machine learning vs knowledge based approaches to adr
play

Machine learning vs. knowledge based approaches to ADR - PowerPoint PPT Presentation

May 10, 2023 •22 likes •242 views

Machine learning vs. knowledge based approaches to ADR identification November 2017 Topics Short about us Iden3fying ADRs Machine Learning for seman3c rela3ons iden3fica3on Results Challenges SHORT ABOUT US Focus on

  1. Machine learning vs. knowledge based approaches to ADR identification November 2017

  2. Topics Short about us • Iden3fying ADRs • Machine Learning for seman3c rela3ons • iden3fica3on Results • Challenges •

  3. SHORT ABOUT US Focus on text-analy3cs for Pharmaceu3cals. Since 1998 Othe her text xt sou sources s Voi oice of of the he Pa3 Pa3ent nt Electroni Ele onic He Healt alth h Re Recor ords ds Scien3fic literature – FDA – Patents – Business opportuni3es

  4. SHORT ABOUT US Advanced Databases Group, Universidad Carlos III de Madrid § Research lines: • Natural language processing • Accessibility § Resources produced: • Drug-drug-interac-on collec-on (DDI Corpus) • DINTO ontology

  5. Our goal at TAC ADR “ Combine Knowledge Based with Machine Learning Based approaches to leverage ADR identification ”

  6. Iden%fying ADRs

  7. TO TOPI PIC C EXTRA EXTRACTI CTION N NLP and Resource based approach ¡ SI SIDER DER ¡ UMLS UMLS ¡ Tr Trai aini ning ng cor orpus s

  8. TO TOPI PIC C EXTRA EXTRACTI CTION N NLP and Resource based approach ¡ SI SIDER DER ¡ UMLS UMLS ¡ Tr Trai aini ning ng cor orpus s Dictionary #entries Adverse Reactions 21,826 Factor 41 Severity 158 Animal 27 DrugClass 101

  9. TO TOPI PIC C EXTRA EXTRACTI CTION N NLP and Resource based approach And some rules to iden3fy nega3on: • MeaningCloud Insights Engine API supports this rule syntax •

  10. Machine Learning for seman%c rela%ons iden%fica%on

  11. Machine learning for seman3c rela3ons iden3fica3on Represen3ng ADR men3on context through a set of features: ADRMen9on – Other pairs (where Other is Severity, DrugClass, Ø M1TXT, M1TXT, M2TXT, M2TXT, BWTXT BWTXT: the text of both/between men3ons. Nega9on, Animal or Factor) Ø C1BO C1BOW, W, C2BO C2BOW: bag-of-words of both men3ons. Ø C1PO C1POS, S, C2PO C2POS: part of speech of both men3ons. Ø PB1PO PB1POS, S, PA PA1PO 1POS, S, PB2PO PB2POS, S, PA PA2PO 2POS, S, PWPO PWPOS: the PoS tags of the two tokens before/ aZer/between both men3ons. Ø WA WA1TXT, 1TXT, WB2TXT, WB2TXT, WA WA2TXT, 2TXT, WB1TXT WB1TXT: the two tokens aZer/before the men3on. Ø LA LA1LEM, 1LEM, LB2LEM, LB2LEM, LA LA2LEM, 2LEM, LB1LEM LB1LEM: the lemmas of the two tokens aZer/before both men3ons. Ø LWLEM LWLEM: the lemmas between of the two men3ons Ø NTO NTOKB KB: the number of tokens between the two men3ons.

  12. Machine learning for seman3c rela3ons iden3fica3on And the algorithm? § SVM, support vector machines (using scikit-learn on Python) § Specifically, SVC implementa3on: • Default parameter values • Linear kernel But, no deep learning??!! Of course (CNN), but not in the official runs.

