Healthcare NLP for Data Scientists

Focused View

Ace Vo

13:40:20

682 View

1. Introduction

1. Healthcare NLP for Data Scientists course overview.mp4

06:18

2. Course Structure.mp4

02:09

3. How to obtain a Healthcare NLP license for the course.html

2. Text Embeddings

1.1 AverageEmbeddings.ipynb.html

1. AverageEmbeddings.mp4

05:48

2.1 BertSentenceChunkEmbeddings.ipynb.html

2. BertSentenceChunkEmbeddings.mp4

11:26

3.1 ChunkSentenceSplitter.ipynb.html

3. ChunkSentenceSplitter.mp4

10:00

4.1 EntityChunkEmbeddings.ipynb.html

4. EntityChunkEmbeddings.mp4

09:59

3. Text Processing

1.1 AnnotationMerger.ipynb.html

1. AnnotationMerger.mp4

06:41

2.1 Replacer.ipynb.html

2. Replacer.mp4

09:24

3.1 Chunk2Token.ipynb.html

3. Chunk2Token.mp4

04:46

4.1 ChunkKeyPhraseExtraction.ipynb.html

4. ChunkKeyPhraseExtraction.mp4

13:10

5.1 DateNormalizer.ipynb.html

5. DateNormalizer.mp4

07:31

6.1 DrugNormalizer.ipynb.html

6. DrugNormalizer.mp4

07:36

7.1 IOBTagger.ipynb.html

7. IOBTagger.mp4

04:52

8.1 NerDisambiguator.ipynb.html

8. NerDisambiguator.mp4

06:48

9.1 NerChunker.ipynb.html

9. NerChunker.mp4

05:18

10. Flattener.mp4

10:14

11.1 NerQuestionGenerator.ipynb.html

11. NerQuestionGenerator.mp4

06:01

12. InternalDocumentSplitter.mp4

12:11

4. NER

1. RegexMatcher.mp4

07:57

2.1 NerConverterInternal.ipynb.html

2. NerConverter.mp4

11:34

3.1 MedicalNerApproach.ipynb.html

3. Ner Model Inference.mp4

22:54

4.1 MedicalNerModel.ipynb.html

4. NerModel.mp4

10:26

5.1 MedicalBertForTokenClassifier.ipynb.html

5. BertForTokenClassifier.mp4

06:16

6.1 ChunkFilterer.ipynb.html

6. ChunkFilterer.mp4

21:06

7.1 ChunkFiltererApproach.ipynb.html

7. ChunkFilterer Model Inference.mp4

15:01

8.1 ChunkMapperApproach.ipynb.html

8. ChunkMerge Model Inference.mp4

19:50

9.1 ChunkMergeModel.ipynb.html

9.2 ChunkMergeModel.ipynb.html

9. ChunkMergeModel.mp4

09:46

10.1 ChunkConverter.ipynb.html

10. ChunkConverter.mp4

05:48

11.1 ContextualParserModel.ipynb.html

11. ContextualParserModel.mp4

06:15

12.1 ZeroShotNerModel.ipynb.html

12. ZeroShotNerModel.mp4

09:58

13.1 ContextualParserModel.ipynb.html

13. ContextualParser Model Inference.mp4

07:56

14. EntityRuler.mp4

06:42

15. TextMatcher.mp4

07:05

5. Assertion Status Detection

1.1 AssertionChunkConverter.ipynb.html

1. AssertionChunkConverter.mp4

03:33

2.1 AssertionFilterer.ipynb.html

2. AssertionFilterer.mp4

08:12

3.1 AssertionDLModel.ipynb.html

3. AssertionDLModel.mp4

17:51

4. AssertionLogReg Model Inference.mp4

05:38

5.1 AssertionLogRegModel.ipynb.html

5. AssertionLogRegModel.mp4

05:43

6.1 AssertionDLApproach.ipynb.html

6. AssertionDL Model Inference.mp4

21:22

6. Relation Extraction

1.1 RelationExtractionModel.ipynb.html

1. RelationExtractionModel.mp4

17:46

2.1 RelationExtractionDLModel.ipynb.html

2. RelationExtractionDLModel.mp4

09:48

3.1 RelationExtractionApproach.ipynb.html

3. RelationExtraction Model Inference Pt1.mp4

15:55

4.1 RelationExtractionApproach.ipynb.html

4. RelationExtraction Model Inference Pt2.mp4

13:32

5.1 RENerChunksFilter.ipynb.html

5. RENerChunksFilter.mp4

13:43

6.1 ZeroShotRelationExtractionModel.ipynb.html

6. ZeroShotRelationExtractionModel.mp4

11:44

7. Text Classification

1.1 FeaturesAssembler.ipynb.html

1. FeaturesAssembler.mp4

07:53

2.1 MedicalDistilBertForSequenceClassification.ipynb.html

2. DistilBertForSequenceClassification.mp4

09:36

3.1 MedicalBertForSequenceClassification.ipynb.html

3. BertForSequenceClassification.mp4

09:12

4.1 GenericClassifierApproach.ipynb.html

4. GenericClassifier Model Inference.mp4

11:49

5.1 GenericSVMClassifierModel.ipynb.html

5. GenericSVMClassifierModel.mp4

07:11

6.1 GenericLogRegClassifierApproach.ipynb.html

6. GenericLogRegClassifier Model Inference.mp4

