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Exploring Diseases and Syndromes in Neurology Case Reports from 1955 to 2017 with Text Mining

Background: A large number of neurology case reports have been published, but it is a challenging task for human medical experts to explore all of these publications. Text mining offers a computational approach to investigate neurology literature and capture meaningful patterns. The overarching goal of this study is to provide a new perspective on case reports of neurological disease and syndrome analysis over the last six decades using text mining. Methods: We extracted diseases and syndromes (DsSs) from more than 65,000 neurology case reports from 66 journals in PubMed over the last six decades from 1955 to 2017. Text mining was applied to reports on the detected DsSs to investigate high-frequency DsSs, categorize them, and explore the linear trends over the 63-year time frame. Results: The text mining methods explored high-frequency neurologic DsSs and their trends and the relationships between them from 1955 to 2017. We detected more than 18,000 unique DsSs and found 10 categories of neurologic DsSs. While the trend analysis showed the increasing trends in the case reports for top-10 high-frequency DsSs, the categories had mixed trends. Conclusion: Our study provided new insights into the application of text mining methods to investigate DsSs in a large number of medical case reports that occur over several decades. The proposed approach can be used to provide a macro level analysis of medical literature by discovering interesting patterns and tracking them over several years to help physicians explore these case reports more efficiently.


Introduction
Medical publications have been considered as primary sources to report findings in different fields such as neurology. The first line for reporting clinical evidence has been in medical case applications. The goal of topic modeling is to detect and group related words for the purpose of organizing and understanding documents in a corpus. This type of computational analysis is an effective and efficient approach to get a macro level view of medical case reports. Analysis of case report entities such as DsSs can provide a granular perspective of the case report studies. To fill the gap in the related literature landscape, we investigated the DsSs in the neurology case reports.
In the present study, we applied text mining and trend analysis the neurology case reports to (1) investigate high-frequency DsSs; (2) detect and categorize DsSs; and (3) explore the overall and yearly trends of the high frequency neurologic DsSs and their categories during this research time frame. This study has research and educational applications for the experts who are interested in exploring neurological DsSs and their relationships in medical case reports.
The contributions of this paper are three-fold. First, the proposed framework offers a fully functional and automated approach to investigate a large number of the existing medical case reports and their overall trends during six decades. Research managers and policymakers can utilize our approach to understand the landscape of the neurology case reports. Secondly, the objective nature and the publicly available data of this research makes it easier to replicate the results using the shared data. Thirdly, this is the first research that finds the interactions between DsSs and discloses the patterns in a dataset containing rare medical cases.

Methods
Our approach uses text mining for disclosing the main themes of neurology case reports. The present study included five phases: data collection, disease and syndrome extraction, frequency analysis, relationship detection and analysis, and trend exploration ( Figure 1).

Disease and Syndrome Extraction
Frequency Analysis

Relationship
Detection and Analysis Trend Exploration

Data Collection
Data collection in this research was carried out in two steps. In the first step, we retrieved a list of top 200 neurology journals based on impact factor indicator from the Scimago Journal & Country Rank website 1 in April 2018. In the second step, we retrieved the case reports based on the journal name indexed in the PubMed website. For example, we used "case reports [Publication Type] AND lancet neurol[Journal]" query to retrieved case reports in The Lancet Neurology. Of the 200 top neurology journals, we focused on 66 journals that published at least 50 case reports, defining them as the highly active journals for publishing case reports (Appendix A). The collected data is available at https://github.com/amir-karami/MedicalCaseReport-Diseases.

