Relevant bibliographies by topics / Mining methods

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Mining methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Mining methods"

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Singh, Sarah, and Ineke Klinge. "Mining for Methods." Freiburger Zeitschrift für GeschlechterStudien 21, no. 2 (November 9, 2015): 15–31. http://dx.doi.org/10.3224/fzg.v21i2.20934.

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., D. M. Kulkarni. "USING DATA MINING METHODS KNOWLEDGE DISCOVERY FOR TEXT MINING." International Journal of Research in Engineering and Technology 03, no. 01 (January 25, 2014): 24–29. http://dx.doi.org/10.15623/ijret.2014.0301005.

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VijayGaikwad, Sonali, Archana Chaugule, and Pramod Patil. "Text Mining Methods and Techniques." International Journal of Computer Applications 85, no. 17 (January 16, 2014): 42–45. http://dx.doi.org/10.5120/14937-3507.

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Rajavat, Anand, and Pranjal singh solanki. "Modern Association Rule Mining Methods." International Journal of Computational Science and Information Technology 2, no. 4 (November 30, 2014): 1–9. http://dx.doi.org/10.5121/ijcsity.2014.2401.

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Su, Xiaogang. "Data Mining Methods and Models." American Statistician 62, no. 1 (February 2008): 91. http://dx.doi.org/10.1198/tas.2008.s97.

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Booth, David E. "Data Mining Methods and Models." Technometrics 49, no. 4 (November 2007): 500. http://dx.doi.org/10.1198/tech.2007.s697.

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Chen, Liping, Jie Yang, and Wei Liu. "Global mining governance evaluation methods." Mineral Economics 28, no. 3 (November 2015): 123–27. http://dx.doi.org/10.1007/s13563-015-0073-0.

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Walker, S. "Comparative underground coal mining methods." Fuel and Energy Abstracts 37, no. 3 (May 1996): 170. http://dx.doi.org/10.1016/0140-6701(96)88326-3.

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Harper, Gavin, and Stephen D. Pickett. "Methods for mining HTS data." Drug Discovery Today 11, no. 15-16 (August 2006): 694–99. http://dx.doi.org/10.1016/j.drudis.2006.06.006.

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Rao, K. Srinivasa, and B. Srinivasa Rao. "An Insight in to Privacy Preserving Data Mining Methods." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 01, no. 02 (June 27, 2013): 31–35. http://dx.doi.org/10.9756/sijcsea/v1i2/0103570301.

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Dissertations / Theses on the topic "Mining methods"

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Mwitondi, K. S. "Robust methods in data mining." Thesis, University of Leeds, 2003. http://etheses.whiterose.ac.uk/807/.

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The thesis focuses on two problems in Data Mining, namely clustering, an exploratory technique to group observations in similar groups, and classification, a technique used to assign new observations to one of the known groups. A thorough study of the two problems, which are also known in the Machine Learning literature as unsupervised and supervised classification respectively, is central to decision making in different fields - the thesis seeks to contribute towards that end. In the first part of the thesis we consider whether robust methods can be applied to clustering - in particular, we perform clustering on fuzzy data using two methods originally developed for outlier-detection. The fuzzy data clusters are characterised by two intersecting lines such that points belonging to the same cluster lie close to the same line. This part of the thesis also investigates a new application of finite mixture of normals to the fuzzy data problem. The second part of the thesis addresses issues relating to classification - in particular, classification trees and boosting. The boosting algorithm is a relative newcomer to the classification portfolio that seeks to enhance the performance of classifiers by iteratively re-weighting the data according to their previous classification status. We explore the performance of "boosted" trees (mainly stumps) based on 3 different models all characterised by a sine-wave boundary. We also carry out a thorough study of the factors that affect the boosting algorithm. Other results include a new look at the concept of randomness in the classification context, particularly because the form of randomness in both training and testing data has directly affects the accuracy and reliability of domain- partitioning rules. Further, we provide statistical interpretations of some of the classification-related concepts, originally used in Computer Science, Machine Learning and Artificial Intelligence. This is important since there exists a need for a unified interpretation of some of the "landmark" concepts in various disciplines, as a step forward towards seeking the principles that can guide and strengthen practical applications.
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Wirta, Valtteri. "Mining the transcriptome - methods and applications." Doctoral thesis, Stockholm : School of Biotechnology, Royal Institute of Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4115.

