# Linking information between FHIR resources

# Introduction

The purpose of this vignette is to provide details on how FHIR documents are transformed to graphs in BiocFHIR.

This text uses R commands that will work for an R (version 4.2 or greater) in which BiocFHIR (version 0.0.14 or greater) has been installed. The source codes are always available at github and may be available for installation by other means.

# Examining sample data, again

In the “Upper level FHIR concepts” vignette, we used the following code to get a peek at the information structure in a single document representing a Bundle associated with a patient.

    tfile = dir(system.file("json", package="BiocFHIR"), full=TRUE)
    peek = jsonlite::fromJSON(tfile)
    names(peek)

    ## [1] "resourceType" "type"         "entry"

    peek$resourceType

    ## [1] "Bundle"

    names(peek$entry)

    ## [1] "fullUrl"  "resource" "request"

    length(names(peek$entry$resource))

    ## [1] 72

    class(peek$entry$resource)

    ## [1] "data.frame"

    dim(peek$entry$resource)

    ## [1] 301  72

    head(names(peek$entry$resource))

    ## [1] "resourceType" "id"           "text"         "extension"    "identifier"
    ## [6] "name"

We perform a first stage of transformation with `process_fhir_bundle`:

    bu = process_fhir_bundle(tfile)
    bu

    ## BiocFHIR FHIR.bundle instance.
    ##   resource types are:
    ##    AllergyIntolerance CarePlan ... Patient Procedure

# A graph relating patients to conditions

    data(allin)
    g = make_condition_graph(allin)
    g

    ## BiocFHIR.FHIRgraph instance.
    ## A graphNEL graph with directed edges
    ## Number of Nodes = 120
    ## Number of Edges = 348
    ##  50 patients, 70 conditions

    names(g)

    ## [1] "graph"      "patients"   "conditions"

We made a new S3 class to hold the graph with some convenient metadata. Ultimately that metadata should be bound into the graph itself as nodeData and edgeData components.

Because basic identifying information is decomposed into components in FHIR, we have a utility to acquire the patient name for a given bundle.

    getHumanName(allin[[1]]$Patient)

    ## [1] "Ankunding277D'Amore443"

The edges emanating from the node corresponding to this patient are conditions that have been recorded. Edges are retrieved using the `edgeL` method.

    library(graph)
    nodes(g$graph)[edgeL(g$graph)[["Ankunding277D'Amore443"]]$edges]

    ## [1] "Chronic sinusitis (disorder)"
    ## [2] "Normal pregnancy"
    ## [3] "Miscarriage in first trimester"
    ## [4] "Blighted ovum"
    ## [5] "Viral sinusitis (disorder)"
    ## [6] "Acute viral pharyngitis (disorder)"
    ## [7] "Body mass index 30+ - obesity (finding)"
    ## [8] "Sprain of wrist"
    ## [9] "Hyperlipidemia"

# Adding procedures to the graph

We have been unable so far to see how procedures can be linked directly to conditions, except by association with a given patient. We add the procedure information as follows:

    g = add_procedures(g, allin)

    ## ...some bundles had no Procedure component

    g

    ## BiocFHIR.FHIRgraph instance.
    ## A graphNEL graph with directed edges
    ## Number of Nodes = 214
    ## Number of Edges = 864
    ##  50 patients, 70 conditions

Data on additional resources can be added using the methods of `add_procedures`. This will be carried out in future releases.

# Interactive visualization of the graph

A visNetwork widget can be produced directly from a list of ingested bundles. This display can be zoomed and dragged. Procedures are green, patients are blue, conditions are red.

    display_proccond_igraph( build_proccond_igraph( allin ))

    ## ...some bundles had no Procedure component

# Conclusions

This collection of vignettes shows some approaches to working with FHIR R4 JSON using R. It is very likely that a new collection of bundles obtained from a different source would not be properly ingested or transformed by the code present in this version of BiocFHIR. Future extensions of the package will employ direct analysis of JSON structures to identify data values and relationships, that should be more adaptable to diverse collections of documents.

Relationships among resources may be represented in many different ways. This survey of the resources in the synthea bundles is surely limited, perhaps even with respect to the information available in the bundles. FHIR experts are invited to identify gaps in this implementation. We anticipate considerable additional work needed to deal with other contexts such as research studies.

# Session information

    sessionInfo()

    ## R version 4.5.2 (2025-10-31)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## time zone: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] igraph_2.2.1        graph_1.88.1        BiocGenerics_0.56.0
    ## [4] generics_0.1.4      rjsoncons_1.3.2     jsonlite_2.0.0
    ## [7] DT_0.34.0           BiocFHIR_1.12.0     BiocStyle_2.38.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] dplyr_1.1.4          compiler_4.5.2       BiocManager_1.30.27
    ##  [4] BiocBaseUtils_1.12.0 promises_1.5.0       tidyselect_1.2.1
    ##  [7] Rcpp_1.1.1           tidyr_1.3.2          later_1.4.5
    ## [10] jquerylib_0.1.4      yaml_2.3.12          fastmap_1.2.0
    ## [13] mime_0.13            R6_2.6.1             knitr_1.51
    ## [16] htmlwidgets_1.6.4    visNetwork_2.1.4     tibble_3.3.1
    ## [19] maketools_1.3.2      shiny_1.12.1         bslib_0.10.0
    ## [22] pillar_1.11.1        rlang_1.1.7          cachem_1.1.0
    ## [25] httpuv_1.6.16        xfun_0.56            sass_0.4.10
    ## [28] sys_3.4.3            otel_0.2.0           cli_3.6.5
    ## [31] magrittr_2.0.4       crosstalk_1.2.2      digest_0.6.39
    ## [34] xtable_1.8-4         lifecycle_1.0.5      vctrs_0.7.1
    ## [37] evaluate_1.0.5       glue_1.8.0           buildtools_1.0.0
    ## [40] stats4_4.5.2         purrr_1.2.1          rmarkdown_2.30
    ## [43] tools_4.5.2          pkgconfig_2.0.3      htmltools_0.5.9