no means restricted to medical IE but should also deeply influence other areas of medical informatics, either NLP- or non-NLP-based.

Authors: Udo Hahn, Michel Oleynik

Date Published: 1st 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

Enabling ad-hoc reuse of private data repositories through schema extraction
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

BACKGROUND: Sharing sensitive data across organizational boundaries is often significantly limited by legal and ethical restrictions. Regulations such as the EU General Data Protection Rules (GDPR) … impose strict requirements concerning the protection of personal and privacy sensitive data. Therefore new approaches, such as the Personal Health Train initiative, are emerging to utilize data right in their original repositories, circumventing the need to transfer data. RESULTS: Circumventing limitations of previous systems, this paper proposes a configurable and automated schema extraction and publishing approach, which enables ad-hoc SPARQL query formulation against RDF triple stores without requiring direct access to the private data. The approach is compatible with existing Semantic Web-based technologies and allows for the subsequent execution of such queries in a safe setting under the data provider’s control. Evaluation with four distinct datasets shows that a configurable amount of concise and task-relevant schema, closely describing the structure of the underlying data, was derived, enabling the schema introspection-assisted authoring of SPARQL queries. CONCLUSIONS: Automatically extracting and publishing data schema can enable the introspection-assisted creation of data selection and integration queries. In conjunction with the presented system architecture, this approach can enable reuse of data from private repositories and in settings where agreeing upon a shared schema and encoding a priori is infeasible. As such, it could provide an important step towards reuse of data from previously inaccessible sources and thus towards the proliferation of data-driven methods in the biomedical domain.

Authors: Lars Christoph Gleim, Md Rezaul Karim, Lukas Zimmermann, Oliver Kohlbacher, Holger Stenzhorn, Stefan Decker, Oya Beyan

Date Published: 1st Jul 2020

Publication Type: Journal article

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