index.Rmd

---
title: "Processing ehrlink"
output:
  html_document:
    theme: cosmo
    highlight: pygments
---

```{r, message=FALSE}
library(dplyr)
library(DT)
library(ggplot2)

options(stringsAsFactors=FALSE)
```

```{r}
# read ehrlink problem-medication pairs
ehrlink.df <- file.path('download', 'amiajnl-2012-000852-s1.tsv') %>%
  read.delim()

# read rxnorm concept to rxnorm active ingredient mapping
ingredient_map.df <- file.path('data', 'rxnorm-as-ingredient.tsv') %>%
  read.delim()

# read ehrlink medication to rxnorm mapping
medication_map.df <- 'https://raw.githubusercontent.com/antoine-lizee/RRxNorm/531bfcb892ca82ba3a60e9ba42b09373977f08ef/Output/allResolvedMatches.csv' %>%
  read.csv(na.strings = '')

# read ehrlink problem to DO (disease ontology) mapping
problem_map.df <- file.path('data', 'problem-to-doid.tsv') %>%
  read.delim(na.strings = '')
```

```{r}

mapped.df <- ehrlink.df %>% dplyr::inner_join(
  medication_map.df %>%
    dplyr::filter(score >= 55) %>%
    dplyr::select(medication_definition_id, rxcui)
) %>% dplyr::inner_join(
  ingredient_map.df %>%
    dplyr::filter(n_ingredients == 1) %>%
    dplyr::transmute(rxcui = input_rxcui, ingredient_rxcui, ingredient_name)
) %>% dplyr::inner_join(
  problem_map.df %>%
    dplyr::filter(! is.na(doid)) %>%
    dplyr::distinct(problem_definition_id, doid) %>%
    dplyr::transmute(problem_definition_id, doid_code = doid, doid_name = name)
)

mapped.df %>%
  write.table(file.path('data', 'indications.tsv'), quote=FALSE, row.names=FALSE, sep='\t')

mapped.df %>% DT::datatable(rownames=F)

# DT::datatable(ehrlink.df)
# DT::datatable(ingredient_map.df)
# DT::datatable(medication_map.df)
# DT::datatable(problem_map.df)
# DT::datatable(doslim_map.df)
```