Description
This model maps HPO codes to the real disease(s) they are associated with, based on the Human Phenotype Ontology’s own phenotype-disease annotations (OMIM, Orphanet, and DECIPHER identifiers). Two positionally-aligned relations are available: disease_id and disease_name. Trained on the Human Phenotype Ontology (HPO) 2026-06-23 release.
Live Demo Open in Colab Copy S3 URI
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
chunk_assembler = Doc2Chunk()\
.setInputCols(["document"])\
.setOutputCol("hpo_code")
hpo_disease_mapper = ChunkMapperModel.pretrained("hpo_disease_mapper", "en", "clinical/models")\
.setInputCols(["hpo_code"])\
.setOutputCol("mappings")\
.setRels(["disease_id"]) # or disease_name
pipeline = Pipeline(stages=[document_assembler, chunk_assembler, hpo_disease_mapper])
data = spark.createDataFrame([["HP:0000025"], ["HP:0000058"], ["HP:0000002"]]).toDF("text")
result = pipeline.fit(data).transform(data)
document_assembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
chunk_assembler = nlp.Doc2Chunk()\
.setInputCols(["document"])\
.setOutputCol("hpo_code")
hpo_disease_mapper = medical.ChunkMapperModel.pretrained("hpo_disease_mapper", "en", "clinical/models")\
.setInputCols(["hpo_code"])\
.setOutputCol("mappings")\
.setRels(["disease_id"]) # or disease_name
pipeline = nlp.Pipeline(stages=[document_assembler, chunk_assembler, hpo_disease_mapper])
data = spark.createDataFrame([["HP:0000025"], ["HP:0000058"], ["HP:0000002"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val chunkAssembler = new Doc2Chunk()
.setInputCols("document")
.setOutputCol("hpo_code")
val hpoDiseaseMapper = ChunkMapperModel
.pretrained("hpo_disease_mapper", "en", "clinical/models")
.setInputCols(Array("hpo_code"))
.setOutputCol("mappings")
.setRels(Array("disease_id")) // or disease_name
val pipeline = new Pipeline().setStages(Array(documentAssembler, chunkAssembler, hpoDiseaseMapper))
val data = Seq("HP:0000025", "HP:0000058", "HP:0000002").toDF("text")
val result = pipeline.fit(data).transform(data)
Results
| hpo_code | n_diseases | disease_id | all_k_resolutions |
|:-----------|-------------:|:-------------|:------------------------------------------------------------------------------------------------------------------------------------------|
| HP:0000025 | 1 | ORPHA:904 | ORPHA:904::: |
| HP:0000058 | 3 | ORPHA:251510 | ORPHA:251510:::ORPHA:37202:::ORPHA:325345 |
| HP:0000002 | 10 | OMIM:144750 | OMIM:144750:::OMIM:186570:::OMIM:617800:::OMIM:621382:::OMIM:621091:::OMIM:612475:::OMIM:616977:::ORPHA:209964:::ORPHA:140976:::ORPHA:432 |
Model Information
| Model Name: | hpo_disease_mapper |
| Compatibility: | Healthcare NLP 6.4.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [ner_chunk] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 4.6 MB |