JAMIA Open, 0(0), 2018, 1–7 doi: 10.1093/jamiaopen/ooy001 Research and Applications Research and Applications Understanding the research landscape of major depressive disorder via literature mining: an entity-level analysis of PubMed data from 1948 to 2017 1,2 1 1 1 Yongjun Zhu , Min-Hyung Kim , Samprit Banerjee , Joseph Deferio , 3 1 George S Alexopoulos and Jyotishman Pathak 1 2 Department of Healthcare Policy and Research, Weill Cornell Medicine, Cornell University, New York, New York, USA, Department of Library and Information Science, Sungkyunkwan University, Seoul, South Korea and Department of Psychiatry, Weill Cornell Medicine, Cornell University, New York, New York, USA Corresponding Author: Jyotishman Pathak, PhD, Department of Healthcare Policy and Research, Weill Cornell Medicine, Cornell University, 425 East 61st Street, Suite 301, New York, New York 10065, USA (firstname.lastname@example.org) Received 23 December 2017; Revised 22 January 2018; Accepted 24 January 2018 ABSTRACT Objective: To analyze literature-based data from PubMed to identify diseases and medications that have fre- quently been studied with major depressive disorder (MDD). Materials and methods: Abstracts of 23 799 research articles about MDD that have been published since 1948 till 2017 were analyzed using data and text mining approaches. Methods such as information extraction, fre- quent pattern mining, regression, and burst detection were used to explore diseases and medications that have been associated with MDD. Results: In addition to many mental disorders and antidepressants, we identiﬁed several nonmental health dis- eases and nonpsychotropic medications that have frequently been studied with MDD. Our results suggest that: (1) MDD has been studied with disorders such as Pain, Diabetes Mellitus, Wounds and Injuries, Hypertension, and Cardiovascular Diseases; (2) medications such as Hydrocortisone, Dexamethasone, Ketamine, and Lithium have been studied in terms of their side effects and off-label uses; (3) the relationships between nonmental dis- orders and MDD have gained increased attention from the scientiﬁc community; and (4) the bursts of Diabetes Mellitus and Cardiovascular Diseases explain the psychiatric and/or depression screening recommended by au- thoritative associations during the periods of the bursts. Discussion and conclusion: This study summarized and presented an overview of the previous MDD research in terms of diseases and medications that are highly relevant to MDD. The reported results can potentially facili- tate hypothesis generation for future studies. The approaches proposed in the study can be used to better un- derstand the progress and advance of the ﬁeld. Key words: literature mining, frequent pattern mining, temporal analysis, research landscape, major depressive disorder increase from being ranked 15th in the same study conducted in BACKGROUND AND SIGNIFICANCE 1990. MDD is also known as a risk factor for suicide and ischemic According to the World Health Organization, depression affects the heart disease. Scientific publications have been a primary venue for lives of more than 350 million people globally. In the 2010 Global researchers to discuss and report many important findings on MDD. Burden of Disease study, major depressive disorder (MDD) was However, with increasing rates of publication of scientific literature, ranked 11th among the 291 diseases and injuries, which was a 37% it is almost impossible to inventory and understand all the articles V The Author(s) 2018. Published by Oxford University Press on behalf of the American Medical Informatics Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact email@example.com 1 Downloaded from https://academic.oup.com/jamiaopen/advance-article-abstract/doi/10.1093/jamiaopen/ooy001/4959056 by Ed 'DeepDyve' Gillespie user on 08 June 2018 2 JAMIA Open, 2018, Vol. 0, No. 0 relevant to the disease. This requires us to take a new approach in Entity annotation and extraction addition to the traditional way of reviewing scientific literature PubTator developed by the National Center for Biotechnology manually to better understand the disease. Information (NCBI) was used to annotate the 23 799 PubMed cita- Bibliometric analysis of scientific papers can be used as an effec- tions. The tool annotates species, diseases, chemicals, genes, and tive complementary method to get a “bird’s eye view” of previous mutations described in titles and abstracts of the PubMed citations. studies. Analysis of biomedical entities such as diseases and medi- The tool also normalizes different names that denote the same con- cations would give us a granular understanding of the MDD studies cept to unified ID systems (eg MeSH IDs for diseases). We extract that have been conducted so far. For example, by investigating dis- annotated entities from the free text and replace their names with eases and medications that have been frequently studied with MDD, MeSH terms. For each citation, MeSH terms are stored in a set to re- we can understand their