Biomedical Search Engine Research Articles

Attention-based Unsupervised Keyphrase Extraction and Phrase Graph for COVID-19 Medical Literature Retrieval

Searching, reading, and finding information from the massive medical text collections are challenging. A typical biomedical search engine is not feasible to navigate each article to find critical information or keyphrases. Moreover, few tools provide a visualization of the relevant phrases to the query. However, there is a need to extract the keyphrases from each document for indexing and efficient search. The transformer-based neural networks—BERT has been used for various natural language processing tasks. The built-in self-attention mechanism can capture the associations between words and phrases in a sentence. This research investigates whether the self-attentions can be utilized to extract keyphrases from a document in an unsupervised manner and identify relevancy between phrases to construct a query relevancy phrase graph to visualize the search corpus phrases on their relevancy and importance. The comparison with six baseline methods shows that the self-attention-based unsupervised keyphrase extraction works well on a medical literature dataset. This unsupervised keyphrase extraction model can also be applied to other text data. The query relevancy graph model is applied to the COVID-19 literature dataset and to demonstrate that the attention-based phrase graph can successfully identify the medical phrases relevant to the query terms.

NSGA-III-Based Deep-Learning Model for Biomedical Search Engines

With the advancements in biomedical imaging applications, it becomes more important to provide potential results for searching the biomedical imaging data. During the health emergency, tremors require efficient results at rapid speed to provide results to spatial queries using the Web. An efficient biomedical search engine can obtain the significant search intention and return additional important contents in which users have already indicated some interest. The development of biomedical search engines is still an open area of research. Recently, many researchers have utilized various deep-learning models to improve the performance of biomedical search engines. However, the existing deep-learning-based biomedical search engines suffer from the overfitting and hyperparameter tuning problems. Therefore, in this paper, a nondominated-sorting-genetic-algorithm-III- (NSGA-III-) based deep-learning model is proposed for biomedical search engines. Initially, the hyperparameters of the proposed deep-learning model are obtained using the NSGA-III. Thereafter, the proposed deep-learning model is trained by using the tuned parameters. Finally, the proposed model is validated on the testing dataset. Comparative analysis reveals that the proposed model outperforms the competitive biomedical search engine models.

Wound Microbiome, Biofilm Infection and Interactions with Host Immune Cells in Diabetic Foot Ulcers (DFU)

Background: 
 The treatment of infected diabetic foot wounds places a great burden on our healthcare system. The problem lies in the complexity of treating these wounds, often made more complex by the formation of a bacterial biofilm leading to chronic infection. This critical review discusses current knowledge of biofilm infection and wound healing related to diabetic foot ulcers. Hypothesis: Wound microbiome and biofilm infection subvert immune cell function, specifically the macrophage defense response, which leads to a non-healing chronic wound. In addition to the critical review of literature, a systematic analysis of wound microbiome was performed on fluids obtained from chronic wound patients presented at the IUHCWC. 
 
 Methods: 
 A comprehensive literature review was performed on DFU microbiome, biofilm infection and interaction with host immune system, and macrophage interactions with biofilm using life sciences and biomedical search engines such as PubMed. Over 50 original and review articles were critically reviewed to derive the information presented in this work. For the wound microbiome analysis, wound fluids from chronic wound patients (n=20) undergoing wound care at IUHCWC were collected under an IRB approved protocol. DNA extraction was performed from the fluid and was subjected to metagenomic 16S rDNA sequencing using a next-generation sequencing platform for the microbiome analysis. 
 
 Results & Conclusions: 
 The biofilm induces a persistent state of low-grade inflammation via cytokine release and the continuous production of metabolic waste. Up-regulation of genes such as arginine-1 and interleukin-10, as well as a metabolic shift from aerobic glycolysis to oxidative phosphorylation, promote macrophage polarization to an anti-inflammatory, and more importantly, immune suppressive phenotype incapable of clearing the biofilm infection. A detailed analysis of wound fluid microbiome is currently ongoing and will be presented in the poster presentation forum. 
 