  13. Results

  14. Results • Task 1. ADR and related en33es Low precision!! • Task 2. Rela3ons between ADRs and en33es Oh, oh!!

  15. Results • Task 3. Posi3ve ADRs Only a few negated men9ons? PreQy good!! • Task 4. Normaliza3on through MedDRA Using dic9onaries with seman9c informa9on produces nice results

  16. Challenges

  17. Challenges • Nega3on iden3fica3on requires more effort (not only on the ADRs field). Some weird things found in the test set: Eg.: The most frequently observed malignancies other than non- melanoma skin cancer … Negation? • CNNs and the use of syntac3c features improves results P R F1 Other 0.71 0.81 0.76 Negated 0.72 0.40 0.51 Hypothetical 0.75 0.75 0.75 Effect 0.76 0.61 0.68 Avg / total 0.73 0.73 0.73

  18. Challenges • Recall must be improved: o separated mul3word men3ons o ADRs with no MedDRA code, enough lexical resources? • How to approach errors when applying deep learning? • Enough accuracy for prac3cal applica3ons? What does FDA think?

  19. Thanks QUESTIONS?

  20. LabDA Resources Corpus DDI (Drug-Drug Interac3ons) 1,025 annotated documents, 18,502 en33es and } 5,028 DDIs (by expert pharma) MedLine and DrugBank texts } Annota3ns guidelines and interannotator agreement. } Available at labda.inf.uc3m.es } Used at DDIExtrac3on 2011 and DDIExtrac3on 2013 } Semeval Tasks

  21. LabDA Resources DINTO Ontology- knowledge about drugs and intera3ons (11,555 DDIs and 8,786 pharmacological en33es). Available at OBO Foundry Applica3on to Informa3on Extrac3on and Predic3on

  22. Meani Me aning ngClou Cloud d LLC LLC Automa3ng the extrac3on of Meaning from any informa3on source. Addr ddress ss 35-37 36th Street 11106 Astoria NY Cont Contac act Inf nfo o jmar3nez@meaningcloud.com Tele Te lephone hone Phone: +1 (646) 403-3104 meaningcloud.com

Recommend

10a Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A

10a Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A

10a Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A Framework for 10.6 Unsupervised Learning Symbol-based Learning 10.7 Reinforcement Learning 10.2 Version Space Search 10.8 Epilogue and 10.3 The

935 views • 54 slides
10d Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A

10d Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A

10d Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A Framework for 10.6 Unsupervised Learning Symbol-based Learning 10.7 Reinforcement Learning 10.2 Version Space Search 10.8 Epilogue and 10.3 The

1.05k views • 29 slides
10c Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A

10c Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A

10c Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A Framework for 10.6 Unsupervised Learning Symbol-based Learning 10.7 Reinforcement Learning 10.2 Version Space Search 10.8 Epilogue and 10.3 The

605 views • 35 slides
10b Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A

10b Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A

10b Machine Learning: Symbol-based 10.0 Introduction 10.5 Knowledge and Learning 10.1 A Framework for 10.6 Unsupervised Learning Symbol-based Learning 10.7 Reinforcement Learning 10.2 Version Space Search 10.8 Epilogue and 10.3 The

866 views • 50 slides
Machine-Learning & AI-based Approaches for GPCR Bioactive Ligand Discovery Sebastian Raschka,

Machine-Learning & AI-based Approaches for GPCR Bioactive Ligand Discovery Sebastian Raschka,

Cambridge Healthtech Institute's 14th Annual GPCR-Based Drug Discovery Discovery on Target Conference September 19, 2019 | Boston, MA https://www.discoveryontarget.com/GPCR-drug-discovery Machine-Learning & AI-based Approaches for GPCR

411 views • 38 slides
TAXONOMY AND CHALLENGES IN MACHINE LEARNING-BASED APPROACHES TO DETECT ATTACKS IN THE INTERNET

TAXONOMY AND CHALLENGES IN MACHINE LEARNING-BASED APPROACHES TO DETECT ATTACKS IN THE INTERNET

The 15th International Conference on Availability, Reliability and Security (ARES 2020) August 25 to August 28, 2020 in Dublin, Ireland ID-86 workshop paper (IoT-SECFOR) TAXONOMY AND CHALLENGES IN MACHINE LEARNING-BASED APPROACHES TO DETECT