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Siddiqui, Muazzam. "DATA MINING METHODS FOR MALWARE DETECTION." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2783.

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This research investigates the use of data mining methods for malware (malicious programs) detection and proposed a framework as an alternative to the traditional signature detection methods. The traditional approaches using signatures to detect malicious programs fails for the new and unknown malwares case, where signatures are not available. We present a data mining framework to detect malicious programs. We collected, analyzed and processed several thousand malicious and clean programs to find out the best features and build models that can classify a given program into a malware or a clean class. Our research is closely related to information retrieval and classification techniques and borrows a number of ideas from the field. We used a vector space model to represent the programs in our collection. Our data mining framework includes two separate and distinct classes of experiments. The first are the supervised learning experiments that used a dataset, consisting of several thousand malicious and clean program samples to train, validate and test, an array of classifiers. In the second class of experiments, we proposed using sequential association analysis for feature selection and automatic signature extraction. With our experiments, we were able to achieve as high as 98.4% detection rate and as low as 1.9% false positive rate on novel malwares.
Ph.D.
Other
Sciences
Modeling and Simulation PhD
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Espinoza, Sofia Elizabeth. "Data mining methods applied to healthcare problems." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44903.

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Growing adoption of health information technologies is allowing healthcare providers to capture and store enormous amounts of patient data. In order to effectively use this data to improve healthcare outcomes and processes, clinicians need to identify the relevant measures and apply the correct analysis methods for the type of data at hand. In this dissertation, we present various data mining and statistical methods that could be applied to the type of datasets that are found in healthcare research. We discuss the process of identification of appropriate measures and statistical tools, the analysis and validation of mathematical models, and the interpretation of results to improve healthcare quality and safety. We illustrate the application of statistics and data mining techniques on three real-world healthcare datasets. In the first chapter, we develop a new method to assess hydration status using breath samples. Through analysis of the more than 300 volatile organic compounds contained in human breath, we aim to identify markers of hydration. In the second chapter, we evaluate the impact of the implementation of an electronic medical record system on the rate of inpatient medication errors and adverse drug events. The objective is to understand the impact on patient safety of different information technologies in a specific environment (inpatient pediatrics) and to provide recommendations on how to correctly analyze count data with a large amount of zeros. In the last chapter, we develop a mathematical model to predict the probability of developing post-operative nausea and vomiting based on patient demographics and clinical history, and to identify the group of patients at high-risk.
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Vu, Lan. "High performance methods for frequent pattern mining." Thesis, University of Colorado at Denver, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3667246.

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Current Big Data era is generating tremendous amount of data in most fields such as business, social media, engineering, and medicine. The demand to process and handle the resulting "big data" has led to the need for fast data mining methods to develop powerful and versatile analysis tools that can turn data into useful knowledge. Frequent pattern mining (FPM) is an important task in data mining with numerous applications such as recommendation systems, consumer market analysis, web mining, network intrusion detection, etc. We develop efficient high performance FPM methods for large-scale databases on different computing platforms, including personal computers (PCs), multi-core multi-socket servers, clusters and graphics processing units (GPUs). At the core of our research is a novel self-adaptive approach that performs efficiently and fast on both sparse and dense databases, and outperforms its sequential counterparts. This approach applies multiple mining strategies and dynamically switches among them based on the data characteristics detected at runtime. The research results include two sequential FPM methods (i.e. FEM and DFEM) and three parallel ones (i.e. ShaFEM, SDFEM and CGMM). These methods are applicable to develop powerful and scalable mining tools for big data analysis. We have tested, analysed and demonstrated their efficacy on selecting representative real databases publicly available at Frequent Itemset Mining Implementations Repository.

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SOUZA, Ellen Polliana Ramos. "Swarm optimization clustering methods for opinion mining." Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/25227.

