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2022-11-18-kegg_disease_mapper_en (#13113)
* Add model 2022-11-18-kegg_disease_mapper_en * Add model 2022-11-21-kegg_drug_mapper_en Co-authored-by: Ahmetemintek <ahmetemin.tek.66@gmail.com>
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docs/_posts/Ahmetemintek/2022-11-18-kegg_disease_mapper_en.md
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| --- | ||
| layout: model | ||
| title: Mapping Diseases from the KEGG Database to Their Corresponding Categories, Descriptions and Clinical Vocabularies | ||
| author: John Snow Labs | ||
| name: kegg_disease_mapper | ||
| date: 2022-11-18 | ||
| tags: [disease, category, description, icd10, icd11, mesh, brite, en, clinical, chunk_mapper, licensed] | ||
| task: Chunk Mapping | ||
| language: en | ||
| edition: Healthcare NLP 4.2.2 | ||
| spark_version: 3.0 | ||
| supported: true | ||
| article_header: | ||
| type: cover | ||
| use_language_switcher: "Python-Scala-Java" | ||
| --- | ||
|
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| ## Description | ||
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| This pretrained model maps diseases with their corresponding `category`, `description`, `icd10_code`, `icd11_code`, `mesh_code`, and hierarchical `brite_code`. This model was trained with the data from the KEGG database. | ||
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| ## Predicted Entities | ||
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| `category`, `description`, `icd10_code`, `icd11_code`, `mesh_code`, `brite_code` | ||
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| {:.btn-box} | ||
| <button class="button button-orange" disabled>Live Demo</button> | ||
| [Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/26.Chunk_Mapping.ipynb){:.button.button-orange.button-orange-trans.co.button-icon} | ||
| [Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/kegg_disease_mapper_en_4.2.2_3.0_1668794743905.zip){:.button.button-orange.button-orange-trans.arr.button-icon} | ||
|
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| ## How to use | ||
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| <div class="tabs-box" markdown="1"> | ||
| {% include programmingLanguageSelectScalaPythonNLU.html %} | ||
| ```python | ||
| document_assembler = DocumentAssembler()\ | ||
| .setInputCol("text")\ | ||
| .setOutputCol("document") | ||
|
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| sentence_detector = SentenceDetector()\ | ||
| .setInputCols(["document"])\ | ||
| .setOutputCol("sentence") | ||
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| tokenizer = Tokenizer()\ | ||
| .setInputCols("sentence")\ | ||
| .setOutputCol("token") | ||
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| word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\ | ||
| .setInputCols(["sentence", "token"])\ | ||
| .setOutputCol("embeddings") | ||
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| ner = MedicalNerModel.pretrained("ner_diseases", "en", "clinical/models") \ | ||
| .setInputCols(["sentence", "token", "embeddings"]) \ | ||
| .setOutputCol("ner") | ||
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| converter = NerConverter() \ | ||
| .setInputCols(["sentence", "token", "ner"]) \ | ||
| .setOutputCol("ner_chunk")\ | ||
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| chunkerMapper = ChunkMapperModel.pretrained("kegg_disease_mapper", "en", "clinical/models")\ | ||
| .setInputCols(["ner_chunk"])\ | ||
| .setOutputCol("mappings")\ | ||
| .setRels(["description", "category", "icd10_code", "icd11_code", "mesh_code", "brite_code"])\ | ||
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| pipeline = Pipeline().setStages([ | ||
| document_assembler, | ||
| sentence_detector, | ||
| tokenizer, | ||
| word_embeddings, | ||
| ner, | ||
| converter, | ||
| chunkerMapper]) | ||
|