Disease and Syndrome Extraction
In this step, we used PubTator 1 developed by the National Center for Biotechnology Information (NCBI) to extract and annotate the collected case reports [16]. The PubTator service can annotate diseases, species, chemicals, genes, and mutations in the PubMed documents [17]. Figure  2 shows an example of entity detection using PubTator including title, abstract, gene, chemical, disease, and mutation. This case describes a patient having breast cancer along with gene, chemical, and mutation information. For this study, we considered the disease entity that covers both disease and syndrome (DS).
29702197|t|Mutations in the estrogen receptor alpha hormone binding domain promote stem cell phenotype through notch activation in breast cancer cell lines. 29702197|a|The detection of recurrent mutations affecting the hormone binding domain (HBD) of estrogen receptor alpha (ERa/ESR1) in endocrine therapy-resistant and metastatic breast cancers has prompted interest in functional characterization of these genetic alterations. Here, we explored the role of HBD-ESR1 mutations in influencing the behavior of breast cancer stem cells (BCSCs), using various BC cell lines stably expressing wild-type or mutant (Y537 N, Y537S, D538G) ERa. Compared to WT-ERa clones, mutant cells showed increased CD44+/CD24-ratio, mRNA levels of stemness genes, Mammosphere Forming Efficiency (MFE), Self-Renewal and migratory capabilities. Mutant clones exhibited high expression of NOTCH receptors/ligands/target genes and blockade of NOTCH signaling reduced MFE and migratory potential. Mutant BCSC activity was dependent on ERa phosphorylation at serine 118, since its inhibition decreased MFE and NOTCH4 activation only in mutant cells. Collectively, we demonstrate that the expression of HBD-ESR1 mutations may drive BC cells to acquire stem cell traits through ER/NOTCH4 interplay. We propose the early detection of HBD-ESR1 mutations as a challenge in precision medicine strategy, suggesting the development of tailored-approaches (i.e. NOTCH inhibitors) to prevent disease development and metastatic spread in BC mutant-positive patients.

Frequency Analysis
Frequency analysis provides overall information about the detected DsSs [18]. Exploring highfrequency DsSs with bar chart and word cloud is a starting point for content analysis. Word cloud analysis visually summarizes text analysis and provides an overall view of the data in a corpus. This method presents the frequency of words in a corpus where the word size is proportional to the frequency of the word within the corpus-the larger size, the higher frequency. This approach has been used for a variety of applications, such as opinion mining [19]. Using word clouds to explore high-frequency DsSc reveals the important DsSc in the case reports over the past decades.

Relationship Detection and Analysis
Next we turned our attention to detect relationships between DsSs. The goal of this analysis was to find DsSs that were discussed together in multiple case reports. There are two major approaches in content analysis for detecting the relationships between words in a corpus: cooccurrence analysis and the afore-mentioned topic modeling [20]. Previous studies have shown that topic modeling shows a better performance than co-occurrence analysis for large corpora [21]. Topic modeling has been applied on both short-length documents such as tweets and long-length documents like research papers [22,23]. Among different topic models, latent Dirichlet allocation (LDA) is the most popular topic model and also has demonstrated better performance than cooccurrence analysis with respect to word clustering [24,25]. According to the literature, LDA is a valid and widely used model with more than 25,000 citations in Google Scholar 1 for discovering categories of words in a corpus. LDA is a generative probabilistic model for categorizing the words that occur together in a corpus [26,27]. For example in Figure 3, LDA assigns "gene," "dna," and "genetic" into the same category.
LDA has been utilized for health applications such as diet, diabetes, exercise, and obesity [28,29,30], and LGBT health issues [31,32], and non-health applications such as business and organizations [33,34,35], spam detection [36], disaster management [37], and politics [38,39]. There are some work investigating related studies in medical and health domains using LDA such as exploring the literature of depressive disorders [17], biomedical literature [40,18], and adolescent substance use and depression [41].  [42] To the best of our knowledge, this study is the first research uses LDA to analyze medical case reports. LDA assumes that there is an exchange between words and documents in a corpus [27]. In this research, we represent each case report (document) with the embedded DsSs (words) and don't consider other words such as title and abstract in the case report. Based on the LDA assumption, the neurology case reports are represented by categories of DsSs in our dataset. Another assumption of LDA is that a category, which is a group of related DsSs, has a different probability of occurrence for each of the case reports. This assumption can help to measure the weight of categories in each case report. In summary, LDA identifies the relationship between case reports and the categories, P(Category|CaseReport), and categories and DsSs, P(DS|Category).
For n case reports, m DsSs, and t categories, the outputs of LDA were: the probability of each of the DsSs per each category or P(DS i |C k ) and probability of each of the categories per each case report or P(C k |CR j ): The top DsSs in each category, based on the order of P(DS i |C k ), were used to represent the categories. We also used P(C k |CR j ) to find the Significance of each Category, SC(C k ). For an effective comparison between the categories, SC(C k ) was normalized: If N SC(C x ) > N SC(C y ), it means that researchers discussed the diseases in category x more than the ones in category y. N SC(C k ) can also help to find the weight of each category for each year and all the years.