 Work supported by IMPRS fellowship to EC and NIH R01DK114718 to SR

Granulomatous cheilitis: Report of three cases and systematic review of cases and case series reported from India

Introduction: Granulomatous cheilitis (GC) is a rare idiopathic inflammatory disorder characterized by chronic persistent swelling of the lips. Despite a lingering controversy, it remains linked to orofacial granulomatosis and Melkersson–Rosenthal syndrome (MRS). Aim: To systematically review all cases of GC reported hereto from India with the three being reported now, analyzing patients'/disease characteristics, associated diseases, and treatment protocol. Materials and Methods: Using biomedical search engines such as PubMed, Scopus, and Google Scholar, a systemic review of cases/case series reported from India (1994–2018) was undertaken, and data on patients'/disease characteristics, association with other diseases, and treatment extracted discussed and analyzed without subjecting to statistical analysis due to the small population size. Results: The mean age at presentation of these cases (n, 73: Single, 39: Participants of 6 series, 31 plus presently reported, 3) was 29.36 years and at onset was 26.62 years. There was female preponderance of 1.8. Complete triad of MRS was recorded in 8 (10.95%), and oligosymptomatic forms were as follows: GC + facial palsy, 5 (6.85%); GC + fissured tongue, 4 (5.48%); and facial palsy + fissured tongue, 1 (1.37%). Monosymptomatic GC was seen in 55 (75.34%) and upper lip involvement in 29 (39.72%). Conditions associated with GC included Down syndrome, rosacea, granulomatous vulvitis, and pregnancy. None of the cases were associated with Crohn disease and sarcoidosis. Intralesional triamcinolone acetonide (33, 45.21%) was the most common modality of treatment. Conclusion: Cases of GC from India over the past quarter of a century were reported rarely and showed female preponderance. This chronically recurrent entity with variable severity lacks larger studies and randomized controlled trials for optimum treatment.

Improving Bibliographic Coupling with Category-Based Cocitation

Bibliographic coupling (BC) is a similarity measure for scientific articles. It works based on an expectation that two articles that cite a similar set of references may focus on related (or even the same) research issues. For analysis and mapping of scientific literature, BC is an essential measure, and it can also be integrated with different kinds of measures. Further improvement of BC is thus of both practical and technical significance. In this paper, we propose a novel measure that improves BC by tackling its main weakness: two related articles may still cite different references. Category-based cocitation (category-based CC) is proposed to estimate how these different references are related to each other, based on the assumption that two different references may be related if they are cited by articles in the same categories about specific topics. The proposed measure is thus named BCCCC (Bibliographic Coupling with Category-based Cocitation). Performance of BCCCC is evaluated by experimentation and case study. The results show that BCCCC performs significantly better than state-of-the-art variants of BC in identifying highly related articles, which report conclusive results on the same specific topics. An experiment also shows that BCCCC provides helpful information to further improve a biomedical search engine. BCCCC is thus an enhanced version of BC, which is a fundamental measure for retrieval and analysis of scientific literature.

BIOMEDICAL PUBLICATIONS PRODUCTIVITY OF ORAL AND DENTAL MEDICINE FROM LIBYA (PUBMED 2003-2013)

The purpose of this short communication study was to investigation the trends of biomedical publications productivity of oral and dentistry field from Libya using the most popular biomedical search engine PubMed with focus on Libyan academic institutions. This search was performed on articles that were related to dentistry and oral medicine using the affiliation of Libya for the period 2003-2013. A total of 358 biomedical published articles were retrieved of which only 40 were related to oral and dentistry medicine. In conclusion, this investigation shows a very low productivity and weak distribution of biomedical research in oral and dentistry medicine productivity throughout Libya.

Fifteen Years of Platelet Rich Fibrin in Dentistry and Oromaxillofacial Surgery: How High is the Level of Scientific Evidence?

Platelet-rich fibrin is a blood concentrate system used for soft tissue and bone tissue regeneration. In the last decade, platelet rich fibrin (PRF) has been widely used in different indication fields, particularly in oral and maxillofacial surgery. This review investigates the level of scientific evidence of published articles related to the use of PRF for bone and soft tissue regeneration in dentistry and maxillofacial surgery. An electronic literature research using the biomedical search engine "National Library of Medicine" (PubMed-MEDLINE) was performed in May 2017. A total of 392 articles were found, 72 of which were classified for each indication field. When comparing PRF with biomaterials vs biomaterial alone in sinus lift (5 studies; IIa), no statistically significant differences were detected. Socket preservation and ridge augmentation using PRF significantly enhanced new bone formation compared to healing without PRF (7 studies Ib, IIa, IIb). Reepithelialization and bone regeneration was achieved in 96 of 101 patients diagnosed with medication-related osteonecrosis of the jaw (5 studies, III). In periodontology, PRF alone (6 studies; Ib, IIa, IIb) or its combination with biomaterials (6 studies; Ib, IIa, IIb) significantly improved the pocket depth and attachment loss compared to a treatment without PRF. Over 70% of the patients were part of studies with a high level of scientific evidence (randomized and controlled prospective studies). This published evidence (38 articles), with a high scientific level, showed that PRF is a beneficial tool that significantly improves bone and soft tissue regeneration. However, the clinical community requires a standardization of PRF protocols to further examine the benefit of PRF in bone and soft tissue regeneration in reproducible studies, with a higher scientific level of evidence.

