Extraction of Adverse Drug Effects from Clinical Records E. ARAMAKI* Ph.D., Y. MIURA **, M. TONOIKE ** Ph.D., T. OHKUMA ** Ph.D., H. MASHUICHI ** Ph.D.,K.WAKI * Ph.D. M.D., K.OHE * Ph.D. M.D., * University of Tokyo, Japan ** Fuji Xerox, Japan Our material is Discharge Summary

Background The use of Electronic Health Records (EHR) in hospitals is increasing rapidly everywhere They contain much clinical information about a patient’s health BUT  Many Natural Language texts ! BUT  Many Natural Language texts ! Extracting clinical information from the reports is difficult because they are written in natural language

NLP based Adverse Effect Detecting System We are developing a NLP system that extracts medical information, especially Adverse Effect, form natural language parts INPUT – a medical text (discharge summary) OUTPUT – Date Time – Medication Event – Adverse Effect Event ≒ i2b2 Medication Challenge But our target focuses only on adverse effect Adverse Effect Relation (AER)

Why Adverse Effect Relations? Clinical trials usually target only a single drug. BUT: real patients sometimes take multiple medications, leading to a gap separating the clinical trials and the actual use of drugs For ensuring patient safety, it is extremely important to capturing a new/unknown AEs in the early stage.

DEMO is available on

副作用関係の推定 System Demo

ＣcＣc 副作用関係の推定 System Demo has no complications at the time of diagnosis 6/23-25 FOLFOX6 2 nd. 6/24, 25: moderate fever (38 ℃ ) again. a fever reducer…. Adverse Effect Medication Relation

The point of This Study (1) Preliminary Investigation: How much information actually exist? – We annotated adverse effect information in discharge summaries (2) NLP Challenge: Could the current NLP retrieve them? – We investigated the accuracy of with which the current technique could extract adverse effect information

Outline Introduction Preliminary Investigation – How much information actually exist in discharge summary? NLP Challenge Conclusions

Material & Method Material: 3,012 Japanese Discharge Summaries 3 humans annotated possible adverse effects due to the following 2 steps Lasix for hypertension is stopped due to his headache. Step 1 Event Annotation Step 2 Relation Annotation XML tag = Event XML attribute = Relation

Annotation Policy & Process We regard only MedDRA/J terms as the events. We regarded even a suspicion of an adverse effect as positive data. Entire data annotation is time-consuming → We split data into 2 sets SET-A (Event Rich parts): contains keywords such as Stop, Change, Adverse effect, Side effect SET-B: The other adverse effect terminology Full annotated Randomly sampled & annotated

14.5%×53.5% %×11.3% = 17.4% SET-B SET-A

Results of Preliminary Investigation About 17% discharge summaries contain adverse effect information. – Even considering that the result includes just a suspicion of effects, the summaries are a valuable resource on AE information. We can say that discharge summaries are suitable resources for our purpose.

Outline Introduction Preliminary Investigation NLP Challenge – Could the current NLP technique retrieve the AEs? Conclusions

Combination of 2 NLP Steps 2 NLP steps directly correspond to each annotation step Lasix for hyperpiesia is stopped due to the pain in the head. symptom Medication Adverse Effect Relation Event Annotation Relation Annotation ≒ Named Entity Recognition Task = Relation Extraction Task, which is one of the most hot NLP research topics.

Step1: Event Identification Machine Learning Method – CRF (Conditional Random Field) based Named Entity Recognition Feature – Lexicon (Stemming), POS, Dictionary based feature (MedDRA), window size=5 Material – SET-A Corpus with Event Annotations state-of-the-art method at i2b2 de-identification task Standard Feature Set

Step1: Result of Event Identification Result Summary Cat. of EventPrecisionRecallF-measure Medication Event AE Event All accuracies (P, R) >> 80 %, F>0.80, demonstrating the feasibility of our approach Considering that the corpus size is small (435 summaries), we can say that the event detection is an easy task

Step2: Relation Extraction Method Basic Approach ≒ Protein-Protein Interaction (PPI) task [BioNLP2009-shared Task] Example Lasix for hypertension is stopped due to his headache For each m (Medications) For each a (Adverse Effects) judge_it_has_rel (a, m) For each m (Medications) For each a (Adverse Effects) judge_it_has_rel (a, m) (1) judge_it_has_AER (Lasix, hypetension) (2) judge_it_has_AER (Lasix, headach)

(1) PTN-BASED: heuristic rules using a set-of- keyword & word distance..is on ACTOS but stopped for relief of the edema. n=1 keyword n=4 Judge_it_has_AER (m, a, keyword=stopped, windowsize5) (2) SVM-BASED: Machine learning approach – Feature: distance & words between two events ( medication & adverse effect) Two judgment methods See proceedings for detailed

Step2: Result of Relation Extraction PrecisionRecallF-measure PTN-BASED41.1%91.7% %62.3%0.598 SVM-BASED Both PTN & SVM accuracies are low (F<0.65) → the Relation extraction task is difficult! SVM accuracy is significant (p=0.05) lower than PTN (1) Corpus size is small (2) positive data << negative data Machine learning suffers from such small imbalanced data

Outline Introduction Preliminary Investigation NLP Challenge Discussions – (1) Overall Accuracy – (2) Controllable Performance – (3) Event Distribution Conclusions

Discussion (1/3) Overall Accuracy The overall accuracy is estimated by the combined accuracies of step1 & step2 Overall (= step1 × step2) Precision0.289 (=0.855 × × 0.390) Each NLP step is not perfect, so, the combination of such imperfect results leads to the low accuracy (especially many false positives; low precision) Recall (=0.802 × × 0.917)

Discussion (2/3) Performance is Controllable Precision & Recall curve in SVM The performance balance between recall & precision could be controlled High precision setting High recall setting That is a strong advantage of NLP

Discussion (3/3) Event Distribution We investigated the entire AE frequency for each medication category. distribution acquired from annotated real data distribution acquired from our system results AE freq. distribution of Drug #1

Discussion (3/3) AER Distribution Then, we checked the goodness of the fit test, which measures the similarity between two distributions Med. 1 Med. 2 Med. 3 Med. 4 Med. 5 Total P-value High p-value (p=0.011 > 0.01) indicates two distributions are similar.

Outline Introduction Preliminary Investigation NLP Challenge Discussions Conclusions

Conclusions (1/2) Preliminary Investigation: – About 17% discharge summaries contain adverse effect information. – We can say that discharge summary are suitable resources for AERs NLP Challenge: – Could NLP retrieve the AE information? – Difficult! Overall accuracy is low

Conclusions (2/2) BUT: 2 positive findings: (1) We can control the performance balance (2) Even the accuracy is low, the aggregation of the results is similar to the real distribution IN THE FUTURE: – A practical system using the above advantages – More acute method for relation extraction

Thank you Contact Info – Eiji ARAMAKI Ph.D. – University of Tokyo – –