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        <r:String xml:lang="sv">ED Atlas a posteriori dragningar</r:String>
        <r:String xml:lang="en">ED Atlas Posterior Sample Files</r:String>
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          <r:String xml:lang="sv">Uppsala universitet</r:String>
          <r:String xml:lang="en">Uppsala University</r:String>
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          <r:String xml:lang="sv">Uppsala universitet</r:String>
          <r:String xml:lang="en">Uppsala University</r:String>
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        <r:SimpleDate>2026-11-13</r:SimpleDate>
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      <r:Content xml:lang="sv">Datamaterialet innehåller de anpassade statistiska modeller som ligger bakom ED Atlas. Det är bayesianska logistiska regressionsmodeller som ger sannolikheten för ett utfall efter ett besök på akutmottagning, givet patientens kön, ålder och sökorsak. Modellerna har tränats på den svenska SwED-kohorten, som omfattar akutbesök i [regioner/sjukhus, år, ungefärligt antal besök]. SwED är kopplad till Patientregistret och Dödsorsaksregistret (Socialstyrelsen).

Materialet består av modellparametrar. Det innehåller inga individdata och inga personuppgifter. Varje modell lagras som en NetCDF-fil (.nc) med posteriorsampel för regressionskoefficienterna, framtagna med MCMC (PyMC 5.17.0, numpyro-sampler, 8 000 dragningar per modell). Filerna kan läsas med valfri NetCDF/HDF5-läsare, till exempel ArviZ/xarray i Python.

Materialet innehåller:

En modell för 30-dagarsmortalitet, definierad som död inom 30 dagar efter ankomst till akutmottagningen, samt en basmodell med enbart kön och ålder.
930 diagnosmodeller, en per ICD-10-kapitel, ICD-10-block eller ICD-10-kod på tre tecken. Varje modell ger sannolikheten att diagnosen registreras [definiera: tidsfönster och vårdform] efter akutbesöket.
För varje modell en CSV-fil som anger kolumnordningen (vilka SNOMED CT-koder för sökorsak modellen innehåller).
En README med steg-för-steg-instruktioner, i pseudokod, för hur man gör en prediktion utifrån kön, ålder och sökorsak.

Indata till modellerna är kön, ålder (vuxna, 18 år och äldre) och en sökorsak kodad i SNOMED CT. Ålder modelleras med en naturlig kubisk spline, och kön, ålder och sökorsak interagerar. Sökorsaker anges med SNOMED CT-begreppsidentifierare. SNOMED CT-termerna ingår inte i materialet.

Materialet kan återanvändas för att beräkna riskuppskattningar för nya patienter, för att jämföra med andra modeller eller kohorter, eller för att studera hur sökorsak, ålder och kön hänger ihop med utfall.</r:Content>
      <r:Content xml:lang="en">This dataset contains the fitted statistical models behind the ED Atlas. They are Bayesian logistic regression models that give the probability of an outcome after an emergency department (ED) visit, given the patient’s sex, age and presenting complaint. The models were trained on the Swedish SwED cohort, which covers ED visits in [regions/hospitals, years, approx. number of visits]. SwED is linked to the National Patient Register and the Cause of Death Register (Socialstyrelsen).

The material is model parameters. It contains no individual-level data and no personal data. Each model is stored as a NetCDF file (.nc) with posterior samples of the regression coefficients, drawn with MCMC (PyMC 5.17.0, numpyro sampler, 8,000 draws per model). The files can be read with any NetCDF/HDF5 reader, for example ArviZ/xarray in Python.

The dataset contains:

One model for 30-day mortality, defined as death within 30 days of ED arrival, and one base model with sex and age only.
930 diagnosis models, one per ICD-10 chapter, block or three-character code. Each gives the probability of that diagnosis being registered [define: time window and care setting] after the ED visit.
For each model, a CSV file listing the column order (the SNOMED CT complaint codes it includes).
A README with step-by-step instructions, in pseudocode, for making a prediction from sex, age and a presenting complaint.

Model inputs are sex, age (adults, 18 and over) and one presenting complaint coded in SNOMED CT. Age is modelled with a natural cubic spline, and sex, age and complaint interact. Presenting complaints use SNOMED CT concept identifiers. The SNOMED CT terms themselves are not included.

The material can be reused to compute risk estimates for new patients, to compare with other models or cohorts, or to study how complaint, age and sex relate to outcomes.</r:Content>
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