Graph-Representation of Patient Data: a Systematic Literature Review.

Graph theory is a well-established theory with many methods used in mathematics to study graph structures. In the field of medicine, electronic health records (EHR) are commonly used to store and analyze patient data. Consequently, it seems straight-forward to perform research on modeling EHR data as graphs. This systematic literature review aims to investigate the frontiers of the current research in the field of graphs representing and processing patient data. We want to show, which areas of research in this context need further investigation. The databases MEDLINE, Web of Science, IEEE Xplore and ACM digital library were queried by using the search terms health record, graph and related terms. Based on the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA) statement guidelines the articles were screened and evaluated using full-text analysis. Eleven out of 383 articles found in systematic literature review were finally included for analysis in this literature review. Most of them use graphs to represent temporal relations, often representing the connection among laboratory data points. Only two papers report that the graph data were further processed by comparing the patient graphs using similarity measurements. Graphs representing individual patients are hardly used in research context, only eleven papers considered such kind of graphs in their investigations. The potential of graph theoretical algorithms, which are already well established, could help increasing this research field, but currently there are too few papers to estimate how this area of research will develop. Altogether, the use of such patient graphs could be a promising technique to develop decision support systems for diagnosis, medication or therapy of patients using similarity measurements or different kinds of analysis.

Identification of Cargo for Adaptor Protein (AP) Complexes 3 and 4 by Sucrose Gradient Profiling.

Intracellular vesicle trafficking is a fundamental process in eukaryotic cells. It enables cellular polarity and exchange of proteins between subcellular compartments such as the plasma membrane or the vacuole. Adaptor protein complexes participate in the vesicle formation by specific selection of the transported cargo. We investigated the role of the adaptor protein complex 3 (AP-3) and adaptor protein complex 4 (AP-4) in this selection process by screening for AP-3 and AP-4 dependent cargo proteins. Specific cargo proteins are expected to be mis-targeted in knock-out mutants of adaptor protein complex components. Thus, we screened for altered distribution profiles across a density gradient of membrane proteins in wild type versus ap-3ß and ap-4ß knock-out mutants. In ap-3ß mutants, especially proteins with transport functions, such as aquaporins and plasma membrane ATPase, as well as vesicle trafficking proteins showed differential protein distribution profiles across the density gradient. In the ap-4ß mutant aquaporins but also proteins from lipid metabolism were differentially distributed. These proteins also showed differential phosphorylation patterns in ap-3ß and ap-4ß compared with wild type. Other proteins, such as receptor kinases were depleted from the AP-3 mutant membrane system, possibly because of degradation after mis-targeting. In AP-4 mutants, membrane fractions were depleted for cytochrome P450 proteins, cell wall proteins and receptor kinases. Analysis of water transport capacity in wild type and mutant mesophyll cells confirmed aquaporins as cargo proteins of AP-3 and AP-4. The combination of organelle density gradients with proteome analysis turned out as a suitable experimental strategy for large-scale analyses of protein trafficking.

A Concept for Graph-Based Temporal Similarity of Patient Data.

Computer-based decision support systems are often used for dedicated tasks such as the detection of sepsis. However, positive predictive values for sepsis detection are reported to achieve only around 46%. In this paper we describe a novel approach to use temporal data of electronic patient records based on similarity measures. We apply the concept of case-based reasoning, which is well-established in many fields of medical informatics. Temporal patient data are organized in a time-graph structure. For the quantification of similarity between cases, we exploit graph theory based approaches. For development and evaluation of our time-graph similarity frame we use the open MIMIC III dataset. In a later phase, we envision to transfer our concept from sepsis to other diseases.