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||
|
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| text= "A 55-year-old female with a history of myopia, kniest dysplasia and prostate cancer. She was on glipizide , and dapagliflozin for congenital nephrogenic diabetes insipidus." | ||
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| data = spark.createDataFrame([[text]]).toDF("text") | ||
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| result = pipeline.fit(data).transform(data) | ||
| ``` | ||
| ```scala | ||
| val document_assembler = new DocumentAssembler() | ||
| .setInputCol("text") | ||
| .setOutputCol("document") | ||
|
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| val sentence_detector = new SentenceDetector() | ||
| .setInputCols(Array("document")) | ||
| .setOutputCol("sentence") | ||
|
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| val tokenizer = new Tokenizer() | ||
| .setInputCols("sentence") | ||
| .setOutputCol("token") | ||
|
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| val word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models") | ||
| .setInputCols(Array("sentence", "token")) | ||
| .setOutputCol("embeddings") | ||
|
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| val ner = MedicalNerModel.pretrained("ner_diseases", "en", "clinical/models") | ||
| .setInputCols(Array("sentence", "token", "embeddings")) | ||
| .setOutputCol("ner") | ||
|
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| val converter = new NerConverter() | ||
| .setInputCols(Array("sentence", "token", "ner")) | ||
| .setOutputCol("ner_chunk") | ||
|
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| val chunkerMapper = ChunkMapperModel.pretrained("kegg_disease_mapper", "en", "clinical/models") | ||
| .setInputCols("ner_chunk") | ||
| .setOutputCol("mappings") | ||
| .setRels(Array("description", "category", "icd10_code", "icd11_code", "mesh_code", "brite_code")) | ||
|
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|
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| val pipeline = new Pipeline().setStages(Array( | ||
| document_assembler, | ||
| sentence_detector, | ||
| tokenizer, | ||
| word_embeddings, | ||
| ner, | ||
| converter, | ||
| chunkerMapper)) | ||
|
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|
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| val text= "A 55-year-old female with a history of myopia, kniest dysplasia and prostate cancer. She was on glipizide , and dapagliflozin for congenital nephrogenic diabetes insipidus." | ||
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| val data = Seq(text).toDS.toDF("text") | ||
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| val result= pipeline.fit(data).transform(data) | ||
| ``` | ||
| </div> | ||
|
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| ## Results | ||
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| ```bash | ||
| +-----------------------------------------+--------------------------------------------------+-----------------------+----------+----------+---------+-----------------------+ | ||
| | ner_chunk| description| category|icd10_code|icd11_code|mesh_code| brite_code| | ||
| +-----------------------------------------+--------------------------------------------------+-----------------------+----------+----------+---------+-----------------------+ | ||
| | myopia|Myopia is the most common ocular disorder world...| Nervous system disease| H52.1| 9D00.0| D009216| 08402,08403| | ||
| | kniest dysplasia|Kniest dysplasia is an autosomal dominant chond...|Congenital malformation| Q77.7| LD24.3| C537207| 08402,08403| | ||
| | prostate cancer|Prostate cancer constitutes a major health prob...| Cancer| C61| 2C82| NONE|08402,08403,08442,08441| | ||
| |congenital nephrogenic diabetes insipidus|Nephrogenic diabetes insipidus (NDI) is charact...| Urinary system disease| N25.1| GB90.4A| D018500| 08402,08403| | ||