interactions with MDD. To fill the gap, in move any duplicates. For example, if a set includes either this study, we aim to analyze MDD research publications from the “Depressive Disorder” or “Depressive Disorder, Major,” which are perspective of biomedical entities to better understand the research MeSH terms, we consider the citation as relevant and include it in landscape of MDD. our analysis. This 2-step filtering (MeSH terms-based search and In particular, the goal of this study is to identify important diseases Named Entity Recognition-based filtering) guarantees that the and medications that have frequently been studied with MDD and un- resulting citation set that we analyze is highly relevant to MDD. In derstand how these diseases and medications have been studied over the study, we focus on co-occurrence of diseases and medications time. Specifically, we intend to investigate (1) the most frequently with MDD at the citation-level. For example, if a medication co- studied diseases and medications; (2) diseases and medications whose occurs with MDD in the abstract of an article, we assume that there overall trends in the scientific literature during the explored period are is a direct or indirect relationship between the two. This assumption increasing or decreasing; and (3) diseases and medications that have is reasonable because entities are studied in the context of MDD. shown sharp increases in the frequency of mention and investigation in the scientific literature during a specific time period. Exploring these Top entities issues has vital importance, and the outcomes will allow us to gain Entities that frequently co-occur with MDD imply their higher rele- insights in many aspects as follows. (1) Diseases (or medications) and vance to MDD than other entities that do not. By exploring entities disease (or medication) sets that have been frequently mentioned with based on the frequency of co-occurrence with MDD, we can identify MDD suggest their high relevance to MDD. If their relationships with important entities that have been actively studied with MDD in the MDD have not been clinically proven, these candidate relationships past 70 years. The number of articles that discuss an entity together can serve as useful hypotheses for researchers to further investigate. with MDD is used to represent the entity’s frequency. The frequency (2) Trends tell us entities that have been gaining increasing or decreas- is a simple, yet meaningful indicator that shows entities’ overall re- ing attention in the research community, which may suggest their in- latedness with MDD in the scientific literature. creasing or decreasing importance to the study of MDD. These trends help investigators understand research streams and emerging research Frequent entity sets problems. (3) A sharp increase in the frequency of disease (or medica- Some entities co-occur frequently with MDD as a set. This implies tion) discussion during a certain time period may suggest a series of that there may be associations among the entities and the co- new findings and warrant further systematic investigation. It can serve occurring patterns may signify important research contexts that as a clue to pay attention to the disease (medication) that might have should be examined in greater detail. For example, if diabetes melli- been overlooked previously. Overall, through a systematic analysis of tus, cardiovascular diseases, and MDD frequently co-occur together, MDD studies, we not only look back on previous studies to get an it may suggest that the 2 diseases interact with each other within the overview but are also able to gain insights for future research. context of MDD. In our study, we applied a commonly used fre- quent pattern mining algorithm called FP-growth to generate a list of frequent disease sets (ie sets of diseases that co-occur frequently with MDD) and another list of frequent medication sets, respec- MATERIALS AND METHODS tively. A second-step was applied to the frequent disease sets, where for each set, for a given disease, we remove it from the set if the set In the following, we use an entity to denote either a disease or a also includes another disease that is a subconcept of the given dis- medication and an entity set as a term for a group of diseases or ease. We use MeSH Tree Structures to determine whether a given medications. We use entity-level analysis to denote the analysis of disease is a subconcept of another or not. For example, if a frequent publication data from the perspective of biomedical concepts such disease set includes Mental Disorders (MeSH Tree: F03), Anxiety as diseases, medications, genes, etc. A linear trend and a burst of an Disorder (MeSH Tree: F03.080), and Personality Disorders (MeSH entity are used to describe the entity’s overall (the whole period) and Tree: F03.675), we remove Mental Disorders from the set because partial (a specific period) trends of being studied in the literature. there are 2 subconcepts of it within the same set. We perform this operation