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Applying a “Big Data” Literature System to Recommend Antihypertensive Drugs for Hypertension Patients with Diabetes Mellitus

Background The explosive increase in medical literature has changed therapeutic strategies, but it is challenging for physicians to keep up-to-date on the medical literature. Scientific literature data mining on a large-scale of can be used to refresh physician knowledge and better improve the quality of disease treatment. Material/Methods This paper reports on a reformulated version of a data mining method called MedRank, which is a network-based algorithm that ranks therapy for a target disease based on the MEDLINE literature database. MedRank algorithm input for this study was a clear definition of the disease model; the algorithm output was the accurate recommendation of antihypertensive drugs. Hypertension with diabetes mellitus was chosen as the input disease model. The ranking output of antihypertensive drugs are based on the Joint National Committee (JNC) guidelines, one through eight, and the publication dates, ≤1977, ≤1980, ≤1984, ≤1988, ≤1993, ≤1997, ≤2003, and ≤2013. The McNemar’s test was used to evaluate the efficacy of MedRank based on specific JNC guidelines. Results The ranking order of antihypertensive drugs changed with the date of the published literature, and the MedRank algorithm drug recommendations had excellent consistency with the JNC guidelines in 2013 (P=1.00 from McNemar’s test, Kappa=0.78, P=1.00). Moreover, the Kappa index increased over time. Sensitivity was better than specificity for MedRank; in addition, sensitivity was maintained at a high level, and specificity increased from 1997 to 2013. Conclusions The use of MedRank in ranking medical literature on hypertension with diabetes mellitus in our study suggests possible application in clinical practice; it is a potential method for supporting antihypertensive drug-prescription decisions.


Background
The vast body of medical literature continues to grow rapidly. MEDLINE is the premier bibliographic database of the world's largest medical library. There were over 22,000,000 references from 5,600 journals in MEDLINE in 2015. More than 869,000 new citations were added to MEDLINE in 2016. As the quantity of medical literature rapidly expands, keeping medical knowledge up-to-date has become a serious challenge for physicians. Thus, the ability to utilize the MEDLINE system effectively and rapidly is essential for medical professionals.
The explosion of data in the last decade is revolutionizing all aspects of human life. Advances in storage and analysis of healthcare data have offered both opportunities and challenges. Data mining is considered a promising tool that could inform personalized medicine and prescriptive analytics as well as clinical risk interventions [1]. In 2009, Google used data from billions of Google search queries to estimate the current level of weekly influenza activity in each region of the United States [2]. In addition to Google search data, a number of "big data" medical sources exist today, including electronic health records (EHR), genetic information, health insurance, registries, clinical trials, and literature databases [3]. One study detected thousands of associations between Mendelian and complex diseases by mining the medical records of over 110 million patients. Such work is meaningful for studies of the etiologies of rare diseases and also highlights the importance of data mining in a very large human population [4]. Data mining is also used for clinical pharmacology. Wu et al. established an ontology and a corpus of information to identified drug interactions by searching abstracts of publications in PubMed using text-mining techniques [5]. Network medicine is a data mining approach used to understand disease and discover therapeutics looking at molecules and molecular interactions [6]. Herskovic et al. applied graph-based ranking algorithm MedRank using concepts extracted from the text to identify the good indexing terms [7]. In 2013, Chen, Li and Han proposed that MedRank, a new network-based ranking algorithm, could be used to recommend medical treatments for a given disease based on data extracted from the MEDLINE system [8]. Thus, utilization of the increasing volume of medical data to explore accurate pathogenic research and provide therapeutic strategies by data mining is becoming a trend.
The innovation of data mining has made it possible to analyze and utilize mass publications. Network-based ranking, which use a graph structure, is a good tool for utilizing mass literature to discover new knowledge [9]. The concept was first referred to as PageRank. But PageRank was not applicable to medical ranking problems, because it was designed for one type only [10,11]. Chen et al. proposed using MedRank to recommend medical treatments for a given disease based upon the MEDLINE database [8]. MedRank uses the MEDLINE database to construct a medical information network [12] that is abstracted as a graph with referential relationships amongst different types of indirect objects extracted from MEDLINE. In their research, five types of diseases, including acquired immune deficiency syndrome, type 2 diabetes mellitus (DM2), amyotrophic lateral sclerosis (ALS), rheumatoid arthritis, and type B hepatitis, were analyzed by MedRank, and the output results was evaluated by physicians. The results demonstrated that the efficacy of MedRank was acceptable, but agreement with expert rankings for DM2 was worse compared with other diseases. This algorithm suggests that data mining may be a useful methodology for drug recommendations based on the medical literature.
Data mining professionals without medical backgrounds designed the formulas of the MedRank algorithm. Three key points about medical knowledge were ignored by MedRank algorithm. First, only simple and independent diseases without complications are suitable for the algorithm. Drug therapy strategies for DM2 patients are dependent on the personal characteristic related to the illness, such as age, obesity, insulin resistance, history of therapy, blood glucose levels, and other factors. Second, the drug outputs were not classified. Hypoglycemic agents were the most frequently recommended drugs, but the data included insulin, metformin, thiazolidinediones, and other agents. The results weren't specific to the drug category. Third, the weight of the publications, including time of publication and research type, was not considered. For these reasons, our medical team reformulated the MedRank algorithm. We made three major improvements in MedRank algorithm, including disease model input, drug recommendation output, and weight of the literature. Cardiovascular and cerebrovascular diseases are responsible for most deaths in the world [13]. As the major risk factor for cardiovascular disease, hypertension is prevalent throughout the world [14]. A large body of evidence has demonstrated that antihypertensive therapy prevents the complications of hypertension, such as stroke, heart failure, and myocardial infarction, and decreases the mortality rate from cardiovascular disease [15]. Given the rapid discovery and development of the antihypertensive pharmaceutical industry and an increasing amount of evidence from clinical studies, antihypertensive strategies are frequently updated. Thus, it is helpful to establish a real-time antihypertensive recommendation system for physicians, especially those specializing in non-cardiovascular disease and family physicians. Hypertension with diabetes mellitus was chosen as the target disease model due to the large number of patients with this diagnosis and the abundance of clinical studies. All types of individual antihypertensive drugs were ranked, and the results were compared with existing guidelines to evaluate the efficacy of our reformulated MedRank algorithm.
This study was an attempt to develop an advantageous data mining service for clinical strategies based on "big data" literature. The cooperation between data mining and medical groups may lead to potential successes in other medical domains.