Trend Exploration
We explored the DsSs trends using a linear trend model across six decades. We used the frequency of DsSs and the categories of DsSs in the case reports within each year to detect and identify increasing and decreasing trends in research interest around the DsSs [17]. We used the lm function in R to measure p − value and slope for each of the DsSs and categories. The p − value determines whether a trend is significant and the slope shows whether a trend is increasing and decreasing. The trend shows increasing or decreasing importance of an entity. The slope shows the intensities of the trends, useful in comparing them.

Results
The data collection step has provided 65,525 case reports for 63 years, from 1955 to 2017, in the MEDLINE format. Figure 4 shows the number of published neurology case reports per year over more than six decades along with the trend line. The linear trend was significant (p < 0.05) and had a positive value (43.95) for the slope indicating an increasing trend. PubTator detected DsSs in 93% (61,149 out of 65,625) of the case reports. We found 258,487 DsSs among which 18,081 DsSs were unique. Word frequency analysis showed that 95% of DsSs occurred fewer than 50 times. With a median of 3 and an average of 14.3, the frequency of DsSs was between 2 (for 6,675 of the DsSs) and 4,319 (for tumor). Figure 5 is in line with Zipf's law and illustrates the position of the top 50 words among the top 1000 high-frequency words. Zipf's law states that the frequency of a word in a corpus is inversely proportional to its frequency rank [43].  Figures 6 and 7 illustrate the frequency of the top 10 high-frequency DsSs and the word cloud for the top 50 DsSs. These two figures indicate that that tumor, seizures, and headache are the most frequent DsSs, characterizing 4339, 3190, and 2566 case reports, respectively. We didn't apply any stemming techniques in the identification of categories in case reports. For example, we didn't convert a plural form such as tumors to the singular one to distinguish the case reports studying multiple tumors from the ones investigating a single tumor. Figure 8 shows overall trends for the top 10 DsSs. Tumor was the most frequently mentioned   Figure 9 and Table 1 illustrate the linear trend information of the top 10 DsSs from 1955 to 2017. The average number of studies per year for the top 10 DsSs was more than 25. The linear trend of the top 10 DsSs was significant (p < 0.05) with a positive slope indicating increasing trends. Based on the slope values, we expected to see the same ranking and patterns for 9 out of 10 DsSs in the following years. The only exception was that the slope value of hydrocephalus was less than the slope value of stroke. This means that we could see more stroke-related case reports than hydrocephalus-related ones.  To select an optimal number of categories, we applied a density-based method that assumes that the best performance of LDA is at the minimum average cosine distance of categories [44]. Applying the ldatuning R package 1 on the number of categories from 5 to 100 increased by 5 has shown the appropriate number of categories at 10. The Mallet implementation, which is a Java programming language for text mining purposes [45], was applied on the DsSs with 10 categories and 1000 iterations.
Then, we evaluated the robustness of LDA on using the log-likelihood for five sets of 1000 iterations. LDA was trained on the case reports having multiple DsSs with 10 categories for five times and reached its maximum value after 500 iterations ( Figure 10). We compared the five iterations using t-test and found that there isn't a significant difference (p − value > 0.05) between the five iterations with respect to mean and standard deviation. This figure and the t-test show that there isn't a significant difference in log-likelihood convergence over different iterations. Figure 11 shows the distribution of categories over the case reports and each line represents a category. This figure indicates that the 10 categories have a high probability for less than 2000 case reports. Table 2 shows the 10 categories. These categories show the possible relationships between the top 5 DsSs in each of the categories. We also explored the weight of categories and found that categories 3, 5, and 7 are the most discussed ones; however, the zero value of the standard deviation of the weights ranging from 0.09 to 0.11 shows no significant difference among the weights ( Figure 12). Therefore, we can assume that the categories have similar weight.  We investigated the yearly trends of categories for six decades from 1955 to 2017 ( Figure  13). The stream figure visualizes the evolution of categories based on the probability of the 10 categories in each of the years. Each color represents a category that horizontally flows from left to right. In Table 3 and Figure 14, although p − value < 0.05 shows that the trends for categories 3, 8, and 10 are significant.

