Models and Data Sources Used in Systems Medicine: A Systematic Literature Review

Löpprich, M.; Karmen, C.; Ganzinger, M.; Gietzelt, M.

doi:10.3414/me15-01-0151

Cited by 15Electronic Health Records SystemsBioinformatics and Genomic NetworksMachine Learning in Healthcare

Models and Data Sources Used in Systems Medicine: A Systematic Literature Review

Löpprich, M., Karmen, C., Ganzinger, M., Gietzelt, M.·2016·Methods of Information in Medicine

DOI 10.3414/me15-01-0151

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Abstract

Summary: Background: Systems medicine is a new approach for the development and selection of treatment strategies for patients with complex diseases. It is often referred to as the application of systems biology methods for decision making in patient care. For systems medicine computer applications, many different data sources have to be integrated and included into models. This is a challenging task for Medical Informatics since the approach exceeds traditional systems like Electronic Health Records. To prioritize research activities for systems medicine applications, it is necessary to get an overview over modelling methods and data sources already used in this field. Objectives: We performed a systematic literature review with the objective to capture current use of 1) modelling methods and 2) data sources in systems medicine related research projects. Methods: We queried the MEDLINE and ScienceDirect databases for papers associated with the search term systems medicine and related terms. Papers were screened and assessed in full text in a two-step process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines. Results: The queries returned 698 articles of which 34 papers were finally included into the study. A multitude of modelling approaches such as machine learning and network analysis was identified and classified. Since these approaches are also used in other domains, no methods specific for systems medicine could be identified. Omics data are the most widely used data types followed by clinical data. Most studies only include a rather limited number of data sources. Conclusions: Currently, many different modelling approaches are used in systems medicine. Thus, highly flexible modular solutions are necessary for systems medicine clinical applications. However, the number of data sources included into the models is limited and most projects currently focus on prognosis. To leverage the potential of systems medicine further, it will be necessary to focus on treatment strategies for patients and consider a broader range of data.

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From Integrated multimodal artificial intelligence framework for healthcare applications (2022) · cites this paper as [3]

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Integrated multimodal artificial intelligence framework for healthcare applications
Soenksen, L. R., Ma, Y., Zeng, C., Boussioux, L., Villalobos Carballo, K., Na, L., Wiberg, H. M., Li, M. L., Fuentes, I., & Bertsimas, D. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00689-4 · Cited by 380 · see citing works
A Machine Learning-based Method for Question Type Classification in Biomedical Question Answering
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Authors: Löpprich, M., Karmen, C., Ganzinger, M., & Gietzelt, M.
Affiliations: Heidelberg University (DE)
Corresponding author: Ganzinger, M.
ORCID: Ganzinger, M.
Gietzelt, M.
Venue: Methods of Information in Medicine
Publisher: Georg Thieme Verlag KG
Published: 2016
Volume: 55
Issue: 02
Pages: 107-113
ISSN: 0026-1270 (ISSN-L)
2511-705X
DOI: 10.3414/me15-01-0151
OpenAlex: W2287839963
Cited by: 15
References: 53
Citations by year: 2024: 2 · 2022: 1 · 2021: 1 · 2020: 1 · 2019: 2 · 2018: 3 · 2017: 5
FWCI: 0.67
Type: journal article
Language: en
Open access: closed
Locations: Methods of Information in Medicine (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords: systems medicine, systems biology, modeling methodology, data sources, omics data, clinical data, machine learning, network analysis, data integration, prognosis, treatment options, complex diseases, clinical decision support
MeSH terms: Models, Theoretical, Statistics as Topic, Systems Analysis, Information Storage and Retrieval

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Models and Data Sources Used in Systems Medicine: A Systematic Literature Review

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