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Analyzing Citation Patterns in COVID-19 Literature

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BigML, Inc
BigML, Inc

Text Analysis: Discovering Insights for the Healthcare Industry. Learn how Machine Learning helps discover insights for the Healthcare industry by analyzing text. The lecturer is Tomáš Kliegr, Associate Professor at the Department of Information and Knowledge Engineering at Prague University of Economics and Business (VSE). *Machine Learning School for Business Schools 2021: Virtual Conference.

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#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Analyzing Text: Discovering Insights for the Healthcare Industry Why was this cited? Generating semantic explanations for the CORD-19 corpus Tomas Kliegr Prague University of Economics and Businesss Czech Republic tomas.kliegr@vse.cz
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Prague University of Economics and Business • The largest university in the field of economics, business and information technology in Czechia, 15 000 students (BSc., MSc, MBA, Ph.D) • English Master programmes – Information Systems Management – Economic and Data Analysis
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Data Science Curriculum • Programming is a mandatory part of our Applied Informatics BSc. • However, we use visual approaches non-programming approach in our data introductory data science course • Up to 200 students/semester 3 2012 2014 credit risk case study 2021 CLUSTERING
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature The problem 4 Imagine you manage a research team. You need to pair the expertise of your staff with the needs of the wider research community, which will build upon your results. How to find out that research made impact? One of the main KPIs in science is the number of citations. What made past research successful – highly cited? We will show how to leverage existing freely available research articles to get the answers. Part of CORD-19 dataset containing more than 400.000 articles related to Sars-Cov-2. Source: VOSViewer
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature 5
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature 6
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature 7
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature 8
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Example 9 Pratelli, A. (2008). Canine coronavirus inactivation with physical and chemical agents. The Veterinary Journal, 177(1), 71-79. Increases citation probability Decreases citation probability • Our original approach was implemented in Python and involved many trial and error iterations and hundreds of hours of computation time. Lucie Beranová, Marcin Joachimiak, Tomáš Kliegr, Gollam Rabby, Vilém Sklenák. Why was this cited? Generating semantic explanations for the CORD-19 corpus. Under preparation. • In this tutorial, we show how the modelling part can be recreated without programming skills using cloud-based machine learning.
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Data preprocessing 10 > 300.000 articles 36.000 3.000 Open citations available CORD-on-FHIR subset
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature 11 A dive into the data Target
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Upload data 12
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Processing of text data 13
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Text processing options "structure of coronavirus main proteinase reveals" • Tokenization and Unigrams – structure;of;coronavirus ;main;proteinase;reveals • Bigrams: – structure of; of coronavirus; coronavirus main; main proteinase, proteinase releals; 14
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Text processing options "Structure of coronavirus main proteinase reveals" • Stemming: proteinase => protein • Stop-word removal: – structure;of;coronavirus ;main;proteinase;reveals 15
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Adding a new column • Since we aim for a classification task, we will add a new column indicating if a paper is above or under median of citation count. 16
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature 17
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Creating train/test split • To validate our evaluation model, we need to create a train/test split 18
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Setting up the split • Using a random seed will ensure we get the same split each time 19 1
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Creating a logistic regression model 20
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Setting up the model 21 Suggested to be removed based on automatic feature importance assessment Manualy remove paper id Check that target is set correctly
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Remove predictors 22
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Evaluation 23
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Evaluation 24
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Inspecting model – which topics? 25
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature How to NOT phrase the title? 26
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Inspecting model – which journals? 27
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Size of author team 28
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature .. and patience will also help 29
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Decision tree ensemble 30
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Evaluating the ensemble 31 Disadvantages of ensembles: • Lower interpretability (accuracy-interpratibility tradefoff) • Longer learning time • Longer time required to apply the model
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Comparing models 32
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Deployment 33
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Other insights from the full analysis 34 „Lucie Beranová, Marcin Joachimiak, Tomáš Kliegr*, Gollam Rabby, Vilém Sklenák. Why was this cited? Generating semantic explanations for the CORD-19 corpus. Under preparation.“ Preprints shared on request to *. • Citation „biases“ • Articles with authors who have western sounding names are better cited • Phylogenetic distance from human virus: Feline (FIPV, FCOV) and canine coronaviruses (CCOV) are lowly cited possibly because these viruses are more distant from the human virus than camel and bat viruses. • Accuracy-interpretability trade-off • Embeddings-based language models, TF-IDF • Random Forests, Rule learning, Neural networks • Directly explainable models (rules) vs explanation algorithms like LIME or Shapley values
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Credits • Colleagues and Ph.D. students at VSE: – Ing. Lucie Beranová (help with mining in Python) – Gollam Rabby, MSc. (citation counts, proof of concept) – prof. Vilém Sklenák – bibliometric expert • Dr. Marcin Joachimiak - Computational Biosciences, LBL Berkeley – Interpretation of results 35
#BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Thanks for your attention! Tomáš Kliegr, UEP tomas.kliegr@vse.cz Open Doors Day on YouTube March 3, 2021 https://fis.vse.cz/english/

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Analyzing Citation Patterns in COVID-19 Literature

