The time window for the target data was set as the 14 years from 1995 to 2008. We collected 1,075 papers' titles and abstracts published in the journal and symposia of Korean Society of Medical Informatics. Abstract-free papers were excluded from the corpus to have 915 for analysis. For consistency, Korean titles and abstracts of 295 papers was translated into English through Google Translator Toolkit. Then, the English terms were corrected, which have different meanings from original ("화상진료시스템" ==> "Burn care system" --> "telemedicine system"), were Romanized as pronounced ("기록지"==> "girokji" --> "record"), and have not been marked as medical terminology ("검사" ==> "inspection" --> "lab test").

In a co-word analysis, the critical step is to create a list of terms that constitute the variables for analysis. Our variables were created from a combination of the sources including: 1) symposium topic lists; 2) author keywords and biomedical informatics keywords from Thomson Web of Science; and 3) MeSH descriptors. Research topics were collected from call for paper topics and session titles in all symposia. The collected terms were appended to the Vocabulary Manager of BTA. The Vocabulary Manager automatically erases duplicate terms and manages n-grams (up to 5-grams), and it allows users to load new vocabularies and input new terms (Figure 1).

Topics are difficult to represent with single words because they often have more than one meaning. In most cases, topics are appropriately described in multi-word phrases, which, especially in research domains, are much more interpretable [17]. In this study, we view a topic as a multi-word phrase rather than a single word. Before we proceeded to extract phrases and obtain precise variables, tokenized words' plural forms were singularized and their synonyms controlled. We adopted a less strict normalization strategy for words: tokenized words' plural forms were singularized (eg, records to record) and abbreviations were controlled with synonym lists (eg, HER = electronic health record). From paper corpora, we extracted n-gram terms (from 2-grams to 5-grams) as variables using BTA (total number of n-gram terms = 2,954). The most frequently occurring term was "information system" (term frequency = 533). After excluding the terms that occurred less than 5 times and inappropriate to be a variable (eg, "two type"), the term variables for analysis became 748.

Since not all terms may have the same importance in a document, the weight of each term was calculated by multiplying term frequencies (TF) by the inverse document frequency (IDF) for that term.

where f_i,j is the number of times the term i appears in the document j, Σ_kn_k,j is the total number of terms in the document d, N is the total number of documents, and n_i is the total number of documents containing the term i.

The co-occurrence analysis approach quantifies term co-occurrences in documents. It assumes that the more frequently two terms appear together in the same document, the sooner they will be identified as being closely related [18]. BTA generates a term co-occurrence frequency matrix (748 × 748), and then transforms the matrix into a cosine correlation matrix, where each cell indicates the relative closeness of each term pair with a 0-1 range. The cosine measure is defined as the cosine of the angle enclosed between two term vectors x and y:

The term-term closeness matrix was converted to a social network showing the binary relationships between any two terms. This network provides a useful medium for representing the topical structure of Korean medical informatics in a concise and intuitive manner. Pajek software was used for network visualization and analysis. The node size equals to the logarithm of the term frequency, and the thickness of the lines indicates the cosine value (closeness) between a pair of terms.

Some terms have links with many terms; their network of co-occurrences is quite extensive and occupies a central position in a field. To identify the important research topics in Korean medical informatics, authority and hub scores were calculated for each topic. In social network analysis, if a vertex points to many good authorities, it is a good hub. And if a vertex is pointed to by many good hubs, it is a good authority [19]. The authority scores and hub scores of topics are rendered as:

The authority score of a topic i (T_i) equals the sum of the hub scores of all topics (T_j) that point to it. The hub score (T_i) of a topic i (T_i) equals to the sum of the authority scores of all topics that it points to. Authority scores mutually reinforce hub scores. As shown in Table 1, we extracted the 50 most important topics in Korean medical informatics during the past 14 years (1995-2008). Table 2 shows that top 19 topics with high authority score occupy about 3.5% of 748 topics.

As shown in Figure 2, the 50 most important topics were grouped into 12 clusters: information system, decision support system, picture archiving and communication system, electronic health record, electronic medical record, XML, university hospital, healthcare, and so on. It is comparable to the findings of a study in global scale, in which physician order entry and practice guideline are one of the major topics as shown in Figure 3. It is also interesting that the information system group (upper right corner of Figure 2) is closely associated with nursing science topics such as nursing record, nursing process, nursing activity, nursing information, and so on.

To investigate newly emerging topics, for each 3-year period (2 years for 2007 and 2008), we calculated term frequencies and identified the topics which represented the lowest 10% in the low frequency group in the preceding period(s), and which also remained in the highest 10% (5% in the years 2007-2008) in the high frequency group in the following periods.

During the past 14 years (1995-1998), information system, medical record, hospital information, management system, hospital information system, health information, web based, and information technology have been occupying top 5% of Korean medical informatics research topics. Some of the newly emerging research topics during the years 1998-2000 are nursing informatics and electronic medical record, during the years 2001-2003 are consumer health. Electronic health record system, information extraction, and ubiquitous healthcare are newly popular topics during 2004-2006. During 2007-2008 oriental nursing, bioinformatics, ubiquitous computing, personal health device are some of newly emerging topics (Figures 4, 5, 6).