Data source
The literature analyzed in this study was present in the MEDLINE database maintained by the United States National Library of Medicine (NLM), which consists of more than 22 million journal citations and abstracts. MEDLINE (2015) corpus can be acquired in XML format (109 GB) from http://www.nlm.nih.gov/ bsd/licensee/access/medline_pubmed.html. Permission to access the data was acquired by the Third Xiangya Hospital in China in June 2015. Each citation contains the bibliographical information of an article, such as article ID (PMID), title, author list, journal title, venue, publication type, and indexed Medical Subject Headings (MeSH) terms.

Search strategy
The MEDLINE database is indexed by NLM MeSH to produce a hierarchically structured medical thesaurus and to facilitate searching. MeSH is a comprehensive controlled vocabulary for the purpose of indexing journal articles and books in the life sciences, including the disease and drugs used in the current study. MeSH is widely used in medical areas such as the NLM catalog of book holdings and the ClinicalTrials.gov registry system. Therefore, a search strategy based on MeSH is responsible and efficient.

Improvement of MedRank and search details
Three major improvements were made to the current MedRank algorithm [8] as follows: disease model input, drug recommendation output, and weight of the literature.

Disease model input
Considering that it is easier to obtain a better recommendation ranking with a higher level of homogeneity in patients, a detailed diagnosis such as "hypertension combined with diabetes mellitus" but not "hypertension" was chosen as the input disease model. Therefore, the required criteria were as follows. First, the indexed MeSH terms must include "Humans" and "Hypertension. Second, an included article was required to be indexed by "Diabetes Mellitus". In the sub-analysis, hypertension with diabetic nephropathy was ranked by MedRank. "Kidney Failure, Chronic" or "Kidney Insufficiency, Chronic" was added as the input disease model, and the disease model also was selected based on different countries and areas, including the United States, Europe, Japan, and China according to MeSH terms. Supplementary Table 1 shows detailed information on the toponymy MeSH terms.

