An Efficient Prediction Model for OTC Medicine Effect with the Package Inserts Information

Abstract In Japan, general public those who are not medical experts usually buy OTC medicine at a pharmacy, depending on their illness condition. In this case, it is difficult for them to consider how much the OTC medicine is effective for their symptom. The components of OTC medicine have been used as ethical medicines for a long period of time. This is because the efficacy and safety of ethical medicine have been confirmed before being employed as OTC medicine. The information of those confirmed medicines is described in package inserts, which is aimed for medical professionals. Therefore, it is difficult for general public to understand what the package insert describes in terms of medical effects. In this study, from the information which appears in the package inserts of prescription medicines, a method for estimating the effect of OTC medicine is investigated. Also, a method of estimating the effects of medicines without directly compared data is proposed, only by using the information of package inserts of ethical medicines.

[1]  香織 倉田,et al.  Pharmaceutical Markup Language(PML)を用いる医療用医薬品添付文書データベースの構築 , 2007 .

[2]  Michiko Ohkura,et al.  Analysis of Questionnaires Regarding Safety of Drug Use Application of Text Mining to Free Description Questionnaires , 2005 .

[3]  Takeshi Fukuda,et al.  Mining Structured Association Patterns from Databases , 2000, PAKDD.

[4]  Koji Yamamoto,et al.  Tendency discovery from incident report map generated by self organizing map and its development , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[5]  Xiaowei Xu,et al.  Mining FDA drug labels using an unsupervised learning technique - topic modeling , 2011, BMC Bioinformatics.

[6]  Michiko Ohkura,et al.  The Analysis of Questionnaires About Safety of Drug Use, the Application of Text Mining to Free Description Questionnaires , 2005 .

[7]  Michiko Ohkura,et al.  Analysis on Descriptions of Dosage Regimens in Package Inserts of Medicines , 2009, HCI.

[8]  Sophia Ananiadou,et al.  BioNLP: Proceedings of the 2012 Workshop on Biomedical Natural Language Processing , 2012 .

[9]  Makoto Nagao,et al.  Automatic Detection of Discourse Structure by Checking Surface Information in Sentences , 1994, COLING.

[10]  Michiko Ohkura,et al.  A Proposal of a Method to Extract Active Ingredient Names from Package Inserts , 2009, HCI.

[11]  Takashi Niwa,et al.  Investigating the Usefulness of a Prescription Checking System in Risk Management. , 2003 .

[12]  Jon Duke,et al.  A quantitative analysis of adverse events and "overwarning" in drug labeling. , 2011, Archives of internal medicine.

[13]  Masakazu Takahashi,et al.  A Study on Filtering of the Effect Range with the Package Insert of the Medicine , 2014, 2014 IEEE 38th International Computer Software and Applications Conference Workshops.

[14]  Kyo Kageura,et al.  Term Extraction Using Verb Co-occurrence , 2004, CompuTerm International Workshop On Computational Terminology.

[15]  Michiko Ohkura,et al.  A proposal for a drug information database and text templates for generating package inserts , 2013, Drug, healthcare and patient safety.

[16]  Sachio Hirokawa,et al.  Formal Concept Analysis of Medical Incident Reports , 2010, KES.

[17]  Hirotomo Aso,et al.  Evaluation of Methods to Extract Collocational Information from Corpus for Semantic Clustering of Japanese Polysemous Verbs , 1998 .

[18]  Michiko Ohkura,et al.  Analysis on descriptions of precautionary statements in package inserts of medicines , 2012, Drug, healthcare and patient safety.

[19]  Yuji Matsumoto,et al.  Applying Conditional Random Fields to Japanese Morphological Analysis , 2004, EMNLP.