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Opinion Mining (OM), also known as sentiment analysis, is the field of study that analyzes people’s sentiments, evaluations, attitudes, and emotions about different entities expressed in textual input. This is accomplished through the classification of an opinion into categories, such as positive, negative, or neutral. Supervised machine learning (ML) and lexicon-based are the most frequent approaches for OM. However, these approaches require considerable effort for preparing training data and to build the opinion lexicon, respectively. In order to address the drawbacks of these approaches, this Thesis proposes the use of unsupervised clustering approach for the OM task which is able to produce accurate results for several domains without manually labeled data for the training step or tools which are language dependent. Three swarm algorithms based on Particle Swarm Optimization (PSO) and Cuckoo Search (CS) are proposed: the DPSOMUT which is based on a discrete PSO binary version, the IDPSOMUT that is based on an Improved Self-Adaptive PSO algorithm with detection function, and the IDPSOMUT/CS that is a hybrid version of IDPSOMUT and CS. Several experiments were conducted with different corpora types, domains, text language, class balancing, fitness function, and pre-processing techniques. The effectiveness of the clustering algorithms was evaluated with external measures such as accuracy, precision, recall, and F-score. From the statistical analysis, it was possible to observe that the swarm-based algorithms, especially the PSO ones, were able to find better solutions than conventional grouping techniques, such as K-means and Agglomerative. The PSO-based algorithms achieved better accuracy using a word bigram pre-processing and the Global Silhouette as fitness function. The OBCC corpus is also another contribution of this Thesis and contains a gold collection with 2,940 tweets in Brazilian Portuguese with opinions of consumers about products and services.
A mineração de opinião, também conhecida como análise de sentimento, é um campo de estudo que analisa os sentimentos, opiniões, atitudes e emoções das pessoas sobre diferentes entidades, expressos de forma textual. Tal análise é obtida através da classificação das opiniões em categorias, tais como positiva, negativa ou neutra. As abordagens de aprendizado supervisionado e baseadas em léxico são mais comumente utilizadas na mineração de opinião. No entanto, tais abordagens requerem um esforço considerável para preparação da base de dados de treinamento e para construção dos léxicos de opinião, respectivamente. A fim de minimizar as desvantagens das abordagens apresentadas, esta Tese propõe o uso de uma abordagem de agrupamento não supervisionada para a tarefa de mineração de opinião, a qual é capaz de produzir resultados precisos para diversos domínios sem a necessidade de dados rotulados manualmente para a etapa treinamento e sem fazer uso de ferramentas dependentes de língua. Três algoritmos de agrupamento não-supervisionado baseados em otimização de partícula de enxame (Particle Swarm Optimization - PSO) são propostos: o DPSOMUT, que é baseado em versão discreta do PSO; o IDPSOMUT, que é baseado em uma versão melhorada e autoadaptativa do PSO com função de detecção; e o IDPSOMUT/CS, que é uma versão híbrida do IDPSOMUT com o Cuckoo Search (CS). Diversos experimentos foram conduzidos com diferentes tipos de corpora, domínios, idioma do texto, balanceamento de classes, função de otimização e técnicas de pré-processamento. A eficácia dos algoritmos de agrupamento foi avaliada com medidas externas como acurácia, precisão, revocação e f-medida. A partir das análises estatísticas, os algortimos baseados em inteligência coletiva, especialmente os baseado em PSO, obtiveram melhores resultados que os algortimos que utilizam técnicas convencionais de agrupamento como o K-means e o Agglomerative. Os algoritmos propostos obtiveram um melhor desempenho utilizando o pré-processamento baseado em n-grama e utilizando a Global Silhouete como função de otimização. O corpus OBCC é também uma contribuição desta Tese e contem uma coleção dourada com 2.940 tweets com opiniões de consumidores sobre produtos e serviços em Português brasileiro.
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Johnson, Eamon B. "Methods in Text Mining for Diagnostic Radiology." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1459514073.

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Eales, James Matthew. "Text-mining of experimental methods in phylogenetics." Thesis, University of Manchester, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.529251.

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Sundaravej, Dilokpol. "Predictive methods for subsidence due to longwall mining." Ohio : Ohio University, 1986. http://www.ohiolink.edu/etd/view.cgi?ohiou1183379335.

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Bastos, Guilherme Sousa. "Methods for truck dispatching in open-pit mining." Instituto Tecnológico de Aeronáutica, 2010. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=1098.