Drug recommendation output
As the therapeutic strategy for cardiovascular disease is comprehensive, the output should focus on only one kind of therapy. For example, patients suffering from myocardial infarction should receive aspirin, statins, angiotensin-converting enzyme inhibitors (ACEIs), calcium channel blockers (CCBs), nitroglycerin, and so on. It is not reasonable to rank all types of drugs recommended in the literature. Thus, only "antihypertensive drug" was ranked for "hypertension with diabetes mellitus" patients. In addition, the category of drugs in the past MedRank output was revised. Only the chemical drug name and category were defined as the output for the new reformulated MedRank. Therefore, articles on nine major categories of antihypertensive drugs were identified via MeSH indexed terms in the current study. The MeSH terms belonging to the nine major categories included 95 heading terms and 121 supplementary concepts. To build a model for antihypertensive treatment recommendations, only the index MeSH terms/concepts of the aforementioned antihypertensive agents that were labeled with the qualifiers "therapeutic use", "drug therapy", or "administration & dosage" were extracted for modeling.

Weight of the literature
Our model considered the publication date, publication type, institutions and design methods of literature. To ensure the quality of the included articles, the publication type information of an article was taken into consideration, which included the following indexed MeSH terms: "meta-analysis", "randomized controlled trial", "pragmatic clinical trial", "twin study", "controlled clinical trial", "observational study", "comparative study", and "case report". The type of publication, publication date, institutions and design methods were weighted, and the details are discussed below.
The edge weight of the graph was calculated based on 1) time factor (T), publication type (P) and institutions and design methods of clinical trials (I). For T information was extracted from the publication year of the article. If the article was published in the current year, weight=0; if it was published in the past 10 years, weight=1; otherwise, weight=-1. For P, according to the evidence grade of evidence-based medicine (EBM), good articles are associated with good publication types: "meta-analysis", "randomized controlled trial", "prospective studies" and "multicenter study", weight=1; otherwise, weight=0. For I, good articles use good clinical trial methods to control bias. For the list of prospective studies, random allocation, 116 matched-pair analysis, multicenter studies, double-blind meth-od\single-blind method, government financing, academies and institutes, government, nonprofit organizations, weight=1; otherwise, weight=0. The formula for the final weight was, weight=0.7+(T+P+I)/10.
From the viewpoint of EBM, we define "good articles" according to the evidence grade of EBM and the Cochrane Collaboration's risk of bias (RoB) tool. To control selection bias, good articles were indexed by "random allocation" and "prospective studies", rather than "loss to follow up" and "volunteers". To control information bias, good articles used good methods of "singleblind method" and "double-blind method", and were supported by government or reliable health institutes (e.g., "Financing, Government", "Academies and Institutes", "Government", and "Organizations, Nonprofit") rather than by drug manufacturers. To control confounding bias, good articles used good methods of "random allocation" and "matched-pair analysis".

Heterogeneous graph extraction
We constructed a star network graph ( Figure 1) of five types of objects extracted from the MEDLINE corpus, including Article, Author, Journal, Publication Type, and Antihypertensive Drug. Every MEDLINE citation record that satisfied the "Human", "Hypertension" and "Diabetes Mellitus" selection criteria was disease modeled as an instance of the Article type object on the graph. For each article, authors, publishing journal, type of publication, and main antihypertensive drugs discussed in the article were modeled as instances of the Author, Journal, Publication Type, and Antihypertensive Drug object types, respectively, with an edge of the Article object on the heterogeneous graph.

