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

Area of Science:

• Systems biology and medicine
• Medical Informatics
• Computational biology

Background:

• Systems medicine applies systems biology for patient care decision-making in complex diseases.
• Integrating diverse data sources into computational models presents a significant challenge for Medical Informatics.
• Understanding current modeling methods and data sources is crucial for prioritizing systems medicine research.

Purpose of the Study:

• To systematically review and capture the current utilization of modeling methods in systems medicine research.
• To identify and categorize the data sources employed in systems medicine-related projects.

Main Methods:

• Systematic literature review of MEDLINE and ScienceDirect databases using "systems medicine" and related terms.
• Full-text screening and assessment of retrieved articles following PRISMA guidelines.
• Classification of identified modeling approaches and data types.

Main Results:

• 34 papers were included from 698 initial articles, revealing diverse modeling approaches like machine learning and network analysis.
• No modeling methods exclusive to systems medicine were identified; common approaches are used across domains.
• Omics and clinical data are prevalent, but most studies incorporate a limited number of data sources.

Conclusions:

• A wide array of modeling approaches are currently used in systems medicine, necessitating flexible, modular solutions for clinical applications.
• Limited data source integration and a focus on prognosis hinder the full potential of systems medicine.
• Future efforts must expand data inclusion and prioritize treatment strategy development for enhanced patient care.