| +-----------------------------------------+--------------------------------------------------+-----------------------+----------+----------+---------+-----------------------+ | ||
| ``` | ||
|
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| {:.model-param} | ||
| ## Model Information | ||
|
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| {:.table-model} | ||
| |---|---| | ||
| |Model Name:|kegg_disease_mapper| | ||
| |Compatibility:|Healthcare NLP 4.2.2+| | ||
| |License:|Licensed| | ||
| |Edition:|Official| | ||
| |Input Labels:|[ner_chunk]| | ||
| |Output Labels:|[mappings]| | ||
| |Language:|en| | ||
| |Size:|595.6 KB| |
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docs/_posts/Ahmetemintek/2022-11-21-kegg_drug_mapper_en.md
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,159 @@ | ||
| --- | ||
| layout: model | ||
| title: Mapping Drugs from the KEGG Database to Their Efficacies, Molecular Weights and Corresponding Codes from Other Databases | ||
| author: John Snow Labs | ||
| name: kegg_drug_mapper | ||
| date: 2022-11-21 | ||
| tags: [drug, efficacy, molecular_weight, cas, pubchem, chebi, ligandbox, nikkaji, pdbcct, chunk_mapper, clinical, en, licensed] | ||
| task: Chunk Mapping | ||
| language: en | ||
| edition: Healthcare NLP 4.2.2 | ||
| spark_version: 3.0 | ||
| supported: true | ||
| article_header: | ||
| type: cover | ||
| use_language_switcher: "Python-Scala-Java" | ||
| --- | ||
|
|
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| ## Description | ||
|
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| This pretrained model maps drugs with their corresponding `efficacy`, `molecular_weight` as well as `CAS`, `PubChem`, `ChEBI`, `LigandBox`, `NIKKAJI`, `PDB-CCD` codes. This model was trained with the data from the KEGG database. | ||
|
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| ## Predicted Entities | ||
|
|
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| `efficacy`, `molecular_weight`, `CAS`, `PubChem`, `ChEBI`, `LigandBox`, `NIKKAJI`, `PDB-CCD` | ||
|
|
||
| {:.btn-box} | ||
| <button class="button button-orange" disabled>Live Demo</button> | ||
| [Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/26.Chunk_Mapping.ipynb){:.button.button-orange.button-orange-trans.co.button-icon} | ||
| [Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/kegg_drug_mapper_en_4.2.2_3.0_1669069910375.zip){:.button.button-orange.button-orange-trans.arr.button-icon} | ||
|
|
||
| ## How to use | ||
|
|
||
|
|
||
|
|
||
| <div class="tabs-box" markdown="1"> | ||
| {% include programmingLanguageSelectScalaPythonNLU.html %} | ||
| ```python | ||
| document_assembler = DocumentAssembler()\ | ||
| .setInputCol("text")\ | ||
| .setOutputCol("document") | ||
|
|
||
| sentence_detector = SentenceDetector()\ | ||
| .setInputCols(["document"])\ | ||
| .setOutputCol("sentence") | ||
|
|
||
| tokenizer = Tokenizer()\ | ||
| .setInputCols("sentence")\ | ||
| .setOutputCol("token") | ||
|
|
||
| word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\ | ||
| .setInputCols(["sentence", "token"])\ | ||
| .setOutputCol("embeddings") | ||
|
|
||
| ner = MedicalNerModel.pretrained("ner_posology", "en", "clinical/models") \ | ||
| .setInputCols(["sentence", "token", "embeddings"]) \ | ||
| .setOutputCol("ner") | ||
|
|
||
| converter = NerConverter() \ | ||
| .setInputCols(["sentence", "token", "ner"]) \ | ||
| .setOutputCol("ner_chunk")\ | ||
|
|
||
| chunkerMapper = ChunkMapperModel.pretrained("kegg_drug_mapper", "en", "clinical/models")\ | ||
| .setInputCols(["ner_chunk"])\ | ||
| .setOutputCol("mappings")\ | ||
| .setRels(["efficacy", "molecular_weight", "CAS", "PubChem", "ChEBI", "LigandBox", "NIKKAJI", "PDB-CCD"])\ | ||
|
|
||
| pipeline = Pipeline().setStages([ | ||
| document_assembler, | ||
| sentence_detector, | ||
| tokenizer, | ||
| word_embeddings, | ||
| ner, | ||
| converter, | ||
