Automatic extraction of adverse drug reaction terms from medical free text
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1 Automatic extraction of adverse drug reaction terms from medical free text Ola Caster Uppsala Monitoring Centre WHO Collaborating Centre for International Drug Monitoring ISCB 2009, Prague, Czech Republic 26 th August Joint work with Johanna Strandell, Andrew Bate, and I. Ralph Edwards
2 Safety monitoring of drugs Marketed drugs are continuously monitored for adverse drug reactions (ADRs) not detected during clinical testing So called individual case safety reports (ICSRs) are a key component of this monitoring Primarily spontaneously reported incidents from clinical practice The Uppsala Monitoring Centre (UMC) collects ICSRs from more than 90 countries Vigibase: pooled database of almost 5 million reports
3 ADR terminologies and free text sources Reactions are classified according to standard, hierarchical, ADR terminologies MedDRA WHO-ART, which is maintained by UMC Statistical analyses depend on this classification For example data mining for pairs of drugs and ADR terms occurring more often than expected However, lots of relevant information is only available in free text For example the Summary of Product Characteristics
4 ADR terminologies and free text sources Terms from ADR terminologies do occur in medical free text sources however, exact matching is likely to exhibit poor sensitivity This precludes efficient comparison between results from statistical analyses and published information Of particular interest: Is a drug-adr pair highlighted by data mining of ICSRs already listed in (free text) standard literature sources? Purpose is to detect as yet unknown adverse reactions
5 Objective To develop an algorithm that can extract ADR terms from medical free text at higher sensitivity than exact matching, yet with high precision
6 Data sources XML version of Stockley's Drug Interactions [1] Well-renowned source of information on drug-drug interactions Each interaction, including its potential adverse effects, is described in free text database entries in used version The WHO-ART terminology for ADRs 5770 terms (lowest level of hierarchy)
7 The algorithm exemplified ADR term QT prolonged Free text Some quinolones (e.g. gatifloxacin) can prolong the QT interval and [ ] when used with quinidine. A real example from Stockley s database Exact matching does not work Important aspects of the algorithm will be explained
8 Algorithmic step None Remove nonalphanumerics (+ lower case) ADR term QT prolonged qt prolonged Free text Some quinolones (e.g. gatifloxacin) can prolong the QT interval and [ ] when used with quinidine. some quinolones e g gatifloxacin can prolong the qt interval and [ ] when used with quinidine Problem 1: There is a the in the free text but not in the ADR term Solution 1: Remove stopwords
9 Stopwords Words that carry none or little semantical meaning... if, of, any, for, at, are, also... Quite common to remove stopwords in algorithms like this The list used here comes from the Snowball project [2]
10 Algorithmic step None Remove nonalphanumerics (+ lower case) Remove stopwords ADR term QT prolonged qt prolonged qt prolonged Free text Some quinolones (e.g. gatifloxacin) can prolong the QT interval and [ ] when used with quinidine. some quinolones e g gatifloxacin can prolong the qt interval and [ ] when used with quinidine quinolones e g gatifloxacin can prolong qt interval [...] used quinidine Problem 2: The words are in different grammatical forms (prolonged vs prolong) Solution 2: Use stemming
11 Stemming Each word is replaced by its stem Increases the chance to match words with the same semantic meaning, but with different grammatical functions interact, interacts, interacted, interaction, interacting would all be mapped to interact The stemming algorithm used here was the so called Porter algorithm [3]
12 Algorithmic step None Remove nonalphanumerics (+ lower case) Remove stopwords Use stemming ADR term QT prolonged qt prolonged qt prolonged qt prolong Free text Some quinolones (e.g. gatifloxacin) can prolong the QT interval and [ ] when used with quinidine. some quinolones e g gatifloxacin can prolong the qt interval and [ ] when used with quinidine quinolones e g gatifloxacin can prolong qt interval [...] used quinidine quinolon e g gatifloxacin can prolong qt interv [...] use quinidin Problem 3: The order is wrong Solution 3: Permute the ADR term
13 Permutation of ADR terms Many ADR terms that include an adjective are constructed in the opposite order of what would be normal in free text Terminology: scoliosis congenital Free text: congenital scoliosis Use every possible order of the individual words of the ADR term
14 Algorithmic step None Remove nonalphanumerics (+ lower case) Remove stopwords Use stemming Use permutation ADR term QT prolonged qt prolonged qt prolonged qt prolong prolong qt Free text Some quinolones (e.g. gatifloxacin) can prolong the QT interval and [ ] when used with quinidine. some quinolones e g gatifloxacin can prolong the qt interval and [ ] when used with quinidine quinolones e g gatifloxacin can prolong qt interval [...] used quinidine quinolon e g gatifloxacin can prolong qt interv [...] use quinidin quinolon e g gatifloxacin can prolong qt interv [...] use quinidin
15 Outline of algorithm 1. For each ADR term and free text entry Remove all non-alphanumerics and set to lower case Remove all stopwords Exchange each indvidual word by its stem 2. For each free text entry as resulting from step 1 Look for occurrences of each ADR term as resulting from step 1, as well as all permutations of its individual words 3. Study frequently non-matched words that are relevant, and, if possible, add synonyms to ADR terms in order to match these words (not mandatory) 4. Reiterate steps 1 and 2 (not mandatory)
16 Synonyms A few examples from this study: Original word haemorrhage oestrogen cardiac Synonym bleeding estrogen heart
17 Overall result The algorithm extracted ADR terms from database entries
18 Results disseminated Removal of stopwords was less important than using permutation, which was less important than stemming Combined effect (including synonyms): 49% increase in number of matches Suggests increased sensitivity
19 What about specificity? Relevant to examine whether the increased sensitivity comes at a loss of specificity A subset of findings were manually reviewed The rate of false positives is acceptable
20 Conclusion The algorithm, which combines generic principles with addition of source-specific synonyms, is able to increase the hit rate, and thus the sensitivity, compared to exact matching, while maintaining a high specificity.
21 References [1] Baxter K (ed.). Stockley s Drug Interactions (XML). London: Pharmaceutical Press, [2] [3] Porter MF. An algorithm for suffix stripping. Program 1980; 14(3):
22 Thank you! Questions and comments are most welcome at
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