Ranking on the heterogeneous graph
We applied MedRank to the heterogeneous graph produced in the graph extraction phase, which included the Article, Author, Journal, Publication Type, and Antihypertensive Drug object types, with Article as the center type. The algorithm iteratively computes the ranks of objects of the same type until convergence occurs based on the following updating function: where tÎ{1,…,n-1}, n is a positive integer greater than 1; X_t denotes the object type and X_1 is the target type, i.e., the antihypertensive drug in our case; R_(X_1)is a vector for the rank of X_1-type objects; C is the center type, i.e., the article in our case; U is an |X_1 |×|X_1 | unit matrix; |X_1| is the total number of objects of type X_1; a determines the weight of U/|X_1|; W_AB is a weighted adjacency matrix of type A and type B objects that stores the weighted links between the objects; and D_AB^(-1) is a diagonal matrix in which the diagonal value is equivalent to the sum of the W_AB row for the purposes of row normalization.

Ranking results compared with Joint National Committee (JNC) guidelines
The Joint National Committee (JNC) guidelines are issued by American Heart Association and focus on detection, evaluation, and treatment of high blood pressure. JNC guidelines are one of the most authoritative hypertension guidelines and have played a leading role in the field of hypertension [16]. The efficacy of MedRank was compared with the JNC guidelines. The individual drugs were ranked based on the JNC guidelines one through eight, for publishing times £1977, £1980, £1984, £1988, £1993, £1997, £2003, and £2013 [16][17][18][19][20][21][22]. A ranking result indicated that the MedRank algorithm cannot analyze drug efficacy without support from a sufficiently credible or authoritative author and/or journal and/or a poor publication type. Thus, the drugs ranked in the top 20% according to MedRank were defined as preferred drugs, and those ranked in the bottom 20% with a score (not 0) were defined as contraindication drugs. The difference between countries was evaluated through a sub-analysis for publishing year £2013, and the results were compared with guidelines from different countries. Furthermore, each individual antihypertensive drug was divided into nine major antihypertensive drug categories: diuretics, adrenergic b-antagonists, adrenergic a-antagonists, adrenergic a, and b-antagonists, ACEIs, angiotensin receptor blockers (ARBs), CCBs, and ganglionic blockers. For the analysis, first the present antihypertensive category types were ranked based on the MedRank results. The ranking results were based on category at seven different time points, namely £1984, £1988, £1993, £1997, £2003, £2013 and £2015, are summarized.

Statistical analysis
McNemar's test was used to evaluate the efficacy of MedRank with guidelines. The consistency between MedRank and guidelines was determined based on the Kappa index. The sensitivity and specificity of the algorithm were also measured. All analyses were performed using the SPSS (version 17.0) statistical software.

Results
The characteristic of extracted literature The literature meeting the inclusion criterion of being published in the years £1977, £1980, £1984, £1988, £1993, £1997, £2003, £2013, and £2015 is shown in Table 1. The first manuscript that focused on hypertension with diabetes mellitus, was published in 1983. A total of 5,424 manuscripts, 17,632 authors, and 1,152 journals were extracted from the MEDLINE dataset based on the aforementioned inclusion criterion by October 2015. The total number of manuscripts clearly showed explosive growth. In addition, the number of authors and journals that focus on drug strategies for hypertension with diabetes mellitus increased.

The ranking results of MedRank
Although 21 papers met the inclusion criteria of £1984, only four papers mentioned a drug therapy strategy. Five individual antihypertensive drugs (clonidine, indapamide, atenolol, chlorthalidone, and metoprolol) were discussed in those four papers. Finally, metoprolol and clonidine were ranked number 1 and number 2. Indapamide, atenolol, and chlorthalidone were not entered into the ranking system because the authors were not authoritative. The whole individual antihypertensive drug ranking results determined by the MedRank algorithm at £1984, £1988, £1993, £1997, £2003, £2013, and £2015 are shown in Supplementary Tables 2-8. According to the recommendations of our model, 206 antihypertensive agents were included in our study, and the most influential antihypertensive drugs for hypertension with diabetes mellitus were ranked as follows: irbesartan, indapamide, amlodipine, losartan, candesartan, enalapril, olmesartan, hydrochlorothiazide, carvedilol, trandolapril, and ramipril by Oct 2015. The numbers of citations that supported the antihypertensive drugs irbesartan, indapamide, and amlodipine were 66 papers, 42 papers, and 52 papers, respectively. If necessary, MedRank can offer supporting literatures for individual drugs to allow further study (Supplementary Table 9). From the overall ranking results, the recommendation sequence was: ARBs, diuretics, CCBs ACEIs, adrenergic a and b-antagonists, adrenergic b-antagonists, adrenergic a antagonists, others and ganglionic blockers by Oct 2015. Moreover, the therapeutic strategy based on the category changed over time, as shown in Figure 2. As Figure 2 illustrates, ARBs, newly developed antihypertensive drugs, and diuretics, as well as traditional antihypertensive drugs, were shown to benefit hypertension with diabetes mellitus, based on the MedRank algorithm.