iteratively to ensure that every set includes only the most granular concepts within a branch of the MeSH Tree. This approach Publication data enables each frequent set to remain as specific as possible. We used “(humans [MeSH Terms]) AND depressive disorder, major [MeSH Terms]” as the query to search and download relevant Linear trends of entities PubMed citations. The use of MeSH terms guarantees the retrieval Throughout the explored time span, entities have trends of frequen- of only highly relevant studies by effectively filtering studies that are cies of having been discussed in the literature. Overall, we can iden- not about MDD but mention the term in the text. As of August 10, tify entity trends, whether increasing or decreasing, by using the 2017, we retrieved and downloaded 23 799 PubMed citations with linear trend model (with least squares fitting). Because the number publication years ranging from 1948 to 2017. of publications varies from year to year, in each year, for each entity, Downloaded from https://academic.oup.com/jamiaopen/advance-article-abstract/doi/10.1093/jamiaopen/ooy001/4959056 by Ed 'DeepDyve' Gillespie user on 08 June 2018 JAMIA Open, 2018, Vol. 0, No. 0 3 Figure 1. Top 20 diseases (A) and medications (B). AD: anxiety disorders; BD: bipolar disorder; PTSD: stress disorders, post-traumatic; Panic: panic disorder; SIMD: sleep initiation and maintenance disorders; Personality: personality disorders; DM: diabetes mellitus; WI: wounds and injuries; PSD: sexual dysfunctions, psychological; OCD: obsessive-compulsive disorder; Phobic: phobic disorders; ADHD: attention deﬁcit disorder with hyperactivity; Parkinson: Parkinson Disease. Medications are abbreviated with the ﬁrst 4 characters. we use the percentage of articles within each year that discussed the In the treemap (Figure 1), entities are visualized such that the entity to represent the entity’s frequency in the year. The purpose of size of the rectangle represents how each entity is proportional to this analysis is to identify entities that have been continuously gain- the frequency of the entity. A 2-color scheme was used to differenti- ing or losing mentions in the literature but not to estimate the cor- ate mental health disorders from nonmental health conditions (in rect trends. An entity’s increasing trend may suggest its increasing the case of diseases) and antidepressants from nonpsychotropic importance within the research community, whereas a decreasing drugs (in the case of medications). trend may suggest that research interest has declined over time. In As shown in the figure, among the top 20 diseases that co- addition, the slopes of linear trend lines indicate intensities of the occurred frequently with MDD, 12 diseases are mental disorders trends, allowing us to compare them among entities. (based on MeSH Tree Structures) and their frequencies are greater than that of other diseases. These 12 diseases with decreasing fre- quencies are Anxiety Disorders (2374), Bipolar Disorder (2009), Bursting entities Schizophrenia (981), Stress Disorders, Post-Traumatic (623), Panic Not every entity has an overall increasing or decreasing trend and a Disorder (509), Sleep Initiation and Maintenance Disorders (369), more common trend among entities is its “rise” and “fall.” In this Personality Disorders (358), Sexual Dysfunctions, Psychological paradigm, “burst” is a phenomenon that explains how an entity (318), Obsessive-Compulsive Disorder (305), Phobic Disorders rises sharply in frequency at a certain point of time and grows in in- 8 (257), Attention Deficit Disorder with Hyperactivity (236), and tensity for a period. This is an important indicator that highlights a Dementia (218). Among the 12 diseases, Panic Disorder, Obsessive- time span during which an entity has received increased, unusual Compulsive Disorder, and Phobic Disorders are subconcepts of spotlight (represented as mentions in the literature). Yearly percen- Anxiety Disorders in the MeSH Tree Structures. We can see that tages of articles that discuss an entity are represented as time series researchers have studied Anxiety Disorders as a whole as well as data and used to detect bursts for the entity if any. specific types of Anxiety Disorders. A possible reason that MDD has been studied with many other mental disorders is that a large portion of MDD patients have comorbid mental illness. For exam- RESULTS ple, a nationally representative epidemiologic study reported that Top entities more than 70% of MDD patients have comorbid mental disorders. Top 20 diseases and medications were selected based on frequency The limitations of the current psychiatric diagnostic system can (Figure 1). Frequency of an entity was defined as the number of also partly explain the results. The International Classification of articles that mention both the entity and MDD. The medications Diseases (ICD) system and the Diagnostic and Statistical Manual of were manually extracted from the list of chemicals because PubTator Mental Disorders (DSM) system for