| chunkerMapper]) | ||
|
|
||
|
|
||
| text= "She is given OxyContin, folic acid, levothyroxine, Norvasc, aspirin, Neurontin" | ||
|
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| data = spark.createDataFrame([[text]]).toDF("text") | ||
|
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| result = pipeline.fit(data).transform(data) | ||
| ``` | ||
| ```scala | ||
| val document_assembler = new DocumentAssembler() | ||
| .setInputCol("text") | ||
| .setOutputCol("document") | ||
|
|
||
| val sentence_detector = new SentenceDetector() | ||
| .setInputCols(Array("document")) | ||
| .setOutputCol("sentence") | ||
|
|
||
| val tokenizer = new Tokenizer() | ||
| .setInputCols("sentence") | ||
| .setOutputCol("token") | ||
|
|
||
| val word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models") | ||
| .setInputCols(Array("sentence", "token")) | ||
| .setOutputCol("embeddings") | ||
|
|
||
| val ner = MedicalNerModel.pretrained("ner_posology", "en", "clinical/models") | ||
| .setInputCols(Array("sentence", "token", "embeddings")) | ||
| .setOutputCol("ner") | ||
|
|
||
| val converter = new NerConverter() | ||
| .setInputCols(Array("sentence", "token", "ner")) | ||
| .setOutputCol("ner_chunk") | ||
|
|
||
| val chunkerMapper = ChunkMapperModel.pretrained("kegg_drug_mapper", "en", "clinical/models") | ||
| .setInputCols("ner_chunk") | ||
| .setOutputCol("mappings") | ||
| .setRels(Array(["efficacy", "molecular_weight", "CAS", "PubChem", "ChEBI", "LigandBox", "NIKKAJI", "PDB-CCD"])) | ||
|
|
||
|
|
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| val pipeline = new Pipeline().setStages(Array( | ||
| document_assembler, | ||
| sentence_detector, | ||
| tokenizer, | ||
| word_embeddings, | ||
| ner, | ||
| converter, | ||
| chunkerMapper)) | ||
|
|
||
|
|
||
| val text= "She is given OxyContin, folic acid, levothyroxine, Norvasc, aspirin, Neurontin" | ||
|
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| val data = Seq(text).toDS.toDF("text") | ||
|
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| val result= pipeline.fit(data).transform(data) | ||
| ``` | ||
| </div> | ||
|
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| ## Results | ||
|
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| ```bash | ||
| +-------------+--------------------------------------------------+----------------+----------+-----------+-------+---------+---------+-------+ | ||
| | ner_chunk| efficacy|molecular_weight| CAS| PubChem| ChEBI|LigandBox| NIKKAJI|PDB-CCD| | ||
| +-------------+--------------------------------------------------+----------------+----------+-----------+-------+---------+---------+-------+ | ||
| | OxyContin| Analgesic (narcotic), Opioid receptor agonist| 351.8246| 124-90-3| 7847912.0| 7859.0| D00847|J281.239H| NONE| | ||
| | folic acid|Anti-anemic, Hematopoietic, Supplement (folic a...| 441.3975| 59-30-3| 7847138.0|27470.0| D00070| J1.392G| FOL| | ||
| |levothyroxine| Replenisher (thyroid hormone)| 776.87| 51-48-9|9.6024815E7|18332.0| D08125| J4.118A| T44| | ||
| | Norvasc|Antihypertensive, Vasodilator, Calcium channel ...| 408.8759|88150-42-9|5.1091781E7| 2668.0| D07450| J33.383B| NONE| | ||
| | aspirin|Analgesic, Anti-inflammatory, Antipyretic, Anti...| 180.1574| 50-78-2| 7847177.0|15365.0| D00109| J2.300K| AIN| | ||
| | Neurontin| Anticonvulsant, Antiepileptic| 171.2368|60142-96-3| 7847398.0|42797.0| D00332| J39.388F| GBN| | ||
| +-------------+--------------------------------------------------+----------------+----------+-----------+-------+---------+---------+-------+ | ||
| ``` | ||
| {:.model-param} | ||
| ## Model Information | ||
| {:.table-model} | ||
| |---|---| | ||
| |Model Name:|kegg_drug_mapper| | ||
| |Compatibility:|Healthcare NLP 4.2.2+| | ||
| |License:|Licensed| | ||
| |Edition:|Official| | ||
| |Input Labels:|[ner_chunk]| | ||
| |Output Labels:|[mappings]| | ||
| |Language:|en| | ||
| |Size:|1.0 MB| |