Optimal algorithms for selecting top-k combinations of attributes: theory and applications

Traditional top-k algorithms, e.g., TA and NRA, have been successfully applied in many areas such as information retrieval, data mining and databases. They are designed to discover k objects, e.g., top-k restaurants, with highest overall scores aggregated from different attributes, e.g., price and location. However, new emerging applications like query recommendation require providing the best combinations of attributes, instead of objects. The straightforward extension based on the existing top-k algorithms is prohibitively expensive to answer top-k combinations because they need to enumerate all the possible combinations, which is exponential to the number of attributes. In this article, we formalize a novel type of top-k query, called top-k, m, which aims to find top-k combinations of attributes based on the overall scores of the top-m objects within each combination, where m is the number of objects forming a combination. We propose a family of efficient top-k, m algorithms with different data access methods, i.e., sorted accesses and random accesses and different query certainties, i.e., exact query processing and approximate query processing. Theoretically, we prove that our algorithms are instance optimal and analyze the bound of the depth of accesses. We further develop optimizations for efficient query evaluation to reduce the computational and the memory costs and the number of accesses. We provide a case study on the real applications of top-k, m queries for an online biomedical search engine. Finally, we perform comprehensive experiments to demonstrate the scalability and efficiency of top-k, m algorithms on multiple real-life datasets.

Elsevier's approach to the bioCADDIE 2016 Dataset Retrieval Challenge.

We developed a two-stream, Apache Solr-based information retrieval system in response to the bioCADDIE 2016 Dataset Retrieval Challenge. One stream was based on the principle of word embeddings, the other was rooted in ontology based indexing. Despite encountering several issues in the data, the evaluation procedure and the technologies used, the system performed quite well. We provide some pointers towards future work: in particular, we suggest that more work in query expansion could benefit future biomedical search engines. Database URL: https://data.mendeley.com/datasets/zd9dxpyybg/1

Quantitative physiological measurements to evaluate the response of antivascular endothelial growth factor treatment in patients with neovascular diseases.

Antivascular endothelial growth factor (VEGF) treatment is still used intravitreally worldwide for various neovascular diseases, despite other available, approved treatments. We performed a systematic search of the literature focused on visual physiology studies. We used the online biomedical search engine PubMed and searched key words including “M-chart,” “Preferential Hyperacuity Perimetry,” “microperimetry,” (MP) “electroretinography,” and “contrast sensitivity” to estimate treatment efficacy of anti-VEGF treatments in a quantitative manner. Many studies were identified which used a variety of methodologies, disease entities, injected agents, and patient populations, making it difficult to obtain a direct comparison of their results. However, favorable functional outcomes achieved using current quantitative methods would lend further confidence to the effectiveness of a treat-and-extend protocol using intravitreal anti-VEGF for the management of patients with neovascular diseases. Despite anti-VEGF's wide use, a well-designed longitudinal multicenter study to systematically evaluate and compare different physiological methods or parameters in patients with neovascular diseases is still lacking, though it would benefit therapeutic decisions.

BIOMedical Search Engine Framework: Lightweight and customized implementation of domain-specific biomedical search engines

