1. 1 Question Answering in Biomedicine Student: Andreea Tutos Id: 41064739 Supervisor: Diego Molla

2. 2 Project outline Why medical question answering? Current research Project methodology Project outcomes

3. 3 Why Question Answering? Thousands of new biological and medical research articles published daily world wide 66% of physicians report the volume of medical information as unmanageable (Craig et al, 2001). The main impediment in maximizing the utility of research data: insufficient time

4. 4 Project outline Why question answering? Current research Evaluation methodology Project outcomes

5. 5 What is Question Answering? The task of automatically finding an answer to a question Relies on analyzing large collections of documents Aims to provide short and concise answers rather than a list of relevant documents

6. 6 Key steps to follow Select the domain knowledge source Construct the corpus of questions Analyze the input question – Classify the question – Construct the search query Extract the answer

7. 7 Domain knowledge sources Reliability of medical information is critical (NetScoring) MEDLINE - medical repository maintained by the US National Library of Medicine (controlled vocabulary thesaurus MeSH)

8. 8 Key steps to follow Select the domain knowledge source Construct the corpus of questions Analyze the input question – Classify the question – Construct the search query Extract the answer

9. 9 Question corpus sources Question sources we have reviewed in our research: – The Parkhurst Exchange website – The Clinical Questions Collection website – The Journal of Family Practice website

10. 10 Questions format Natural language question : “In children with an acute febrile illness, what is the efficacy of single-medication therapy with acetaminophen or ibuprofen in reducing fever?” PICO format question: “Problem/Population: acute febrile illness / in children Intervention: acetaminophen Comparison: ibuprofen Outcome: reducing fever “ (Demner - Fushman and Lin, 2007 )

11. 11 Key steps to follow Select the domain knowledge source Construct the corpus of questions Analyze the input question – Classify the question – Construct the search query Extract the answer

12. 12 Question classification The Evidence taxonomy (Ely et al, 2002)

13. 13 Query analysis Processes included: – Keyword selection: extract keywords using parsers such as LTCHUNK identify named entities with the support of UMLS – Answer pattern generation (different combinations of query terms) (Molla and Vicedo, 2009)

14. 14 Key steps to follow Select the domain knowledge source Construct the corpus of questions Analyze the input question – Classify the question – Construct the search query Extract the answer

15. 15 Answer extraction Identify relevant sentences that answer the question Rank the answer candidates (popularity, similarity with the question, answer patterns, answer validation) (Molla and Vicedo, 2009) Could use the IMRAD (Introduction, Methods, Results and Discussion) structure of biomedical articles (MedQA)

16. 16 Search engines and question answering systems Generic: – Google – Answers.com – OneLook Medical: – PubMed – MedQA – Google on PubMed only

17. 17 Project outline Why question answering? Current research Project methodology Project outcomes

18. 18 Project methodology - Question corpus We have sourced 50 clinical questions and their answers from the Parkhurst Exchange web site

19. 19 Project methodology - Question processing We have defined five levels of processing to be applied to improve search outcomes.

20. 20 Project methodology – Scoring system We have used a scoring system first referred to in the Text Retrieval Conference (TREC), called Mean Reciprocal Rank (MRR) (Voorhees, 2001) A relevant link returned in nth position (n<= 10) received a score of 1/n

21. 21 Results – No Intervention questions No Intervention questions average scores

22. 22 Results – Intervention questions Intervention questions average scores

23. 23 Results – Answer location Answer location in scientific articles

24. 24 Project outline Why question answering? Current research Project methodology Project outcomes

25. 25 Medical search engines and QA systems conclusions Pubmed obtained similar scores for both categories (0.27 for No Intervention and 0.24 for Intervention questions) Medical search engines perform relatively equal on Intervention and No Intervention questions

26. 26 Generic search engines and QA systems conclusions Google recorded the best performance for both categories of questions Both Google and Answers.com scored better results on No Intervention questions than on Intervention questions Non-medical oriented search engines have more difficulties in producing answers for scenario- based, complex medical questions.

27. 27 Conclusions All selected questions are answerable with the current technology 50% of answers are located in the Abstract section of scientific articles; 25% in the Conclusions section No Intervention questions are easier to answer than Intervention questions when it comes to generic search technology

28. 28 Thank you