  • 1. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Analyzing Text: Discovering Insights for the Healthcare Industry Why was this cited? Generating semantic explanations for the CORD-19 corpus Tomas Kliegr Prague University of Economics and Businesss Czech Republic tomas.kliegr@vse.cz
  • 2. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Prague University of Economics and Business • The largest university in the field of economics, business and information technology in Czechia, 15 000 students (BSc., MSc, MBA, Ph.D) • English Master programmes – Information Systems Management – Economic and Data Analysis
  • 3. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Data Science Curriculum • Programming is a mandatory part of our Applied Informatics BSc. • However, we use visual approaches non-programming approach in our data introductory data science course • Up to 200 students/semester 3 2012 2014 credit risk case study 2021 CLUSTERING
  • 4. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature The problem 4 Imagine you manage a research team. You need to pair the expertise of your staff with the needs of the wider research community, which will build upon your results. How to find out that research made impact? One of the main KPIs in science is the number of citations. What made past research successful – highly cited? We will show how to leverage existing freely available research articles to get the answers. Part of CORD-19 dataset containing more than 400.000 articles related to Sars-Cov-2. Source: VOSViewer
  • 5. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature 5
  • 6. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature 6
  • 7. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature 7
  • 8. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature 8
  • 9. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Example 9 Pratelli, A. (2008). Canine coronavirus inactivation with physical and chemical agents. The Veterinary Journal, 177(1), 71-79. Increases citation probability Decreases citation probability • Our original approach was implemented in Python and involved many trial and error iterations and hundreds of hours of computation time. Lucie Beranová, Marcin Joachimiak, Tomáš Kliegr, Gollam Rabby, Vilém Sklenák. Why was this cited? Generating semantic explanations for the CORD-19 corpus. Under preparation. • In this tutorial, we show how the modelling part can be recreated without programming skills using cloud-based machine learning.
  • 10. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Data preprocessing 10 > 300.000 articles 36.000 3.000 Open citations available CORD-on-FHIR subset
  • 11. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature 11 A dive into the data Target
  • 12. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Upload data 12
  • 13. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Processing of text data 13
  • 14. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Text processing options "structure of coronavirus main proteinase reveals" • Tokenization and Unigrams – structure;of;coronavirus ;main;proteinase;reveals • Bigrams: – structure of; of coronavirus; coronavirus main; main proteinase, proteinase releals; 14
  • 15. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Text processing options "Structure of coronavirus main proteinase reveals" • Stemming: proteinase => protein • Stop-word removal: – structure;of;coronavirus ;main;proteinase;reveals 15
  • 16. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Adding a new column • Since we aim for a classification task, we will add a new column indicating if a paper is above or under median of citation count. 16
  • 17. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature 17
  • 18. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Creating train/test split • To validate our evaluation model, we need to create a train/test split 18
  • 19. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Setting up the split • Using a random seed will ensure we get the same split each time 19 1
  • 20. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Creating a logistic regression model 20
  • 21. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Setting up the model 21 Suggested to be removed based on automatic feature importance assessment Manualy remove paper id Check that target is set correctly
  • 22. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Remove predictors 22
  • 23. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Evaluation 23
  • 24. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Evaluation 24
  • 25. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Inspecting model – which topics? 25
  • 26. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature How to NOT phrase the title? 26
  • 27. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Inspecting model – which journals? 27
  • 28. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Size of author team 28
  • 29. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature .. and patience will also help 29
  • 30. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Decision tree ensemble 30
  • 31. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Evaluating the ensemble 31 Disadvantages of ensembles: • Lower interpretability (accuracy-interpratibility tradefoff) • Longer learning time • Longer time required to apply the model
  • 32. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Comparing models 32
  • 33. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Deployment 33
  • 34. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Other insights from the full analysis 34 „Lucie Beranová, Marcin Joachimiak, Tomáš Kliegr*, Gollam Rabby, Vilém Sklenák. Why was this cited? Generating semantic explanations for the CORD-19 corpus. Under preparation.“ Preprints shared on request to *. • Citation „biases“ • Articles with authors who have western sounding names are better cited • Phylogenetic distance from human virus: Feline (FIPV, FCOV) and canine coronaviruses (CCOV) are lowly cited possibly because these viruses are more distant from the human virus than camel and bat viruses. • Accuracy-interpretability trade-off • Embeddings-based language models, TF-IDF • Random Forests, Rule learning, Neural networks • Directly explainable models (rules) vs explanation algorithms like LIME or Shapley values
  • 35. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Credits • Colleagues and Ph.D. students at VSE: – Ing. Lucie Beranová (help with mining in Python) – Gollam Rabby, MSc. (citation counts, proof of concept) – prof. Vilém Sklenák – bibliometric expert • Dr. Marcin Joachimiak - Computational Biosciences, LBL Berkeley – Interpretation of results 35
  • 36. #BigMLSchool Twitter: @kliegr Web: kliegr.eu Citation patterns in COVID-19 related biomedical literature Thanks for your attention! Tomáš Kliegr, UEP tomas.kliegr@vse.cz Open Doors Day on YouTube March 3, 2021 https://fis.vse.cz/english/