Background and objectivesText mining and semantic analysis approaches can be applied to the construction of biomedical domain-specific search engines and provide an attractive alternative to create personalized and enhanced search experiences. Therefore, this work introduces the new open-source BIOMedical Search Engine Framework for the fast and lightweight development of domain-specific search engines. The rationale behind this framework is to incorporate core features typically available in search engine frameworks with flexible and extensible technologies to retrieve biomedical documents, annotate meaningful domain concepts, and develop highly customized Web search interfaces. MethodsThe BIOMedical Search Engine Framework integrates taggers for major biomedical concepts, such as diseases, drugs, genes, proteins, compounds and organisms, and enables the use of domain-specific controlled vocabulary. Technologies from the Typesafe Reactive Platform, the AngularJS JavaScript framework and the Bootstrap HTML/CSS framework support the customization of the domain-oriented search application. Moreover, the RESTful API of the BIOMedical Search Engine Framework allows the integration of the search engine into existing systems or a complete web interface personalization. ResultsThe construction of the Smart Drug Search is described as proof-of-concept of the BIOMedical Search Engine Framework. This public search engine catalogs scientific literature about antimicrobial resistance, microbial virulence and topics alike. The keyword-based queries of the users are transformed into concepts and search results are presented and ranked accordingly. The semantic graph view portraits all the concepts found in the results, and the researcher may look into the relevance of different concepts, the strength of direct relations, and non-trivial, indirect relations. The number of occurrences of the concept shows its importance to the query, and the frequency of concept co-occurrence is indicative of biological relations meaningful to that particular scope of research. Conversely, indirect concept associations, i.e. concepts related by other intermediary concepts, can be useful to integrate information from different studies and look into non-trivial relations. ConclusionsThe BIOMedical Search Engine Framework supports the development of domain-specific search engines. The key strengths of the framework are modularity and extensibilityin terms of software design, the use of open-source consolidated Web technologies, and the ability to integrate any number of biomedical text mining tools and information resources. Currently, the Smart Drug Search keeps over 1,186,000 documents, containing more than 11,854,000 annotations for 77,200 different concepts. The Smart Drug Search is publicly accessible at http://sing.ei.uvigo.es/sds/. The BIOMedical Search Engine Framework is freely available for non-commercial use at https://github.com/agjacome/biomsef.

Risk Factors and Protective Factors for Lower-Extremity Running Injuries

A review of the scientific literature was performed 1) to identify studies describing the most common running injuries and their relation to the risk factors that produce them and 2) to search for potential and specific protective factors. Spanish and English biomedical search engines and databases (MEDLINE/PubMed, Database Enfermería Fisioterapia Podología [ENFISPO], Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature) were queried (February 1 to November 30, 2013). A critical reading and assessment was then performed by the Critical Appraisal Skills Programme Spanish tool. In total, 276 abstracts that contained the selected key words were found. Of those, 25 identified and analyzed articles were included in the results. Injuries result from inadequate interaction between the runner's biomechanics and external factors. This leads to an excessive accumulation of impact peak forces in certain structures that tends to cause overuse injuries. The main reasons are inadequate muscle stabilization and pronation. These vary depending on the runner's foot strike pattern, foot arch morphology, and sex. Specific measures of modification and control through running footwear are proposed.

Topic Categorization of Biomedical Abstracts

Nowadays, people frequently use search engines in order to find the information they need on the Web. Especially, Web search constitutes of a basic tool used by million researchers in their everyday work. A very popular indexing engine, concerning life sciences and biomedical research, is PubMed. PubMed is a free database accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. The present search engines usually return search results in a global ranking, making it difficult for the users to browse in different topics or subtopics. In this work we propose a novel system to address the issues of clustering biomedical search engine results according to their topic. A methodology that exploits semantic text clustering techniques in order to group biomedical document collections in homogeneous topics, is presented and evaluated. In order to provide more accurate clustering results, we utilize various biomedical ontologies, likeMeSH and GeneOntology. Finally, we embed the proposed methodology in an online system that post-processes the PubMed online database so as to provide users the retrieved results according to well formed topics. So as to expose our method as an online real-time service with reasonable response times, we performed a wide range of engineering optimizations along with experimentation on preprocessing time to precision of results trade off.

Recherche Biomédicale publiée en Algérie – état des lieux en 2014

Background: Biomedical research is the motor for improving knowledge in the domains of biology, medical sciences, dentistry, and pharmacology. PubMed is the most known biomedical search engine; it covers all the “indexed” biomedical journals. We wanted to make a state of the art of Algerian biomedical research articles through a PubMed search. Methods: a PubMed search was performed on December 15th, 2014, using the PubMed website, by including as a unique search word: “Algeria”, as a MeSH (Medical Subject Heading) and as a free search, in order to capture all articles having included Algerian authors or having concerned an Algerian population. The intervals for the date of publication were restricted between January 1st and December 15th, 2014. We have saved the results, read the abstracts, and if necessary, the whole article. Results: All in all, 412 articles have been identified, in progression as compared with the preceding years (288 in 2012 and 336 in 2013). More than half of the articles were dealing with fundamental and translational research. Only three articles were published in surgery. Here we show a subjective selection of articles that we have judged are the most innovative. Conclusion: biomedical research, completed with an “indexed” publication is progressing in Algeria, although our country is still behind some African and Arab countries. Much progress has been made in improving knowledge in biomedical domains in Algeria, which deserves to be encouraged and amplified.

