Updated md card and annotators (#844)

docs/_posts/Ahmetemintek/2022-03-31-drug_action_treatment_mapper_en_3_0.md

@@ -21,8 +21,6 @@ use_language_switcher: "Python-Scala-Java"
 
 This pretrained model maps drugs with their corresponding `action` and `treatment`. `action` refers to the function of the drug in various body systems, `treatment` refers to which disease the drug is used to treat
 
-`Important Note`: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains 'Mapper', it is necessary to use the ChunkMapperModel after an EntityResolverModel.
-
 ## Predicted Entities
 
 `action`, `treatment`
@@ -39,6 +37,7 @@ This pretrained model maps drugs with their corresponding `action` and `treatmen
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
 .setInputCol('text')\

docs/_posts/Ahmetemintek/2022-04-04-drug_action_treatment_mapper_en_3_0.md

@@ -21,8 +21,6 @@ use_language_switcher: "Python-Scala-Java"
 
 This pretrained model maps drugs with their corresponding `action` and `treatment`. `action` refers to the function of the drug in various body systems, `treatment` refers to which disease the drug is used to treat.
 
-`Important Note`: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains 'Mapper', it is necessary to use the ChunkMapperModel after an EntityResolverModel.
-
 ## Predicted Entities
 
 `action`, `treatment`

docs/_posts/Ahmetemintek/2022-04-04-normalized_section_header_mapper_en_3_0.md

@@ -46,39 +46,32 @@ document_assembler = DocumentAssembler()\
 .setInputCol('text')\
 .setOutputCol('document')
 
-
 sentence_detector = SentenceDetector()\
 .setInputCols(["document"])\
 .setOutputCol("sentence")
 
-
 tokenizer = Tokenizer()\
 .setInputCols("sentence")\
 .setOutputCol("token")
 
-
 embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en","clinical/models")\
 .setInputCols(["sentence", "token"])\
 .setOutputCol("word_embeddings")
 
-
 clinical_ner = MedicalNerModel.pretrained("ner_jsl_slim", "en", "clinical/models")\
 .setInputCols(["sentence","token", "word_embeddings"])\
 .setOutputCol("ner")
 
-
 ner_converter = NerConverter()\
 .setInputCols(["sentence", "token", "ner"])\
 .setOutputCol("ner_chunk")\
 .setWhiteList(["Header"])
 
-
 chunkerMapper = ChunkMapperModel.pretrained("normalized_section_header_mapper", "en", "clinical/models") \
 .setInputCols("ner_chunk")\
 .setOutputCol("mappings")\
 .setRel("level_1") #or level_2
 
-
 pipeline = Pipeline().setStages([document_assembler,
 sentence_detector,
 tokenizer, 

docs/_posts/Ahmetemintek/2022-06-07-rxnorm_mapper_en_3_0.md

@@ -21,8 +21,6 @@ use_language_switcher: "Python-Scala-Java"
 
 This pretrained model maps entities with their corresponding RxNorm codes.
 
-`Important Note`: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains 'Mapper', it is necessary to use the ChunkMapperModel after an EntityResolverModel.
-
 ## Predicted Entities
 
 `rxnorm_code`
@@ -39,6 +37,7 @@ This pretrained model maps entities with their corresponding RxNorm codes.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
 .setInputCol('text')\

docs/_posts/Ahmetemintek/2022-06-24-rxnorm_umls_mapper_en_3_0.md

@@ -39,6 +39,7 @@ This pretrained model maps RxNorm codes with corresponding UMLS Codes.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 documentAssembler = DocumentAssembler()\
 .setInputCol("text")\

docs/_posts/Ahmetemintek/2022-07-06-umls_clinical_drugs_mapper_en_3_0.md

@@ -21,8 +21,6 @@ use_language_switcher: "Python-Scala-Java"
 
 This pretrained model maps entities (Clinical Drugs) with their corresponding UMLS CUI codes.
 
-`Important Note`: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains 'Mapper', it is necessary to use the ChunkMapperModel after an EntityResolverModel.
-
 ## Predicted Entities
 
 `umls_code`
@@ -39,6 +37,7 @@ This pretrained model maps entities (Clinical Drugs) with their corresponding UM
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
       .setInputCol('text')\

docs/_posts/Ahmetemintek/2022-07-08-umls_clinical_findings_mapper_en_3_0.md

@@ -39,6 +39,7 @@ This pretrained model maps clinical entities and concepts to 4 major categories
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
       .setInputCol('text')\

docs/_posts/Ahmetemintek/2022-07-11-umls_disease_syndrome_mapper_en_3_0.md

@@ -39,6 +39,7 @@ This pretrained model maps entities (Disease or Syndrome) with corresponding UML
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
       .setInputCol('text')\

docs/_posts/Ahmetemintek/2022-07-11-umls_drug_substance_mapper_en_3_0.md

@@ -21,8 +21,6 @@ use_language_switcher: "Python-Scala-Java"
 
 This pretrained model maps entities (Drug Substances) with their corresponding UMLS CUI codes.
 
