Healthcare Nlp For Data Scientists

Unlock your NLP power with Healthcare NLP, the most popular NLP library in the healthcare industry

What you'll learn

Utilize 20,000+ State-of-the-Art NLP models that specalizes in solving healthcare problems in 200+ languages

Train & tune your own NLP models by leveraging the Spark NLP's pre-defined classifier architecture on your own datasets

Perform popular NLP tasks like clinical entity recognition, entity resolution (mapping entities to medical codes), assertion status detection

Deploy models as API's with NLP Server, a Docker container that contains all Spark NLPs capabilities

Requirements

Hands-on understanding of Python is needed

Recommended: basic understanding of machine learning and natural language processing

Recommended: take the Spark NLP for Data Scientists course

Nice to have: basic understanding of Apache Spark

Description

Hello everyone, welcome to the Healthcare NLP for Data Scientists course, offered by John Snow Labs, the creator of Healthcare NLP library! In this course, you will explore the extensive functionalities of John Snow Labs’ Healthcare NLP & LLM library,  designed to provide practical skills and industry insights for data scientists professionals in healthcare.The course covers foundational NLP techniques, including clinical entity recognition, entity resolution, assertion status detection (negation detection), relation extraction, de-identification, text summarization, keyword extraction, and text classification. There are over 13 hours of lectures with 70+ Python notebooks for you to review and use. You'll learn to leverage pre-trained models and train new models for your specific healthcare challenges.We offer both hands-on coding notebooks with lectures and accompanying blog posts for you to review and apply. By the end of the program, you'll emerge equipped with the skills and insights needed to excel in the dynamic landscape of healthcare NLP and LLM.We recommend that you take the Spark NLP for Data Scientist first to have an understading of our library and platform, that you have working experience using Python, some knowledge on Spark dataframe structure, and knowledge on NLP to make the most out of the course. Of course having some healthcare experience is always a plus.You will need a Healthcare NLP trial license for the course, so please reach out and get one to get started with learning. Looking forward to seeing you in the course.

Overview

Section 1: Introduction

Lecture 1 Healthcare NLP for Data Scientists course overview

Lecture 2 Course Structure

Lecture 3 How to obtain a Healthcare NLP license for the course

Section 2: Text Embeddings

Lecture 4 AverageEmbeddings

Lecture 5 BertSentenceChunkEmbeddings

Lecture 6 ChunkSentenceSplitter

Lecture 7 EntityChunkEmbeddings

Section 3: Text Processing

Lecture 8 AnnotationMerger

Lecture 9 Replacer

Lecture 10 Chunk2Token

Lecture 11 ChunkKeyPhraseExtraction

Lecture 12 DateNormalizer

Lecture 13 DrugNormalizer

Lecture 14 IOBTagger

Lecture 15 NerDisambiguator

Lecture 16 NerChunker

Lecture 17 Flattener

Lecture 18 NerQuestionGenerator

Lecture 19 InternalDocumentSplitter

Section 4: NER

Lecture 20 RegexMatcher

Lecture 21 NerConverter

Lecture 22 Ner Model Inference

Lecture 23 NerModel

Lecture 24 BertForTokenClassifier

Lecture 25 ChunkFilterer

Lecture 26 ChunkFilterer Model Inference

Lecture 27 ChunkMerge Model Inference

Lecture 28 ChunkMergeModel

Lecture 29 ChunkConverter

Lecture 30 ContextualParserModel

Lecture 31 ZeroShotNerModel

Lecture 32 ContextualParser Model Inference

Lecture 33 EntityRuler

Lecture 34 TextMatcher

Section 5: Assertion Status Detection

Lecture 35 AssertionChunkConverter

Lecture 36 AssertionFilterer

Lecture 37 AssertionDLModel

Lecture 38 AssertionLogReg Model Inference

Lecture 39 AssertionLogRegModel

Lecture 40 AssertionDL Model Inference

Section 6: Relation Extraction

Lecture 41 RelationExtractionModel

Lecture 42 RelationExtractionDLModel

Lecture 43 RelationExtraction Model Inference Pt1

Lecture 44 RelationExtraction Model Inference Pt2

Lecture 45 RENerChunksFilter

Lecture 46 ZeroShotRelationExtractionModel

Section 7: Text Classification

Lecture 47 FeaturesAssembler

Lecture 48 DistilBertForSequenceClassification

Lecture 49 BertForSequenceClassification

Lecture 50 GenericClassifier Model Inference

Lecture 51 GenericSVMClassifierModel

Lecture 52 GenericLogRegClassifier Model Inference

Lecture 53 GenericClassifierModel

Lecture 54 GenericSVMClassifier Model Inference

Lecture 55 DocumentMLClassifier Model Inference

Lecture 56 DocumentMLClassifierModel

Lecture 57 FewShotClassifier

Lecture 58 WindowedSentenceModel

Lecture 59 DocumentLogRegClassifier

Lecture 60 DocumentFiltererByClassifier

Section 8: Entity Resolution for Medical Terminologies

Lecture 61 Resolution2Chunk

Lecture 62 ChunkMapperModel

Lecture 63 DocMapperModel

Lecture 64 DocMapper Model Inference

Lecture 65 ChunkMapper Model Inference Pt1

Lecture 66 ChunkMapper Model Inference Pt2

Lecture 67 ChunkMapperFilterer

Lecture 68 Doc2Chunk

Lecture 69 Router

Lecture 70 ResolverMerger

Lecture 71 SentenceEntityResolverModel

Lecture 72 SentenceEntityResolver Model Inference

Section 9: De-identification and Obfuscate PHI Data

Lecture 73 ReIdentification

Lecture 74 NameChunkObfuscator Model Inference

Lecture 75 NameChunkObfuscator

Lecture 76 DocumentHashCoder

Lecture 77 DeIdentification_DeIdentificationModel Pt1

Lecture 78 DeIdentification_DeIdentificationModel Pt2

Section 10: Text Summarization

Lecture 79 Summarizer

Lecture 80 ExtractiveSummarization

Section 11: QuestionAnswering

Lecture 81 QuestionAnswering

Section 12: Text Generation

Lecture 82 TextGenerator

Data scientists who are looking to use Natural Language Processing at scale,Data scientists looking to build custom natural language understanding applications,Data Analysts who want to apply Natural Language Processing,Data scientists who are looking to leverage vast and deep healthcare knowledge in NLP to help achieve business objectives