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Material transportation is one of the most important aspects of open-pit mine operations. The problem usually involves a truck dispatching system in which decisions on truck assignments and destinations are taken in real-time. Due to its significance, several decision systems for this problem have been developed in the last few years, improving productivity and reducing operating costs. As in many other real-world applications, the assessment and correct modeling of uncertainty is a crucial requirement as the unpredictability originated from equipment faults, weather conditions, and human mistakes, can often result in truck queues or idle shovels. However, uncertainty is not considered in most commercial dispatching systems. In this thesis, we introduce novel truck dispatching systems as a starting point to modify the current practices with a statistically principled decision making methodology. First, we present a stochastic method using Time-Dependent Markov Decision Process (TiMDP) applied to the truck dispatching problem. In the TiMDP model, travel times are represented as probabilistic density functions (pdfs), time-windows can be inserted for paths availability, and time-dependent utility can be used as a priority parameter. In order to minimize the well-known curse of dimensionality issue, to which multi-agent problems are subject when considering discrete state modelings, the system is modeled based on the introduced single-dependent-agents. Based also on the single-dependent-agents concept, we introduce the Genetic TiMDP (G-TiMDP) method applied to the truck dispatching problem. This method is a hybridization of the TiMDP model and of a Genetic Algorithm (GA), which is also used to solve the truck dispatching problem. Finally, in order to evaluate and compare the results of the introduced methods, we execute Monte Carlo simulations in a example heterogeneous mine composed by 15 trucks, 3 shovels, and 1 crusher. The uncertain aspect of the problem is represented by the path selection through crusher and shovels, which is executed by the truck driver, being independent of the dispatching system. The results are compared to classical dispatching approaches (Greedy Heuristic and Minimization of Truck Cycle Times - MTCT) using Student's T-test, proving the efficiency of the introduced truck dispatching methods.
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Books on the topic "Mining methods"

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Singh, J. G. Underground Coal Mining Methods. Varanasi, India: Braj-Kalp Publishers, 2002.

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Journel, A. G. Mining geostatistics. Caldwell, N.J: Blackburn Press, 2003.

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Walker, Simon. Comparative underground coal mining methods. Londpn: IEA Coal Research, 1996.

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Data mining methods and models. Hoboken, NJ: Wiley, 2005.

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Larose, Daniel T. Data Mining Methods and Models. New York: John Wiley & Sons, Ltd., 2006.

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Larose, Daniel T. Data Mining Methods and Models. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471756482.

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Mark, Christopher. Pillar design methods for longwall mining. Washington, DC: Dept. of the Interior, 1990.

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Mark, Christopher. Pillar design methods for longwall mining. Washington, DC: U.S. Dept. of the Interior, Bureau of Mines, 1990.

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Cios, Krzysztof J. Data Mining Methods for Knowledge Discovery. Boston, MA: Springer US, 1998.

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1953-, Pedrycz Witold, and Świniarski Roman, eds. Data mining methods for knowledge discovery. Boston: Kluwer Academic, 1998.

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Book chapters on the topic "Mining methods"

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Abzalov, Marat. "Mining Methods." In Modern Approaches in Solid Earth Sciences, 5–18. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39264-6_2.

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Brady, B. H. G., and E. T. Brown. "Mining methods and method selection." In Rock Mechanics, 292–315. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-011-6501-3_12.

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Brady, B. H. G., and E. T. Brown. "Mining methods and method selection." In Rock Mechanics, 326–49. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-8129-5_12.

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Padmalal, D., and K. Maya. "River Sand Mining and Mining Methods." In Environmental Science and Engineering, 23–30. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9144-1_3.

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Han, Jiawei, and Jian Pei. "Pattern-Growth Methods." In Frequent Pattern Mining, 65–81. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_3.

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Gärtner, Thomas, Tamás Horváth, Quoc V. Le, Alex J. Smola, and Stefan Wrobel. "Kernel Methods for Graphs." In Mining Graph Data, 253–82. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/9780470073049.ch11.

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Rusiński, Eugeniusz, Jerzy Czmochowski, Przemysław Moczko, and Damian Pietrusiak. "Methods of Condition Assessment." In Surface Mining Machines, 41–84. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47792-3_3.