583 views • 13 slides
Linguistics 384: Language and Computers approaches Linguistic knowledge Topic 5: Machine

Linguistics 384: Language and Computers approaches Linguistic knowledge Topic 5: Machine

Language and Computers Topic 5: Machine Translation Introduction Examples for Translations Background: Dictionaries Transformer Linguistics 384: Language and Computers approaches Linguistic knowledge Topic 5: Machine Translation based

1.85k views • 184 slides
Machine Learning: Der KDD-Prozess Knowledge Discovery in Databases Machine Learning Data-Mining

Machine Learning: Der KDD-Prozess Knowledge Discovery in Databases Machine Learning Data-Mining

Machine Learning: Der KDD-Prozess Knowledge Discovery in Databases Machine Learning Data-Mining Aufgaben Aufgabe Von Daten zum Wissen Wissensextraktion durch We are drowing in automatisches Erkennen von information,

1.01k views • 47 slides
Learning Approaches to Estimate Depth from RGB Lecture 5 What will we learn - Latest Approaches

Learning Approaches to Estimate Depth from RGB Lecture 5 What will we learn - Latest Approaches

Learning Approaches to Estimate Depth from RGB Lecture 5 What will we learn - Latest Approaches to Depth Estimation based on Machine Learning (DNNs) Why do we need new approaches? Paper1 -> CNNs for Depth estimation Paper2

634 views • 17 slides
The logic of learning: The logic of learning: logic and knowledge representation logic and

The logic of learning: The logic of learning: logic and knowledge representation logic and

The logic of learning: The logic of learning: logic and knowledge representation logic and knowledge representation in machine learning in machine learning Peter A. Flach Department of Computer Science University of Bristol

771 views • 59 slides
Linguistics 384: Language and Computers Transformer approaches Topic 5: Machine Translation

Linguistics 384: Language and Computers Transformer approaches Topic 5: Machine Translation

Language and Computers Topic 5: Machine Translation Introduction Examples for Translations Background: Dictionaries Linguistics 384: Language and Computers Transformer approaches Topic 5: Machine Translation Linguistic knowledge based

2.2k views • 184 slides
Knowledge Engineering Sargur Srihari srihari@cedar.buffalo.edu 1 Machine Learning Srihari

Knowledge Engineering Sargur Srihari srihari@cedar.buffalo.edu 1 Machine Learning Srihari

Machine Learning Srihari Knowledge Engineering Sargur Srihari srihari@cedar.buffalo.edu 1 Machine Learning Srihari Topics Picking Variables Determining Structure Determining Probabilities 2 Machine Learning Srihari Knowledge

313 views • 12 slides
26:198:722 Expert Systems I Knowledge representation I Knowledge acquisition I Machine learning I

26:198:722 Expert Systems I Knowledge representation I Knowledge acquisition I Machine learning I

26:198:722 Expert Systems I Knowledge representation I Knowledge acquisition I Machine learning I ID3 & C4.5 Knowledge Representation Recall: I Knowledge engineering F Knowledge acquisition N Knowledge elicitation F Knowledge representation N

668 views • 62 slides
I. Evolutionary Knowledge Machine (EKM) II. AI Doctor Platform (IMP) III. Case Studies (Silo)

I. Evolutionary Knowledge Machine (EKM) II. AI Doctor Platform (IMP) III. Case Studies (Silo)

AI Doctor based on Department of Computer Science & Engineering, Evolutionary Knowledge Machine (EKM) Kyung Hee University KOREA February 19 th , 2019 Prof. Sungyoung Lee http://uclab.khu.ac.kr 2 Contents / I. Evolutionary Knowledge

416 views • 41 slides
Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis

Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis

Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis Genetics-based Machine Learning and Behavior Based Robotics: A New Synthesis, Marco Dorigo, Uwe Schnepf, IEEE Transactions on System, MAn, and Cybernetics,

348 views • 34 slides
Interpreting Embedding Models of Knowledge Bases: Model Agnostic Approaches 2018 ICML Workshop on

Interpreting Embedding Models of Knowledge Bases: Model Agnostic Approaches 2018 ICML Workshop on

Introduction & Background Interpreting Embedding Models of KBs Experiments Conclusion Interpreting Embedding Models of Knowledge Bases: Model Agnostic Approaches 2018 ICML Workshop on Human Interpretability in Machine Learning Arthur C.