-`Important Note`: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains 'Mapper', it is necessary to use the ChunkMapperModel after an EntityResolverModel.
-
 ## Predicted Entities
 
 `umls_code`
@@ -39,6 +37,7 @@ This pretrained model maps entities (Drug Substances) with their corresponding U
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
       .setInputCol('text')\

docs/_posts/Ahmetemintek/2022-07-11-umls_major_concepts_mapper_en_3_0.md

@@ -40,6 +40,7 @@ This pretrained model maps entities (Major Clinical Concepts) with corresponding
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
       .setInputCol('text')\

docs/_posts/Ahmetemintek/2022-08-23-drug_ade_mapper_en.md

@@ -22,9 +22,6 @@ use_language_switcher: "Python-Scala-Java"
 
 This pretrained model maps drugs with their corresponding Adverse Drug Events.
 
-`Important Note`: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains 'Mapper', it is necessary to use the ChunkMapperModel after an EntityResolverModel.
-
-
 ## Predicted Entities
 
 `ADE`
@@ -41,6 +38,7 @@ This pretrained model maps drugs with their corresponding Adverse Drug Events.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
       .setInputCol('text')\

docs/_posts/Ahmetemintek/2022-09-29-rxnorm_normalized_mapper_en.md

@@ -21,9 +21,6 @@ use_language_switcher: "Python-Scala-Java"
 
 This pretrained pipeline maps entities with their corresponding RxNorm codes and normalized RxNorm resolutions.
 
-`Important Note`: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains 'Mapper', it is necessary to use the ChunkMapperModel after an EntityResolverModel.
-
-
 ## Predicted Entities
 
 `rxnorm_code`, `normalized_name`
@@ -40,6 +37,7 @@ This pretrained pipeline maps entities with their corresponding RxNorm codes and
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
         .setInputCol('text')\

docs/_posts/Ahmetemintek/2022-09-30-icd10_icd9_mapper_en.md

@@ -21,9 +21,6 @@ use_language_switcher: "Python-Scala-Java"
 
 This pretrained model maps ICD-10-CM codes to corresponding ICD-9-CM codes.
 
-`Important Note`: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains 'Mapper', it is necessary to use the ChunkMapperModel after an EntityResolverModel.
-
-
 ## Predicted Entities
 
 `icd9_code`
@@ -36,10 +33,9 @@ This pretrained model maps ICD-10-CM codes to corresponding ICD-9-CM codes.
 
 ## How to use
 
-
-
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 documentAssembler = DocumentAssembler()\
     .setInputCol("text")\

docs/_posts/Ahmetemintek/2022-09-30-icd9_icd10_mapper_en.md

@@ -40,6 +40,7 @@ This pretrained model maps ICD-9-CM codes to corresponding ICD-10-CM codes.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
       .setInputCol("text")\

docs/_posts/Ahmetemintek/2022-09-30-icd9_mapper_en.md

@@ -39,6 +39,7 @@ This pretrained model maps entities with their corresponding ICD-9-CM codes.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
       .setInputCol('text')\

docs/_posts/Ahmetemintek/2022-10-12-cvx_code_mapper_en.md

@@ -40,6 +40,7 @@ This pretrained model maps CVX codes with their corresponding vaccine names and
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
       .setInputCol('text')\

docs/_posts/Ahmetemintek/2022-10-12-cvx_name_mapper_en.md

@@ -40,6 +40,7 @@ This pretrained model maps vaccine products with their corresponding CVX codes,
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
       .setInputCol('text')\

docs/_posts/Ahmetemintek/2022-10-29-icd10cm_mapper_en.md

@@ -40,6 +40,7 @@ This pretrained model maps entities with their corresponding ICD-10-CM codes.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
     .setInputCol('text')\

docs/_posts/Ahmetemintek/2022-10-30-abbreviation_mapper_augmented_en.md

@@ -40,6 +40,7 @@ This pretrained model maps abbreviations and acronyms of medical regulatory acti
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
     .setInputCol("text")\

docs/_posts/Ahmetemintek/2022-11-16-abbreviation_category_mapper_en.md

@@ -40,6 +40,7 @@ This pretrained model maps abbreviations and acronyms of medical regulatory acti
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
     .setInputCol("text")\

docs/_posts/Ahmetemintek/2022-11-18-kegg_disease_mapper_en.md

@@ -40,6 +40,7 @@ This pretrained model maps diseases with their corresponding `category`, `descri
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
     .setInputCol("text")\

docs/_posts/Ahmetemintek/2022-11-21-kegg_drug_mapper_en.md

@@ -21,8 +21,6 @@ use_language_switcher: "Python-Scala-Java"
 
 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.
 