12:22

7.1 GenericClassifierModel.ipynb.html

7. GenericClassifierModel.mp4

04:58

8.1 GenericSVMClassifierApproach.ipynb.html

8. GenericSVMClassifier Model Inference.mp4

11:58

9.1 DocumentMLClassifierApproach.ipynb.html

9. DocumentMLClassifier Model Inference.mp4

15:46

10.1 DocumentMLClassifierModel.ipynb.html

10. DocumentMLClassifierModel.mp4

05:28

11.1 Notebook.html

11. FewShotClassifier.mp4

05:04

12.1 Notebook.html

12. WindowedSentenceModel.mp4

04:53

13.1 Notebook.html

13. DocumentLogRegClassifier.mp4

03:37

14.1 Notebook.html

14. DocumentFiltererByClassifier.mp4

04:27

8. Entity Resolution for Medical Terminologies

1.1 Resolution2Chunk.ipynb.html

1. Resolution2Chunk.mp4

05:01

2.1 ChunkMapperModel.ipynb.html

2. ChunkMapperModel.mp4

18:37

3.1 DocMapperModel.ipynb.html

3. DocMapperModel.mp4

19:57

4.1 DocMapperApproach.ipynb.html

4. DocMapper Model Inference.mp4

22:12

5.1 ChunkMapperApproach.ipynb.html

5. ChunkMapper Model Inference Pt1.mp4

13:51

6.1 ChunkMapperApproach.ipynb.html

6. ChunkMapper Model Inference Pt2.mp4

14:44

7.1 ChunkMapperFilterer.ipynb.html

7. ChunkMapperFilterer.mp4

08:56

8.1 Doc2ChunkInternal.ipynb.html

8. Doc2Chunk.mp4

05:22

9.1 Router.ipynb.html

9. Router.mp4

08:42

10.1 ResolverMerger.ipynb.html

10. ResolverMerger.mp4

07:11

11.1 SentenceEntityResolverModel.ipynb.html

11. SentenceEntityResolverModel.mp4

08:51

12.1 SentenceEntityResolverApproach.ipynb.html

12. SentenceEntityResolver Model Inference.mp4

13:33

9. De-identification and Obfuscate PHI Data

1.1 ReIdentification.ipynb.html

1. ReIdentification.mp4

05:26

2.1 NameChunkObfuscatorApproach.ipynb.html

2. NameChunkObfuscator Model Inference.mp4

16:41

3.1 NameChunkObfuscator.ipynb.html

3. NameChunkObfuscator.mp4

10:30

4.1 DocumentHashCoder.ipynb.html

4. DocumentHashCoder.mp4

11:30

5.1 DeIdentification.ipynb.html

5.2 DeIdentificationModel.ipynb.html

5. DeIdentification DeIdentificationModel Pt1.mp4

19:13

6.1 DeIdentification.ipynb.html

6.2 DeIdentificationModel.ipynb.html

6. DeIdentification DeIdentificationModel Pt2.mp4

14:26

10. Text Summarization

1.1 MedicalSummarizer.ipynb.html

1. Summarizer.mp4

07:52

2. ExtractiveSummarization.mp4

04:51

11. QuestionAnswering

1.1 Medical Question Answering.ipynb.html

1. QuestionAnswering.mp4

05:14

12. Text Generation

1.1 MedicalTextGenerator.ipynb.html

1. TextGenerator.mp4

09:53

Description

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

Who is this for?

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

What You Need to Know?

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

More details

Description
Hello everyone, welcome to the Healthcare NLPÂ for Data Scientists course, offered by John Snow Labs, the creator of HealthcareÂ NLP library!
In this course, you will explore the extensive functionalities of John Snow Labsâ€™ Healthcare NLP & LLM library,Â 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 forÂ 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 HealthcareÂ NLPÂ 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.
Who this course is for:
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

User Reviews

Rating

average 0

Total votes0

Focused display

We here at John Snow Labs are working to support natural language processing (NLP) tasks in the healthcare, legal, and finance industry. We provide the state-of-the-art algorithms for our clients so they can augment their work with the richness of text data. We offer NLP Summit every quarter, where we also host trainings on how to use our product and library packages. We hope to provide more accessible learning experience for everyone who are interested in NLP.

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.