751 views • 24 slides
Recent approaches to machine learning of interatomic potentials seen from a perspective of plasma

Recent approaches to machine learning of interatomic potentials seen from a perspective of plasma

Recent approaches to machine learning of interatomic potentials seen from a perspective of plasma material interaction and primary radiation Recent approaches to machine learning of interatomic potentials seen from a perspective of plasma

903 views • 29 slides
Learning to Inflate Tom Rudelius IAS Based on 1810.05159/hep-th Outline Machine Learning

Learning to Inflate Tom Rudelius IAS Based on 1810.05159/hep-th Outline Machine Learning

Learning to Inflate Tom Rudelius IAS Based on 1810.05159/hep-th Outline Machine Learning and hep-th Introduction to Inflation Learning to Inflate Algorithm Results Possible Next Steps Machine Learning and hep-th Machine

709 views • 34 slides
Machine learning approaches to predicting protein-ligand binding Dr Pedro J Ballester MRC

Machine learning approaches to predicting protein-ligand binding Dr Pedro J Ballester MRC

Machine learning approaches to predicting protein-ligand binding Dr Pedro J Ballester MRC Methodology Research Fellow EMBL-EBI, Cambridge, United Kingdom EBI is an Outstation of the European Molecular Biology Laboratory. Talk outline 1.

880 views • 30 slides
Advancing human security through knowledge-based approaches to reducing vulnerability and

Advancing human security through knowledge-based approaches to reducing vulnerability and

Advancing human security through knowledge-based approaches to reducing vulnerability and environmental Institute for Environment & Human Security risks United Nations University UNGA 2nd Committee Special Event - Climate Change:

512 views • 23 slides
Thinking about Genomics through a Machine Learning Lens: Basics of how several DNA- and RNA-based

Thinking about Genomics through a Machine Learning Lens: Basics of how several DNA- and RNA-based

Thinking about Genomics through a Machine Learning Lens: Basics of how several DNA- and RNA-based problems translate into commonly-studied areas of machine learning Faculty Lead Discussion (short version) 26 June 2018 GAUSSI Summer Retreat

323 views • 21 slides
Using machine learning approaches to identify imaging markers that predict antidepressant

Using machine learning approaches to identify imaging markers that predict antidepressant

Using machine learning approaches to identify imaging markers that predict antidepressant response Amit Etkin, MD, PhD Associate Professor Department of Psychiatry and Behavioral Sciences Stanford Neurosciences Institute Stanford University

474 views • 22 slides
Introduction to Machine Learning CMU-10701 3. Bayes classification Barnabs Pczos & Aarti

Introduction to Machine Learning CMU-10701 3. Bayes classification Barnabs Pczos & Aarti

Introduction to Machine Learning CMU-10701 3. Bayes classification Barnabs Pczos & Aarti Singh 2014 Spring What about prior knowledge ? (MAP Estimation) 2 What about prior knowledge, Domain knowledge, expert knowledge We know the

746 views • 39 slides
INTRODUCTION TO MACHINE LEARNING Joseph C. Osborn CS 51A Spring 2020 Machine Learning is

INTRODUCTION TO MACHINE LEARNING Joseph C. Osborn CS 51A Spring 2020 Machine Learning is

INTRODUCTION TO MACHINE LEARNING Joseph C. Osborn CS 51A Spring 2020 Machine Learning is Machine learning is about predicting the future based on the past. -- Hal Daume III Machine Learning is Machine learning is about predicting

917 views • 59 slides

More recommend