Building Cognitive Computing for Healthcare

Transcription

1 Building Cognitive Computing for Healthcare Boston University Digital Health Initiative Round Table February 27 th 2017 Patrick McNeillie M.D. Clinical Lead and Senior Architect on Watson for Genomics

2 Agenda Building Cognitive Computing -Defining the Importance of Natural Language Processing (NLP) Applying Cognitive to a Complex Problem Surfacing Valuable Insights in Big Data

3 Moving Beyond Jeopardy: Bringing Cognitive Computing to Healthcare

4 Applying Cognitive Computing to Complex Problems Define Attributes Attribute Extraction Compare to Prior Knowledge Clinical Expert What is the best treatment for a given patient? Stage Stage IIIA IIIA Squamous Cell Squamous Cell Lung Cancer Lung Cancer PD-L1 Receptor PD-L1 Receptor Data Set

5 Applying Cognitive Computing to Complex Problems Define Attributes Attribute Extraction Compare to Prior Knowledge Clinical Expert What is the best treatment for a given patient? Stage Stage IIIA IIIA Squamous Cell Squamous Cell Lung Cancer Lung Cancer PD-L1 Receptor PD-L1 Receptor Stage IIIA PD-L1 Receptor Natural Language Processing Lung Cancer Squamous Cell Data Set

6 Applying Cognitive Computing to Complex Problems Define Attributes Attribute Extraction Compare to Prior Knowledge Clinical Expert What is the best treatment for a given patient? Stage Stage IIIA IIIA Squamous Cell Squamous Cell Lung Cancer Lung Cancer PD-L1 Receptor PD-L1 Receptor Stage IIIA PD-L1 Receptor Natural Language Processing Lung Cancer Squamous Cell Stage IIIA PD-L1 Receptor Atezolizumab Lung Cancer Treatment Squamous Cell Docetaxel Survival: 13.8 Months Survival: 9.6 Months Data Set

7 Applying Cognitive Computing to Complex Problems Define Attributes Attribute Extraction Compare to Prior Knowledge Clinical Expert What is the best treatment for a given patient? Stage Stage IIIA IIIA Squamous Cell Squamous Cell Lung Cancer Lung Cancer PD-L1 Receptor PD-L1 Receptor Stage IIIA PD-L1 Receptor Natural Language Processing Lung Cancer Squamous Cell Stage Stage Squamous Squamous IIIA IIIA Cell Cell Lung Lung Cancer Cancer PD-L1 PD-L1 Receptor Receptor Treatment?? Atezolizumab Docetaxel Survival: 13.8 Months Survival: 9.6 Months Data Set

8 Differences in attribute extraction between Machine Learning and Rules-Based NLP Machine Learning NLP Rules-Based NLP Rule: connect disease to therapy if within 10 words of each other nephrotic syndrome glomerulosclerosis >10 word <10 word <none> cortisol Disease Therapy steroid resistant nephrotic syndrome <subtype> glomerulosclerosis rxdx-101 Disease Therapy <conflict> nephrotic syndrome <conflict> glomerulosclerosis <incorrect> cortisol

9 Applying Cognitive to Genomics

10 Impact on Medical Genomics [COSMIC. Wellcome Trust Sanger Institute. October 2015 ] [Modern Pathology Mar;25(3): (accessed 10/5/2015)]

11 What s the best treatment given a tumor sequence?

12 Ingest & Learn Reference Genomes Molecular Protein Pathways Genomic Databases Clinical Partners (currently 16 centers) Unstructured Text Best Practices Guidelines Patient Data Literature Articles Clinical Trials Conference Reports Process Patient Data Output Report Personalized Driver Alterations and Targeted Therapies

13 Our Approach to NLP Boston University Digital Health Initiative Round Table Focused Annotation Specialist Team (FAST) NLP Expert Annotator Label Documents Standardize Ontologies and Dictionaries Train Machine Learning Measure Performance 1.00 Enable Integration & Reuse Reduce Bias

14 Annotating mentions Boston University Digital Health Initiative Round Table 2014 International Business Machines Corporation 14

15 Annotating relationships Boston University Digital Health Initiative Round Table 2014 International Business Machines Corporation 15

16 Ensuring Agreement: Measuring Kappa Scores 100% Agreement Domain Experts Annotators Time

17 Concepts Detection: Compare to a standard and iterate Boston University Digital Health Initiative Round Table Model Performance for Labeling Concepts When a plateau is reached, development teams need to assess if the plateau is secondary to bias or variance to efficiently apply resources and iterate F1 Precision Recall

18 Relationship Detection: Compare to a standard and iterate Model Performance for Labeling Relationships When a plateau is reached, development teams need to assess if the plateau is secondary to bias or variance to efficiently apply resources and iterate F1 Precision Recall

19 Maintaining Context Within and Across Documents <text>unexpectedly, we find that EGFR A763_Y764insFQEA is highly sensitive to EGFR TKIs in vitro, and patients whose NSCLCs harbor this mutation respond to erlotinib.</text> ="alters"> <relation rid="s6-r1" subtype="other" type="alters"> <rel_entity_arg argnum="1" eid="e-39"/> <rel_entity_arg argnum="2" eid="c1"/> <relmentions> <relmention class="specific" modality="asserted" rmid="s6-r1-1" score="1" tense="unspecified"> <rel_mention_arg argnum="1" mid="s6-m6">a763_y764insfqea</rel_mention_arg> <rel_mention_arg argnum="2" mid="s6-m0">egfr</rel_mention_arg> <relation rid="s6-r5" subtype="other" type="drives"> <rel_entity_arg argnum="1" eid="c1"/> <rel_entity_arg argnum="2" eid="c3"/> <relmentions> <relmention class="specific" modality="asserted" rmid="s6-r5-1" score="1" tense="unspecified"> <rel_mention_arg argnum="1" mid="s6-m0">egfr</rel_mention_arg> Variant s6-m6 A763_Y764insFQEA type=category Rel: Alters PMID: Gene/Protein S6-m0 EGFR type=concept <rel_mention_arg argnum="2" mid="s6-m8">egfr TKIs</rel_mention_arg> Gene/Protein S6-m0 EGFR type=concept Rel: Sensitive PMID: Cancer Type S6-m8 EGFR TKIs type=concept

20 Clinical Knowledge: Example Graph

21 Boston University Digital Health Initiative Round Table Clinical Knowledge: Extracted Data from Corpus of Documents

22 Boston University Digital Health Initiative Round Table Clinical Knowledge: Extracted Data from Corpus of Documents

23 Output Sample from Knowledge Graph Query

24 Leveraging Literature Articles to Support Clinical Care

25 Leveraging Literature Articles to Support Clinical Care

26 Summary

27 Empowering Healthcare Professionals with Cognitive Computing The Process of Building Work Closely with domain experts Leverage tools (like NLP) to extract unstructured concepts and their associations Combine unstructured and structured data to create knowledge Value of Applying to Healthcare Increased efficiency Reduced bias Scaling expert knowledge Innovating across a complex domain Validate and iterate with domain experts With Cognitive computing, the extraction of information from clinical data and published literature can empower healthcare professionals to stay up-to-date, focus on the complex problems, and spend more time with caring for patients.

28 Questions? (remember, I m not Watson..)