The efficacy evaluation of MedRank
The total number of individual drugs ranked were 2, 13, 27, 41, 48, 52, and 52 during the time periods £1984, £1988, £1993, £1997, £2003, £2013, and £2015, respectively. Finally, the results, including £1997, £2003, and £2013, were compared with JNC guidelines six through eight [16][17][18], because a sufficient number of drugs were available for analysis. According to JNC guideline six, ACEIs, a-blockers, calcium antagonists, and diuretics in low doses "are preferred" for diabetic hypertensive patients [16]. Thiazide diuretics, b-blockers, ACEIs, ARBs and CCBs were preferred according to JNC guideline seven [17] in a different subtype of diabetic hypertension. The initiation of a thiazide-type diuretic or ACEI or ARB or CCB, alone or in In the sub-analysis, irbesartan, losartan, olmesartan, benazepril, hydrochlorothiazide, and trandolapril were identified as ranked Ranking order £1997 £2003 £2013  [20] and 2014 Japanese guidelines [21] all recommend ACEI and ARB for hypertension with diabetes. ACEIs, CCB, and diuretics were on the ranking list based on Chinese literature. As was the case with the ESH/ESC and Japanese guidelines, Chinese guidelines [22] preferred ACEIs and ARBs, while CCBs and diuretics were considered second choices. The detailed results are shown in Table 3.