mental disorders are primarily does not differentiate medications from other chemicals. based on symptoms, and therefore, a patient can be diagnosed with Downloaded from https://academic.oup.com/jamiaopen/advance-article-abstract/doi/10.1093/jamiaopen/ooy001/4959056 by Ed 'DeepDyve' Gillespie user on 08 June 2018 4 JAMIA Open, 2018, Vol. 0, No. 0 Figure 2. Top 20 frequent disease (A) and medications (B) sets. AD: anxiety disorders; BD: bipolar disorder; PTSD: stress disorders; post-traumatic; Panic: panic disorder; SIMD: sleep initiation and maintenance disorders; Personality: personality disorders; DM: diabetes mellitus; WI: wounds and injuries; PSD: sexual dys- functions; psychological; OCD: obsessive-compulsive disorder; Phobic: phobic disorders; ADHD: attention deﬁcit disorder with hyperactivity; Parkinson: Parkin- son disease. Medications are abbreviated with the ﬁrst 4 characters. multiple mental disorders that share the same symptoms. To address the arc co-occur with entities in its inner arcs and MDD. For exam- this, there are on-going efforts to improve the classification and di- ple, among the frequent disease sets, Schizophrenia (top-right) agnostic systems to convey the heterogeneous pathophysiology. co-occurred with Bipolar Disorder and MDD more frequently than Eight nonmental disorders with decreasing frequencies are Pain Diabetes Mellitus (top-left) with Hypertension and MDD. (339), Diabetes Mellitus (334), Wounds and Injuries (328), Head- As shown in Figure 2, many mental disorders co-occurred fre- ache (200), Seizures (200), Neoplasms (198), Parkinson Disease quently with each other, which is consistent with previous findings. (191), and Hypertension (189). Here, we briefly explain the rela- The 4 frequent disease sets that include both mental disorders and tionships between some of the above diseases with MDD. Chronic other diseases are: MDD, Stress Disorders, Post-Traumatic, and Pain is known to be common among MDD patients, and it has Wounds and Injuries (157); MDD, Anxiety Disorders, and Pain been shown that caring for patients with both physical pain and psy- (77); MDD, Hypertension, and Diabetes Mellitus (67); and MDD, chiatric illness can be challenging. Depression is also associated Anxiety Disorders, and Wounds and Injuries (59). The co- with the prevalence of Diabetes Mellitus, and patients’ treatment occurrence of MDD and Wounds and Injuries with Stress Disorders, adherence. In terms of Wounds and Injuries, we found that many Post-Traumatic and Anxiety Disorders is explained by the fact that studies have explored associations between depression and trau- trauma experience caused by Wounds and Injuries can lead to men- 15 16 17 18 matic brain injury, war, sexual violence, childhood abuse, tal disorders such as MDD, Anxiety Disorders, and Stress Disorders, 19 25 and early life parental loss. Post-Traumatic. The frequent disease set of MDD, Anxiety Disor- Among the top 20 medications that co-occurred frequently with ders, and Pain has been discussed as a challenging psychiatric co- MDD, 12 medications are antidepressants, and include (in decreas- morbid condition because of the “complex interplay of affective, ing order of frequency) Citalopram (751), Fluoxetine (601), Venla- behavioral, cognitive and physical aspects of pain.” The co- faxine (478), Sertraline (398), Paroxetine (394), Duloxetine (302), occurrence pattern of MDD, Hypertension, and Diabetes Mellitus Mirtazapine (209), Bupropion (184), Aripiprazole (127), Quetia- can be explained by the fact that Hypertension and Diabetes Melli- pine (118), Nortriptyline (118), and Fluvoxamine (110). Among the tus may co-occur as a metabolic syndrome. Studies also reported other 8 nonantidepressants, Norepinephrine (427) and Dopamine that MDD is related with both Diabetes Mellitus and Hyper- (257) were extracted by the tool, likely because these 2 terms are tension independently. parts of classes of antidepressants (ie Norepinephrine reuptake In the top 20 frequent medication sets, most are sets of antide- inhibitors, Serotonin-norepinephrine reuptake inhibitors, and pressants. The only set that includes nonantidepressant medications Norepinephrine-dopamine reuptake inhibitors). Hydrocortisone is Hydrocortisone and Dexamethasone, which co-occurred 119 (484), Dexamethasone (159), and Benzodiazepines (129) are known times with MDD. As mentioned previously, both Hydrocortisone 20,21 to have depression as one of their side effects. Ketamine (226) and Dexamethasone are known to have depression as one of their and Lithium (186) are off-label uses, and they have not been offi- side effects. 