Summary
This guide walks you through Linkr end-to-end: workspace, project, OMOP database import, cohort building, analytical dataset creation and then a dashboard. Expect 15 to 20 minutes with a demo dataset.
Client-only
This entire walkthrough runs in the browser alone.Before you start
Make sure you have a Linkr instance ready, either online or local. See the quickstart or local install.
For this walkthrough we’ll use the MIMIC-IV demo OMOP dataset (100 patients, OMOP CDM v5.4) which is loaded automatically the first time you open the app — no import needed.
What about connecting to a real hospital warehouse?
Client-only mode works on local files (extracts, demo datasets). To query a relational warehouse (PostgreSQL, SQL Server, Oracle…) directly, you need full-stack mode, which is under development. See Deployment modes.
Long vs wide format
An OMOP database is in long format: one row per clinical event (one measurement, one diagnosis, one prescription…). That’s the opposite of an Excel spreadsheet, which is in wide format (one row per patient). Linkr is specifically designed to turn long into wide without writing SQL. For more on this distinction, see Data organization — long vs wide format.
Guided walkthrough
The first two steps (creating a workspace and a project) are described in the quickstart. This guide picks up from data exploration.
Open the demo database
On first launch, Linkr automatically loads the MIMIC-IV demo OMOP dataset (100 patients) in the background. You can find it in Data Warehouse → Databases, marked as active (used by default on all warehouse pages in the project).
MIMIC-IV Demo (OMOP)
DuckDB / OMOP CDM 5.4
31 Parquet files
Explore OMOP concepts
In Data Warehouse → Concepts, browse the concepts present in the database, grouped by domain (Condition, Drug, Measurement…). Click a concept to view its statistics (patient count, distribution…). This helps identify the codes you'll use in the cohort.
| Concept ID | Concept Name | Concept Code | Standard Concept | Records | Patients |
|---|---|---|---|---|---|
| 3013682 | Lactate [Moles/volume] in Blood | 32693-4 | Yes | 1,842 | 78 |
| 3016723 | Creatinine [Mass/volume] in Serum or Plasma | 2160-0 | Yes | 2,184 | 96 |
| 3000963 | Hemoglobin [Mass/volume] in Blood | 718-7 | Yes | 1,576 | 92 |
| 132797 | Sepsis | 91302008 | Yes | 312 | 41 |
| 201826 | Type 2 diabetes mellitus | 44054006 | Yes | 268 | 34 |
| 1321341 | Norepinephrine | 7512 | Yes | 487 | 52 |
Build a cohort
In Data Warehouse → Cohorts, create a new cohort by stacking criteria (age, sex, concepts, period, length of stay…) into a logical tree (AND, OR, NOT). Linkr generates the corresponding SQL automatically, which you can inspect and copy. Example: adult patients (age ≥ 18) with a sepsis diagnosis admitted between 2015 and 2020.
Explore a dataset
In Lab → Datasets, you can analyse data in wide format (one row per patient, one column per variable) — the format clinicians and statisticians are used to.
| patient_id | age | sex | sofa_score | length_of_stay | mortality |
|---|---|---|---|---|---|
| P-0001 | 67 | M | 8 | 4.2 | Alive |
| P-0002 | 54 | F | 12 | 9.7 | Deceased |
| P-0003 | 71 | M | 5 | 2.1 | Alive |
| P-0004 | 49 | F | 7 | 6.4 | Alive |
| P-0005 | 82 | M | 11 | 14.3 | Deceased |
| P-0006 | 35 | F | 3 | 1.8 | Alive |
Visualise the distribution of each column, spot missing values and explore your datasets in a few clicks.
From long to wide format: where Linkr creates the link
Today, turning long format (the OMOP warehouse) into wide format (one row per patient) cannot yet be done through the graphical interface: the transformation has to be written in the built-in IDE (Python or R, against the loaded dataset). This is exactly where Linkr creates the link: the same app brings together, side by side, the low-code tools clinicians use and the programming environment data scientists need. Each works at their preferred level of abstraction, on the same project, with the same data — combining the strengths of both worlds in service of health data.
Run an analysis, build a dashboard
On your dataset, run built-in analyses (Table 1, Key Indicator, Plot Builder) without writing code, then compose a dashboard from the resulting widgets. The dashboard is saved automatically inside the project.
Table 1 — cohort characteristics
| Variable | Overall (n=400) | Survivors (n=312) | Deceased (n=88) | p |
|---|---|---|---|---|
| Age (years) | 63.4 ± 16.8 | 61.2 ± 16.1 | 71.5 ± 15.4 | <0.001 |
| Sex — Male (%) | 54.5 | 53.8 | 57.0 | 0.62 |
| SOFA score | 6.8 ± 3.2 | 5.9 ± 2.7 | 10.1 ± 3.5 | <0.001 |
| Length of stay (d) | 5.3 (2.1–9.7) | 4.6 (1.9–8.4) | 8.1 (3.2–14.6) | <0.001 |
Describe your cohort with a Table 1 — no code required.
Save the project
In Versioning → Export, download the project as a ZIP archive. The ZIP follows a canonical structure (scripts/, cohorts/, datasets/, etc.) and can be re-imported into another Linkr instance. Remote Git versioning will be available in full-stack mode, in a future release.
Export
Download your project as a ZIP archive with a git-friendly folder structure.
Data files are excluded by default for security. Enable this only if you need to share the actual dataset contents.
Recap
In six steps you have:
- Opened the auto-loaded MIMIC-IV demo database.
- Explored the database’s concepts.
- Built a visual cohort with generated SQL.
- Explored a dataset in wide format.
- Run an analysis and assembled a dashboard.
- Saved everything through ZIP export.
You did not write a single line of code. If you’re comfortable with Python or R, you can go further in the built-in IDE (the project’s IDE page) to write custom analyses directly on the dataset.
Next steps
- Understand the Data Warehouse → Pipeline → Lab flow — see the “Concepts” section (coming).
- Browse the Projects section (coming) for a detailed tour of each page.
- Revisit deployment modes if you’re considering a shared instance.