Development and evaluation of a biomedical search engine using a predicate-based vector space model

Although biomedical information available in articles and patents is increasing exponentially, we continue to rely on the same information retrieval methods and use very few keywords to search millions of documents. We are developing a fundamentally different approach for finding much more precise and complete information with a single query using predicates instead of keywords for both query and document representation. Predicates are triples that are more complex datastructures than keywords and contain more structured information. To make optimal use of them, we developed a new predicate-based vector space model and query-document similarity function with adjusted tf-idf and boost function. Using a test bed of 107,367 PubMed abstracts, we evaluated the first essential function: retrieving information. Cancer researchers provided 20 realistic queries, for which the top 15 abstracts were retrieved using a predicate-based (new) and keyword-based (baseline) approach. Each abstract was evaluated, double-blind, by cancer researchers on a 0–5 point scale to calculate precision (0 versus higher) and relevance (0–5 score). Precision was significantly higher (p<.001) for the predicate-based (80%) than for the keyword-based (71%) approach. Relevance was almost doubled with the predicate-based approach—2.1 versus 1.6 without rank order adjustment (p<.001) and 1.34 versus 0.98 with rank order adjustment (p<.001) for predicate—versus keyword-based approach respectively. Predicates can support more precise searching than keywords, laying the foundation for rich and sophisticated information search.

Twenty Questions for Neuroscience Metadata

Finding relevant data in the biomedical literature can be difficult sometime. To select a few neuroscience examples, suppose you would like to know (a) which serotonin receptor subunits are expressed in dentate gyrus mossy cells; (b) the average volume of the amygdala in adult male chimpanzees; (c) whether there is an EEG signature of CreutzfeldJacob disease; or (d) studies reporting bilateral fMRI activity in Brodmann area 38. These are fairly simple questions, for which standard search engines should fare relatively well. Yet even for these kinds of questions, securing the relevant information takes much longer than googling up the local weather forecast for the week-end or tomorrow’s commuter train schedule. The actual data required to build biologically realistic computational models are often more detailed: what is the time constant of the excitatory synaptic current from a specified pair of neuron types? Finding the answer in this case might require many hours or even days of queries over multiple search engines. Most importantly, the results of these queries must be typically followed by at least cursory reading of dozens of papers. When the graduate student triumphantly brings to the lab the needed reference, the adviser could mumble without lifting the eyes from the keyboard “that’s in young animals, and it was recorded at room temperature”. Another unfortunate major limitation is that, until and unless a definitive answer is found, it is usually impossible to know whether the information is available or not. In other words, existing biomedical search engines are ill-equipped to inform users that something is not yet known. Standard search algorithms such as PubMed are less than ideal to deal with data identification, because they are ultimately based on matching strings or concepts that appear in the title, abstract, and the keywords. These texts, however, are written with narrow scientific agendas in mind. The authors cannot possibly provide a list of keywords that would encompass all research projects for which some data in their articles might be relevant. If the topic of a report is the molecular phenotyping of a new genetic model of schizophrenia, the technical details of the deconvolution algorithm to deblur the optical micrographs would be nearly impossible to pick up through keyword searches. Could we devise a procedure to interrogate the scientific literature so as to extract accurately and efficiently most if not all of the relevant data? Is there a literature mining protocol that could give us the confidence that, if the query returns a blank, it means that the sought data is not yet available? Although common to all of biomedical science, this issue is particularly critical in neuroscience because of its unmatched diversity of dimensions, scales, questions, approaches, and techniques. Thus, effective tagging of publications with relevant metadata remains an outstanding neuroinformatics challenge. Full text searches provide half of the solution, in that they eliminate many of the issues related to false negatives. Many of the helpful terms to identify relevant data, for example, appear in the Materials and Methods sections of published articles rather than in their titles, abstracts, and keywords. Search engines scanning through the entire main text of publications include early visionary projects such as Textpresso, which started within the limited domain of C. elegans, then expanded to a