22,23 cially approved for treating depression. Increasing involvement with Ethanol (172) is known to increase the risk of depression. Linear trends of entities The linear trends of diseases (from 2000 to 2016) are shown in Frequent entity sets Table 1 with nonmental disorders boldfaced. The year 2000 was The frequent disease and medication sets were computed and the chosen because the volume of the publications available electroni- top 20 sets in each category are shown in Figure 2.In Figure 2, the cally via PubMed each year prior to 2000 is small with the average length of an arc is proportional to the frequency of the entity in number of 23. In most years, the size of MDD publications is Downloaded from https://academic.oup.com/jamiaopen/advance-article-abstract/doi/10.1093/jamiaopen/ooy001/4959056 by Ed 'DeepDyve' Gillespie user on 08 June 2018 JAMIA Open, 2018, Vol. 0, No. 0 5 Table 1. Linear trends of diseases (2000–2016) Table 2. Linear trends of medications (2000–2016) Name Frequency Slope P-value Name Frequency Slope P-value Bipolar disorder 1885 0.00159 .00933 Citalopram 735 0.00155605 .005598284 Diabetes mellitus 353 0.00102 .00031 Ketamine 185 0.001131715 1.09E06 Stress disorders, post-traumatic 606 0.00010 .00073 Ethanol 164 0.001026535 .013549743 Wounds and injuries 345 0.00087 .00004 Aripiprazole 121 0.000560246 3.11E05 Cardiovascular diseases 291 0.00046 .01059 Quetiapine 114 0.000400164 .003401941 Sleep initiation, and maintenance 360 0.00038 .01485 Nortriptyline 109 0.000465342 .004795913 disorders Imipramine 87 0.000551417 .004298498 Obsessive-compulsive disorder 292 0.00076 .00025 Lithium 175 0.000592834 .00034675 Personality disorders 389 0.00077 .00085 Dexamethasone 150 0.000636098 .000637746 Phobic disorders 225 0.00078 .00053 Fluvoxamine 106 0.000742118 .63E06 Dementia 299 0.00080 .00276 Hydrocortisone 465 0.001293984 .000232898 Panic disorder 485 0.00113 .00027 Paroxetine 379 0.001306111 2.23E05 Fluoxetine 581 0.001360485 .004872951 Figure 3. Bursting diseases (2000–2016). Bursting diseases and the time periods (red bars) during which the bursts occurred and continued are plotted. smaller than 100. The year 2017 was excluded because the publica- tine have not been studied as frequently as before. Among non- tion records for the year are not complete. Table 1 shows diseases antidepressants, Ketamine, which has been reported to have whose linear trends are significant (P-value <.05). Slopes represent antidepressant effect has been gaining increasing interests among intensities of trends with positive values indicating increasing trends researchers along with Ethanol. Another off-label use, Lithium with and negative values indicating decreasing trends. For example, in 2 medications (Dexamethasone and Hydrocortisone) have decreas- Table 1, Bipolar Disorder has a stronger increasing trend than ing trends. Diabetes Mellitus. Diseases that have been mentioned less than 10 times each year on average are not included. We set the constraint to focus on diseases that have been frequently or moderately Bursting entities mentioned and show significant trend lines. Among the 11 diseases Bursts were only identified in diseases, as no bursts were detected in shown in Table 1, 5 diseases have been increasingly mentioned in medications. Figure 3 shows bursting diseases and the time periods the literature and 3 of them (Diabetes Mellitus, Wounds and Inju- (red bars) during which the bursts occurred and continued. Among ries, and Cardiovascular Diseases) are not mental disorders. On the the 9 bursting diseases, 6 diseases such as Pain, Diabetes Mellitus, other hand, all the other 6 diseases, which are mental disorders, Wounds and Injuries, Cardiovascular Diseases, Seizures, and Hyper- have been gaining decreasing interests among researchers with re- tension are nonmental disorders. spect to their relationships with MDD are mental disorders. It shows To interpret this data, we reviewed literature to get insights on that, in recent years, researchers are more actively involved in under- bursts of articles related to very common chronic conditions in the standing relationships between mental disorders and other diseases. adult US population: Diabetes Mellitus and Cardiovascular Dis- Table 2 shows the linear trends of medications (from 2000 to eases. Diabetes Mellitus burst between 2000 and 2010. The number 2016) with nonantidepressants boldfaced. Table 2 includes medica- of relevant articles increased from <5 in early years (2010, 2001, tions that have been mentioned more than 5 times each year on aver- and 2002) to more than 40 in 2010. The scope of the study on Dia- age. Because medications have not been mentioned as frequently as betes Mellitus and MDD during the period was broad including 27 29 14 30 diseases in the literature, we set the minimum frequency value of 5 studies on association, test, treatment adherence, prognosis, 31 32 in order to be included. Among antidepressants, Citalopram, Aripi- quality of care, and medical cost. In 2005, Standards of Medical prazole, and Quetiapine have been studied increasingly whereas Care in Diabetes published by the American Diabetes Association Nortriptyline, Imipramine, Fluvoxamine, Paroxetine, and Fluoxe- included the recommendation for “screening for psychosocial Downloaded from https://academic.oup.com/jamiaopen/advance-article-abstract/doi/10.1093/jamiaopen/ooy001/4959056 by Ed 'DeepDyve' Gillespie user on 08 June 2018 6 JAMIA Open, 2018, Vol. 0, No. 