Discussion
Due to the lack of a macro level analysis and the high volume of medical case reports, there is a need to utilize computational methods for the purpose of analyzing a large number of case reports. In this study we investigated DsSs in the neurology case reports to recognize patterns and provide additional insights using text mining. Analysis of case report entities such as DsSs provides a "bird's eye view" to understand the importance of the entities and their interactions.
Exploring the patterns of DsSs will provide vital insights into many aspects as follows. First, the high-frequency DsSs suggest their high importance to medical experts. Second, the DsSs in a category that have not been studied together can be considered as useful hypotheses for further investigation. Third, the detected trends show research streams. Fourth, sharp changes in the detected trends provide an overview of past studies and insights for future studies.
Through frequency analysis, we found high-frequency DsSs such as tumor(s), seizure(s), headache, aneurysm, pain, epilepsy, hydrocephalus, and stroke. This analysis shows the importance of these DsSs for researchers. Relationship detection and analysis generated 10 categories of DsSs. For example, the first category shows the case reports mentioning two or more of the represented DsSs, such as a paper reporting headache, hydrocephalus, pain, and migraine [46]. Examples of other case reports for each of the categories can be found in Table 4.
The stream river figure has visualized how the ten categories have changed over more than 60 years. The width variation reflects the strength of the categories at each time slice. For example, the DsSs in category 1 were considered more in 1955 than 1970.

Conclusion
Medical experts have been contributing continuously to neurology case reports. Due to the rapid growth in the number of case reports and their overall large numbers, there is a need for a systematic analysis approach to help researchers, policymakers, and practitioners to have a largescale understanding of the case reports. This study achieved this macro level analysis by applying text-mining and trend analysis methods on thousands neurology case reports to detect and explore high frequency DsSs and their categories from 1955 to 2017.
The results show the ability of text mining methods to investigate DsSs in a large number of medical case reports and explore them for several decades to disclose interesting patterns. Our methodology demonstrates the usefulness of computational linguistics methods to study DsSs and their trends in medical case reports. The proposed approach can be used to provide a macro level analysis of medical literature by discovering interesting patterns and tracking them in specific time frames. The results illustrated the application of text mining for detecting DsSs and their relationships, and disclosing their trends in a large number of neurology case reports. We believe that this paper proposes a systematic approach for analysis of case reports in not only neurology but also other medical fields.
This study has some limitations. First, the collected case reports were only from the journals among the top 200 neurology journals in the Scimago Journal & Country Rank website. In our future work, we plan to incorporate additional neurology case reports, investigate other medical research areas, and compare them. Second, we only analyzed DsSs; however, there are other entities in the case reports. Therefore, we will investigate DsSs along with chemicals, mutations, genes, and species based on time and location variables.

Conflict of interest
The authors state that they have no conflict of interest.