-`Important Note`: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains 'Mapper', it is necessary to use the ChunkMapperModel after an EntityResolverModel.
-
 
 ## Predicted Entities
 
@@ -40,6 +38,7 @@ This pretrained model maps drugs with their corresponding `efficacy`, `molecular
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
     .setInputCol("text")\

docs/_posts/Ahmetemintek/2023-02-09-rxnorm_drug_brandname_mapper_en.md

@@ -21,7 +21,6 @@ use_language_switcher: "Python-Scala-Java"
 
 This pretrained model maps RxNorm and RxNorm Extension codes with their corresponding drug brand names. It returns 2 types of brand names for the corresponding RxNorm or RxNorm Extension code.
 
-`Important Note`: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains 'Mapper', it is necessary to use the ChunkMapperModel after an EntityResolverModel.
 
 
 ## Predicted Entities

docs/_posts/Ahmetemintek/2023-02-23-rxnorm_nih_mapper_en.md

@@ -21,8 +21,6 @@ use_language_switcher: "Python-Scala-Java"
 
 This pretrained model maps entities with their corresponding RxNorm codes according to the National Institute of Health (NIH) database. It returns Rxnorm codes with their NIH Rxnorm Term Types within a parenthesis.
 
-`Important Note`: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains 'Mapper', it is necessary to use the ChunkMapperModel after an EntityResolverModel.
-
 
 ## Predicted Entities
 
@@ -40,6 +38,7 @@ This pretrained model maps entities with their corresponding RxNorm codes accord
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
 .setInputCol('text')\

docs/_posts/Ahmetemintek/2023-04-13-hcpcs_ndc_mapper_en.md

@@ -39,6 +39,7 @@ This pretrained model maps HCPCS codes with their corresponding National Drug Co
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
       .setInputCol("text")\

docs/_posts/Ahmetemintek/2023-04-13-ndc_hcpcs_mapper_en.md

@@ -39,6 +39,7 @@ This pretrained model maps National Drug Codes (NDC) with their corresponding HC
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
       .setInputCol("text")\

docs/_posts/Ahmetemintek/2023-08-08-icd10cm_ms_drg_mapper_en.md

@@ -39,6 +39,7 @@ This pretrained model maps ICD-10-CM codes with their corresponding Medicare Sev
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
       .setInputCol("text")\

docs/_posts/Damla-Gurbaz/2022-06-26-drug_brandname_ndc_mapper_en_3_0.md

@@ -21,9 +21,6 @@ use_language_switcher: "Python-Scala-Java"
 
 This pretrained model maps drug brand names to corresponding National Drug Codes (NDC). Product NDCs for each strength are returned in result and metadata.
 
-`Important Note`: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains 'Mapper', it is necessary to use the ChunkMapperModel after an EntityResolverModel.
-
-
 ## Predicted Entities
 
 `Strength_NDC`
@@ -36,8 +33,6 @@ This pretrained model maps drug brand names to corresponding National Drug Codes
 
 ## How to use
 
-
-
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
 

docs/_posts/Damla-Gurbaz/2022-06-26-icd10cm_snomed_mapper_en_3_0.md

@@ -21,9 +21,6 @@ use_language_switcher: "Python-Scala-Java"
 
 This pretrained model maps ICD10-CM codes to corresponding SNOMED codes under the Unified Medical Language System (UMLS).
 
-`Important Note`: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains 'Mapper', it is necessary to use the ChunkMapperModel after an EntityResolverModel.
-
-
 ## Predicted Entities
 
 `snomed_code`

docs/_posts/Damla-Gurbaz/2022-06-26-icd10cm_umls_mapper_en_3_0.md

@@ -21,9 +21,6 @@ use_language_switcher: "Python-Scala-Java"
 
 This pretrained model maps ICD10CM codes to corresponding UMLS codes under the Unified Medical Language System (UMLS).
 
-`Important Note`: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains 'Mapper', it is necessary to use the ChunkMapperModel after an EntityResolverModel.
-
-
 ## Predicted Entities
 
 `umls_code`

docs/_posts/Damla-Gurbaz/2022-06-26-icdo_snomed_mapper_en_3_0.md

@@ -21,8 +21,6 @@ use_language_switcher: "Python-Scala-Java"
 
 This pretrained model maps ICDO codes to corresponding SNOMED codes.
 
-`Important Note`: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains 'Mapper', it is necessary to use the ChunkMapperModel after an EntityResolverModel.
-
 
 ## Predicted Entities
 

docs/_posts/Damla-Gurbaz/2022-06-26-mesh_umls_mapper_en_3_0.md

@@ -21,8 +21,6 @@ use_language_switcher: "Python-Scala-Java"
 
 This pretrained model maps MESH codes to corresponding UMLS codes under the Unified Medical Language System (UMLS).
 
-`Important Note`: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains 'Mapper', it is necessary to use the ChunkMapperModel after an EntityResolverModel.
-
 ## Predicted Entities
 
 `umls_code`