0 problems in diabetes patients with poor adherence.” Many rele- highly relevant entities whereas full-texts have broader coverage vant articles published between 2000 and 2005 might have contrib- with possibly irrelevant entities. Third, our analyzes were based on uted to the recommendation. In addition, the recommendation co-occurrence of entities. Co-occurrence is a useful method to iden- might also have drawn researchers’ attention to further study the re- tify relationships between entities. However, co-occurrence does not lationship between Diabetes Mellitus and MDD. The publications always imply relationships. Therefore, it is possible that entities co- related to Cardiovascular Diseases burst between 2003 and 2011. occur together with MDD in an abstract do not necessary mean they The number of relevant articles increased from 3 in 2003 to 25 in have clear relationships with MDD. 34–36 2011. Many laboratory studies on the association between Cardiovascular Diseases and MDD have contributed to the burst as well as to the publication of clinical recommendation for CONCLUSION “depression screening in patients with coronary heart disease” by the American Heart Association Prevention Committee in 2008. Scientific literature is a valuable repository of knowledge. Research- From the aforementioned 2 examples, we can see that, the bursts ers have been continuously contributing to that repository by pub- identified in the entities correctly represent temporal characteristics lishing research articles. Because we cannot keep up with the rapid of research and can serve as an effective signal for later findings and pace of knowledge production, a bibliometric systematic analysis of clinical decisions. research publications is of great help to both researchers and practi- tioners to have a general understanding of a research domain. The study achieved this by exploring diseases and medications that have important relationships with MDD based on the entity-level analysis DISCUSSION of a comprehensive set of MDD research articles published from In the study, we broadly explored diseases and medications that 1948 to 2017. We presented the research landscape of MDD from have frequently been studied with MDD by analyzing abstracts of various perspectives by considering both static (top entities and fre- approximately 24 000 MDD research articles that have been pub- quent entity sets) and dynamic (linear trends and bursts) characteris- lished since 1948. Results of the study provided a research overview tics of entities. and landscape of MDD from 4 perspectives: top entities, frequent With the rapid growth and accumulation of MDD research pub- entity sets, linear trends of entities, and bursting entities. lications, there is a continuous need to systematically analyze them Many mental disorders have frequently been studied with MDD, to get overall and up-to-date research landscapes. The approaches which is consistent with a previous finding that 70% MDD patients proposed in the study can be used to better understand the progress have comorbid mental disorders. Nonmental disorders such as Pain, and advance of the field. The study not only provides researchers Diabetes Mellitus, and Hypertension have also been studied. MDD and practitioners a clear understanding of previous work, but results has also been studied in terms of its relationships with traumatic reported in this study can serve as useful hypotheses and help 15 16 17 18 brain injury, war, sexual violence, childhood abuse, and researchers formulate meaningful research questions. early life parental loss. In addition to common antidepressants, medications that cause depression as a side effect (Hydrocortisone, Dexamethasone, and FUNDING 20,21 Benzodiazepines) and off-label uses of medications that may act 22,23 as or supplement antidepressants (Ketamine and Lithium) have This research was funded in part by NIH R01 MH105384 and P50 also been widely studied. Top entities and frequent entity sets are a MH113838. simple, yet a very useful way to identify a core set of entities that Conflict of interest statement. Authors report no competing interests play an important role in MDD research. in this study. No sponsors took any role in the study design, analy- Through the temporal analyzes, we found that relationships be- sis, interpretation, or writing of the report. tween MDD and nonmental health disorders such as Diabetes Melli- tus, Wounds and Injuries, and Cardiovascular Diseases have gained increasing attention in the scientific community in recent years. REFERENCES Among nonpsychotropic drugs, Ketamine has been studied signifi- 1. Smith K. Mental health: a world of depression. Nature 2014;515(7526): cantly in the recent past. Bursts were identified in several diseases, and our results suggest that that they can correctly represent tempo- 2. Murray CJ, Vos T, Lozano R. 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JAMIA Open – Oxford University Press
Published: Apr 3, 2018
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