README.Rmd

---
title: "ICD9fi"
output:
  github_document:
    toc: true
    df_print: kable
#   html_document:
#     df_print: paged
#     toc: true
---

```{r setup, include=FALSE}
rm(list = ls())

knitr::opts_chunk$set(echo = FALSE)

library(tidyverse)
library(scales)

source("../lib/vocabulary_reporting_fuctions.r")


# Document variables 
N_TOPS <- 10

# output status
db_statuses <- tibble()

```

```{r load, message=FALSE, warning=FALSE}
icd9fi_concept <- read_csv("./1_source_vocabulary/CONCEPT_ICD9fi.csv")

icd9fi_mapsto <- read_csv("./2_mapping_to_standard/CONCEPT_RELATIONSHIP_ICD9fi.csv")

icd9fi_freqs <- read_csv("./3_freq_of_source_codes/freq_total.csv")
```


# Intro
TODO: intro icd9fi


# Formating source vocabulary to OMOP
The icd9fi codes have been ...

TODO: i got it from aki now in the gitlab repo 

The concept names are a mixed of Latin, Finnish, and English !!. These are not translated to English !!. The English name were append by matching the ICD9fi code to the ICD9 code dowloaded from [Wolfbane.com](http://www.wolfbane.com/icd/) (1.6.2020). 

There are 5 tipes of maching : 

TODO

# Mapping the source vocabulary to the standard vocabularies

ICD9fi codes ranked by frequency calculated from  FinnGen-DF5.

Mapping was carried in USAGI by medical student [\@kalleaseppala](github.com/kalleaseppala). 


## Progess in number of codes
```{r}
n_codes <- icd9fi_concept %>% nrow()
n_codes_accepted <- icd9fi_mapsto %>% distinct(concept_id_1) %>% nrow()
```


From `r scales::number(n_codes)` codes `r scales::number(n_codes_accepted)` have been approved. 

This makes `r scales::percent(n_codes_accepted/n_codes)` of codes approved. 


```{r}
db_statuses <- bind_rows(
  tibble(
    status = "mapped",
    n_codes = n_codes_accepted,
    per_codes = percent(n_codes_accepted/!!n_codes),
    n_events = as.double(NA),
    per_events = as.character(NA), 
    db_name = "source"
  ),
 tibble(
    status = "not_mapped",
    n_codes = !!n_codes-n_codes_accepted,
    per_codes = percent((!!n_codes-n_codes_accepted)/!!n_codes),
    n_events =  as.double(NA),
    per_events = as.character(NA), 
    db_name = "source"
  )
)
```





# Assessing coverage of databases


```{r  results='asis'}

#write_csv(db_statuses, "status_table.csv" )
 database_coverage_md("ICD9fi", icd9fi_concept, icd9fi_mapsto, icd9fi_freqs ) 

```