Development of ICD-10-TM ontology for a semi-automated morbidity coding system in Thailand.

OBJECTIVES: The International Classification of Diseases and Related Health Problems, 10th Revision, Thai Modification (ICD-10-TM) ontology is a knowledge base created from the Thai modification of the World Health Organization International Classification of Diseases and Related Health Problems, 10th Revision. The objectives of this research were to develop the ICD-10-TM ontology as a knowledge base for use in a semi-automated ICD coding system and to test the usability of this system. METHODS: ICD concepts and relations were identified from a tabular list and alphabetical indexes. An ICD-10-TM ontology was defined in the resource description framework (RDF), notation-3 (N3) format. All ICD-10-TM contents available as Microsoft Word documents were transformed into N3 format using Python scripts. Final RDF files were validated by ICD experts. The ontology was implemented as a knowledge base by using a novel semi-automated ICD coding system. Evaluation of usability was performed by a survey of forty volunteer users. RESULTS: The ICD-10-TM ontology consists of two main knowledge bases (a tabular list knowledge base and an index knowledge base) containing a total of 309,985 concepts and 162,092 relations. The tabular list knowledge base can be divided into an upper level ontology, which defines hierarchical relationships between 22 ICD chapters, and a lower level ontology which defines relations between chapters, blocks, categories, rubrics and basic elements (include, exclude, synonym etc.) of the ICD tabular list. The index knowledge base describes relations between keywords, modifiers in general format and a table format of the ICD index. In this research, the creation of an ICD index ontology revealed interesting findings on problems with the current ICD index structure. One problem with the current structure is that it defines conditions that complicate pregnancy and perinatal conditions on the same hierarchical level as organ system diseases. This could mislead a coding algorithm into a wrong selection of ICD code. To prevent these coding errors by an algorithm, the ICD-10-TM index structure was modified by raising conditions complicating pregnancy and perinatal conditions into a higher hierarchical level of the index knowledge base. The modified ICD-10-TM ontology was implemented as a knowledge base in semi-automated ICD-10-TM coding software. A survey of users of the software revealed a high percentage of correct results obtained from ontology searches (>95%) and user satisfaction on the usability of the ontology. CONCLUSION: The ICD-10-TM ontology is the first ICD-10 ontology with a comprehensive description of all concepts and relations in an ICD-10-TM tabular list and alphabetical index. A researcher developing an automated ICD coding system should be aware of The ICD index structure and the complexity of coding processes. These coding systems are not a word matching process. ICD-10 ontology should be used as a knowledge base in The ICD coding software. It can be used to facilitate successful implementation of ICD in developing countries, especially in those countries which do not have an adequate number of competent ICD coders.

Survey on medical records and EHR in Asia-Pacific region: languages, purposes, IDs and regulations.

OBJECTIVES: To clarify health record background information in the Asia-Pacific region, for planning and evaluation of medical information systems. METHODS: The survey was carried out in the summer of 2009. Of the 14 APAMI (Asia-Pacific Association for Medical Informatics) delegates 12 responded which were Australia, China, Hong Kong, India, Indonesia, Japan, Korea, New Zealand, the Philippines, Singapore, Thailand, and Taiwan. RESULTS: English is used for records and education in Australia, Hong Kong, India, New Zealand, the Philippines, Singapore and Taiwan. Most of the countries/regions are British Commonwealth. Nine out of 12 delegates responded that the second purpose of medical records was for the billing of medical services. Seven out of nine responders to this question answered that the second purpose of EHR (Electronic Health Records) was healthcare cost cutting. In Singapore, a versatile resident ID is used which can be applied to a variety of uses. Seven other regions have resident IDs which are used for a varying range of purposes. Regarding healthcare ID, resident ID is simply used as healthcare ID in Hong Kong, Singapore and Thailand. In most cases, disclosure of medical data with patient's name identified is allowed only for the purpose of disease control within a legal framework and for disclosure to the patient and referred doctors. Secondary use of medical information with the patient's identification anonymized is usually allowed in particular cases for specific purposes. CONCLUSION: This survey on the health record background information has yielded the above mentioned results. This information contributes to the planning and evaluation of medical information systems in the Asia-Pacific region.

Lessons learned from data mining of WHO mortality database.

OBJECTIVES: The objectives of this research were to test the ability of classification algorithms to predict the cause of death in the mortality data with unknown causes, to find association between common causes of death, to identify groups of countries based on their common causes of death, and to extract knowledge gained from data mining of the World Health Organization mortality database. METHODS: The WEKA software version 3.5.3 was used for classification, clustering and association analysis of the World Health Organization mortality database which contained 1,109,537 records. Three major steps were performed: Step 1 - preprocessing of data to convert all records into suitable formats for each type of analysis algorithm; Step 2 - analyzing data using the C4.5 decision tree and Naïve Bayes classification algorithm, K-means clustering algorithm and Apriori association analysis algorithm; Step 3 - interpretation of results and hypothesis testing after clustering analysis. RESULTS: Using a C4.5 decision tree classifier to predict cause of death, we obtained 440 leaf nodes that correctly classify death instances with an accuracy of 40.06%. Naïve Bayes classification algorithm calculated probability of death from each disease that correctly classify death instances with an accuracy of 28.13%. K means clustering divided the data into four clusters with 189, 59, 65, 144 country-years in each cluster. A Chi-square was used to test discriminate disease differences found in each cluster which had different diseases as predominant causes of death. Apriori association analysis produced association rules of linkage among cancer of the lung, hypertension and cerebrovascular diseases. These were found in the top five leading causes of death with 99-100% confidence level. CONCLUSION: Classification tools produced the poorest results in predicting cause of death. Given the inadequacy of variables in the WHO database, creation of a classification model to predict specific cause of death was impossible. Clustering and association tools yielded interesting results that could be used to identify new areas of interest in mortality data analysis. This can be used in data mining analysis to help solve some quality problems in mortality data.