Add model 2022-11-02-icd10cm_resolver_pipeline_en (#13017)

Co-authored-by: Ahmetemintek <ahmetemin.tek.66@gmail.com>

docs/_posts/Ahmetemintek/2022-11-02-icd10cm_resolver_pipeline_en.md

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+---
+layout: model
+title: Pipeline to Resolve ICD-10-CM Codes
+author: John Snow Labs
+name: icd10cm_resolver_pipeline
+date: 2022-11-02
+tags: [en, clinical, licensed, resolver, chunk_mapping, pipeline, icd10cm]
+task: Pipeline Healthcare
+language: en
+edition: Spark NLP for Healthcare 4.2.1
+spark_version: 3.0
+supported: true
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline maps entities with their corresponding ICD-10-CM codes. You’ll just feed your text and it will return the corresponding ICD-10-CM codes.
+
+{:.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/3.Clinical_Entity_Resolvers.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/icd10cm_resolver_pipeline_en_4.2.1_3.0_1667389014041.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+resolver_pipeline = PretrainedPipeline("icd10cm_resolver_pipeline", "en", "clinical/models")
+
+text = """A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage"""
+
+result = resolver_pipeline.fullAnnotate(text)
+```
+```scala
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val resolver_pipeline = new PretrainedPipeline("icd10cm_resolver_pipeline", "en", "clinical/models")
+
+val result = resolver_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage""")
+```
+</div>
+
+## Results
+
+```bash
++-----------------------------+---------+------------+
+|chunk                        |ner_chunk|icd10cm_code|
++-----------------------------+---------+------------+
+|gestational diabetes mellitus|PROBLEM  |O24.919     |
+|anisakiasis                  |PROBLEM  |B81.0       |
+|fetal and neonatal hemorrhage|PROBLEM  |P545        |
++-----------------------------+---------+------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|icd10cm_resolver_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP for Healthcare 4.2.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|3.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverter
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperFilterer
+- Chunk2Doc
+- BertSentenceEmbeddings
+- SentenceEntityResolverModel
+- ResolverMerger