Shortfalls in pediatric hydrocephalus clinical outcome analysis

In this paper, we used search engine technology to study outcome analysis and cost awareness of child hydrocephalus in the literature. The aggregate hospital charges of hydrocephalus treatment procedures for patients <18years old was extracted from the Nationwide Inpatient Sample (NIS) data. Hydrocephalus literature was probed through the PubMed biomedical search engine. Aggregate hospital charges associated with ventriculo-peritoneal shunting as the principle procedure for patients <18years old have increased 1.7-fold over a 13-year period to 235.6 million in 2009. Hospital discharges, however, decreased from 3,390 in 1997 to 2,525 in 2009 (25.5% decrease over 13years). The number of papers in English language indexed by PubMed in relation to child hydrocephalus in humans increased from 81 papers in 1996 to 133 in 2010 (1.6-fold increase), totaling 1,694 over 15years. Randomized controlled trials published in relation to child hydrocephalus totaled 16 over the same period (0.94% of child hydrocephalus papers). Papers related to child hydrocephalus with "costs and cost analysis" as medical subject heading totaled 13 papers (0.77%). Over the past 15years, disappointingly the number of printed child hydrocephalus papers appeared to have only plateaued. Strikingly, only a very small number of these papers were directed toward randomized control studies, the sine qua non of high-grade clinical evidence. Moreover, very few papers make reference to cost analysis or economics in the treatment of hydrocephalus - an issue coming increasingly before the nation at this point.

Development and evaluation of a triple parser to enable visual searching with a biomedical search engine

We describe a new biomedical search engine that enables visual searching and utilises predicates instead of phrases. We report on the development and evaluation of the triple parser, the most essential component of the search engine, which extracts the necessary predicates from the biomedical text. Using texts from three biomedical related sites (N = 180), we compared the parser’s output with a gold standard independently created by a medical expert. The parser achieved more than 91% precision and recall. Its individual components showed different strengths with Finite State Automata being excellent for achieving high recall, while Support Vector Machines improved the precision.

Cost/Charges Appraisal and Clinical Evidence Considerations in Orthopaedic Literature

Introduction: Health care expenditures in the United States have rapidly risen in the last decade, including orthopaedic induced expenses. This paper addresses the methodology by which clinical evidence is obtained to better direct orthopaedic practice and encourage cost-efficiency. Questions: We conducted this inquiry to answer these questions: 1) Have orthopaedic expenses increased in the United States? 2) Does high grade clinical evidence prevail in orthopaedic literature? 3) Does clinical orthopaedic research include cost in outcome analysis? 4) Does the increase in orthopaedic expenses correlate with cost awareness in orthopaedic literature? Methods: The aggregate hospital charges (national bill) associated with three major orthopaedic procedures were extracted from the Nationwide Inpatient Sample (NIS) website (data available from 1997 to 2008). Using the biomedical search engine PubMed (launched 1996), different types of literature pertaining to general orthopaedic and three major orthopaedic procedures—hip replacement, knee replacement, and spine fusion, were probed regarding level of evidence and cost inclusion. Results: From 1997 to 2008, the national hospital charges for spine fusion increased by 10.4 times while for total knee replacement it increased by 4.9 times and for total/partial hip replacement by 3.4 times. From 1996 to 2010, PubMed indexed 1113 randomized controlled trials (RCTs) related to hip replacement, 942 related to knee replacement and 357 related to spine fusion. During the same period, RCTs related to total/partial hip replacement, total knee replacement and spine fusion procedures have increased by 3 times, 6 times, and 3.4 times, respectively. The percentage of blinded RCTS with cost analysis among all RCTs related to each procedure were 0%, 0.001% and 0.003% for total/partial hip replacement, total knee replacement and spine fusion procedures respectively. The correlation in the national hospital charges of all three procedures individually and their level I literature with cost element was not significant except for spine fusion. Conclusion: To improve literature shortfalls, substantial attention needs to be focused toward more rigorous studies which consider cost efficiency.

Mining MEDLINE for the Treatment of Osteoporosis

In this paper, we consider the importance of osteoporosis disease in terms of medical research and pharmaceutical industry and we introduce a knowledge discovery approach regarding the treatment of osteoporosis from a historical perspective. Osteoporosis is a systemic skeletal disease in which osteoporotic fractures are associated with substantial morbidity and mortality and impaired quality of life. Osteoporosis has also higher costs, for example, longer hospital stays than many other diseases such as diabetes and heart attack and it is an attractive market for pharmaceutical companies. We use a freely available biomedical search engine leveraging text-mining technology to extract the drug names used in the treatment of osteoporosis from MEDLINE articles. We conclude that alendronate (Fosamax) and raloxifene (Evista) have the highest number of articles in MEDLINE and seem the dominating drugs for the treatment of osteoporosis in the last decade.