Discussion
To the best of our knowledge, MedRank, proposed and named by Chen et al., is the first network-based ranking algorithm to rank the most influential treatments based on the medical literature [8]. Here, we reformulated the MedRank algorithm from a medical point of view. Input was a clear definition of the disease, while output was the accurate classification of antihypertensive drugs. More importantly, the weight of the logical chain was redefined based on publication type in the current MedRank algorithm. The efficacy of the new MedRank was compared with the corresponding guidelines, including publication time and country. The consistency increased over time, and excellent consistency was found in the 2013 worldwide ranking results (P=1.00 from McNemar's test, Kappa=0.78, P=1.00). Sensitivity was better than specificity for MedRank; meanwhile, sensitivity maintained a high level, and specificity increased from 1997 to 2013. Moreover, the ranking results were consistent with the guidelines from the corresponding countries and reflected the differences among countries. Thus, the new MedRank algorithm is beneficial for the selection of therapeutic strategies based on medical literature, which is growing in number at an ever-increasing speed. It is advantageous to explore text mining of "big data" from the MEDLINE database through cooperation between medical and data mining groups, and this endeavor may lead to potential success in other medical domains.
The MedRank is a new network-based algorithm that ranks heterogeneous objects in a medical information network [8].
The network-based algorithm was first referred to as the PageRank [9]. The idea behind it was essentially the eigenvector centrality that finds those "center" or important nodes such that their neighbors are themselves important. The key idea of PageRank was the rank propagation through links, i.e., ranks were propagated from one webpage to another through the hyperlinks. The PageRank has not only been used in the search engine of Google, but also in identifying the spatial concentration of human movement [23]. Nie et al. proposed a new strategy PopRank that extends the PageRank model from the webpage level to the web object level and from ranking homogeneous objects to heterogeneous ones [24]. Web object belong to different types, such as article or people, and can be related to each other in different ways. Sun et al. extended the ranking mechanism of PopRank from the Web objects to a network of heterogeneous objects. They established the RankClus, a ranking-based clustering algorithm that ranks bitype objects in its own type within clusters [25]. The MedRank was the first work that introduced the network-based ranking approach to the medical domain that recommends treatments for a given disease based on data extracted from the MEDLINE system [8]. PageRank and PopRank are not applicable to the medical ranking problem, because PageRank is designed for one type only, i.e., webpage, and both of them are directly applicable only to directed graphs. MedRank's main difference from RankClus is that it is based on the available category labels and no clustering mechanism is involved. Data mining professionals without a medical background designed the formulas of the MedRank algorithm. There are three key points about medical knowledge that were ignored by the MedRank algorithm. Hence, we reformulated MedRank using text-mining techniques in MEDLINE to recommend the most influential treatment for a given disease; the results were specific to drug names and accounted for the level and time of the study.
The quantity of the literature that contained "hypertension combined with diabetes mellitus", and the numbers of categories and individual drugs dramatically increased from 1983 to 2015. This observation demonstrates the rapid discovery and development of the antihypertensive pharmaceutical industry, and the increasing number of clinical studies that were carried out. Thus, it is a big challenge for physicians to stay up-to-date with new medical knowledge, especially for noncardiovascular physicians and family physicians.
Adverse effects, especially on glucose homeostasis, lipid profiles, renal function, and benefits for cardiovascular events were evaluated in a large number of clinical studies. Based on new evidence, the therapeutic strategy for "diabetic hypertension" have changed from the JNC guidelines one through eight. Based on the JNC guidelines one through five, no preferred antihypertensive drugs were recommended, and thiazides and related sulfonamide diuretics and b-blockers should be used with caution for diabetic hypertension [26][27][28][29][30]. ACEIs, a-blockers, CCBs, and diuretics were preferred in JNC guideline six [16]. Thiazide diuretics, b-blockers, ACEs, ARBs, and CCBs were preferred in JNC guideline seven [17], and b-blockers were excluded as an initial therapy in JNC guideline eight [18]. The therapeutic strategy evaluated by the current MedRank also changed. It is attractive to recommend the therapy strategy for individual patient based on the personal characteristics. The alternative input of MedRank makes it possible to provide the precision medicine for individual patient. In our study, MedRank recommended ARBs and ACEIs for hypertension with diabetic nephropathy patients. Meanwhile, we attempted to analyze the subgroup results based on ethnic factors, but given that few indications of race were made in the literature, MedRank failed to rank drugs based on ethnic groups. More literature on various ethnic groups will make a MedRank ranking of drugs based on ethnic groups possible. In fact, more specific disease models, such as "female, black, diabetes mellitus, hypertension" could be used for input along with the increase of medical literature, and more accurate recommendations will be given by MedRank for an individual patient.
Furthermore, we also ranked the drugs based on literature from different countries or areas. In consideration of the quantity of literature and guidelines, analysis was not implemented in 1997 and 2003. In general, ranking results were consistent with the corresponding guidelines. The differences in therapeutic strategies among different countries reflected medication-taking behaviors and economies to some extent. On the whole, the prices of drugs on the Chinese rank list were cheaper than those in other countries. More importantly, recommendations of individual drugs may be more suitable for patients in a specific country, which is supported by the presence of relatively similar ethnic group and by the ease of obtaining a drug based on the country.
There are two limitations of this study MedRank algorithm. First, MedRank cannot recommend combinations of drugs. The combination of two drugs was viewed as two individual drugs for the purpose of ranking. The rational combination of antihypertensive drugs is a big challenge for MedRank. Second, assigning a weight based on publication time and type may not be suitable for other disease models. Thus, we need to adjust and test the assigned weight according to different disease models. The development of MedRank is a process that improves step by step. Advances in data mining and data sources will continue improve it.

Conclusions
In summary, we improved on an algorithm based on MEDLINE literature searching, named MedRank, using a data mining group. The proposed algorithm was evaluated according to different guidelines, including different countries and publication time. It has been shown that MedRank is effective and efficient. MedRank research suggests the possibility of establishing a real-time recommendation system for physicians to guide their clinical practice decisions for individual patients.
For future research, we will extend this network-based ranking approach to other medical domains. Investigations into ranking medical treatments based on EHR databases, medical literature, genetic information, and health insurance will also be considered.

Conflicts of interest
None.

Supplementary Tables
Supplementary Table 1