--- a
+++ b/medacy/pipelines/clinical_pipeline.py
@@ -0,0 +1,41 @@
+import spacy
+
+from medacy.pipeline_components.feature_extractors.discrete_feature_extractor import FeatureExtractor
+from medacy.pipeline_components.feature_overlayers.metamap.metamap_component import MetaMapOverlayer
+from medacy.pipeline_components.learners.crf_learner import get_crf
+from medacy.pipeline_components.tokenizers.clinical_tokenizer import ClinicalTokenizer
+from medacy.pipelines.base.base_pipeline import BasePipeline
+
+
+class ClinicalPipeline(BasePipeline):
+    """
+    A pipeline for clinical named entity recognition. A special tokenizer that breaks down a clinical document
+    to character level tokens defines this pipeline. It was created for the extraction of ADE related entities
+    from the 2018 N2C2 Shared Task.
+
+    Created by Andiy Mulyar (andriymulyar.com) of NLP@VCU
+    """
+
+
+    def __init__(self, entities, metamap=None, **kwargs):
+        """
+        Create a pipeline with the name 'clinical_pipeline' utilizing
+        by default spaCy's small english model.
+
+        :param entities: a list of entities to use in this pipeline.
+        :param metamap: an instance of MetaMap if metamap should be used, defaults to None.
+        """
+
+        super().__init__(entities, spacy_pipeline=spacy.load("en_core_web_sm"), **kwargs)
+
+        if metamap:
+            self.add_component(MetaMapOverlayer, metamap)
+
+    def get_learner(self):
+        return "CRF_l2sgd", get_crf()
+
+    def get_tokenizer(self):
+        return ClinicalTokenizer(self.spacy_pipeline)
+
+    def get_feature_extractor(self):
+        return FeatureExtractor(window_size=3, spacy_features=['pos_', 'shape_', 'prefix_', 'suffix_', 'text'])