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Maimon, Oded, and Mark Last. "Advanced data mining methods." In Knowledge Discovery and Data Mining, 123–33. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3296-2_8.

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Brady, B. H. G., and E. T. Brown. "Naturally supported mining methods." In Rock Mechanics, 316–50. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-011-6501-3_13.

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Brady, B. H. G., and E. T. Brown. "Artificially supported mining methods." In Rock Mechanics, 351–68. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-011-6501-3_14.

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Conference papers on the topic "Mining methods"

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Yin, Yanchun, Yunliang Tan, Weijia Guo, and Minglu Zhang. "Research advances of heterogeneity representation methods for rocks." In Taishan Academic Forum - Project on Mine Disaster Prevention and Control. Paris, France: Atlantis Press, 2014. http://dx.doi.org/10.2991/mining-14.2014.47.

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Han, Jiawei, Laks V. S. Lakshmanan, and Jian Pei. "Scalable frequent-pattern mining methods." In Tutorial notes of the seventh ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/502786.502792.

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Puolamaki, Kai, Panagiotis Papapetrou, and Jefrey Lijffijt. "Visually Controllable Data Mining Methods." In 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.141.

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Onan, Aytug, and Serdar Korukoglu. "Ensemble methods for opinion mining." In 2015 23th Signal Processing and Communications Applications Conference (SIU). IEEE, 2015. http://dx.doi.org/10.1109/siu.2015.7129796.

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Mannila, Heikki. "Randomization methods in data mining." In the 15th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1557019.1557023.

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Chen, Ying, and Cheng Luo. "Research on Data Mining Methods." In 2016 4th International Conference on Management, Education, Information and Control (MEICI 2016). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/meici-16.2016.139.

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Manchanda, P. "Wavelet methods in data mining." In EMERGING APPLICATIONS OF WAVELET METHODS: 7th International Congress on Industrial and Applied Mathematics - Thematic Minisymposia. AIP, 2012. http://dx.doi.org/10.1063/1.4740042.

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Bereznev, V. A., and V. V. Nikiforov. "The Study of Effective Foundation Design Methods Taking into Account Seismoacoustic Methods." In Engineering and Mining Geophysics 2020. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202051104.

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Romyen, Nirach, Sureeporn Nualnim, Maleerat Maliyaem, Pudsadee Boonrawd, Kanchana Viriyapant, and Tongpool Heeptaisong. "Opinion Mining using TRC Techniques." In 10th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010315203210326.

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Wang, Lei, Yang Yang, and Huimin Cheng. "The Application of Fuzzy Analytic Hierarchy Process for Thick Coal Seam Mining Methods in China." In Taishan Academic Forum - Project on Mine Disaster Prevention and Control. Paris, France: Atlantis Press, 2014. http://dx.doi.org/10.2991/mining-14.2014.13.

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Reports on the topic "Mining methods"

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MacDonald, R. J., and D. A. Payne. Changing mining methods at CBDC. Natural Resources Canada/CMSS/Information Management, 1993. http://dx.doi.org/10.4095/328669.

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Singhal, R. K., and T. S. Golosinski. Surface mining of Canadian coal: equipment selection and mining methods. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1986. http://dx.doi.org/10.4095/304985.

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Muirhead, I. R., and R. J. Kolada. Less conventional methods for mining underground coal. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1986. http://dx.doi.org/10.4095/304905.

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Hedley, D. G. F. Underground mining methods, planning and ground control. Natural Resources Canada/CMSS/Information Management, 1987. http://dx.doi.org/10.4095/325530.

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Antsyferov, Andrey, Oleksandr Glukhov, Victor Trofymov, Mikhail Pedchenko, and Vadim Antsiferov. Methods and practical results of using GeoMark geoinformation system in mining. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0232.

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Currie, Janet, Henrik Kleven, and Esmée Zwiers. Technology and Big Data Are Changing Economics: Mining Text to Track Methods. Cambridge, MA: National Bureau of Economic Research, January 2020. http://dx.doi.org/10.3386/w26715.

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Sprague, J. B. Review of methods for sublethal aquatic toxicity tests relevant to the Canadian metal-mining industry. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1997. http://dx.doi.org/10.4095/306923.

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Eyermann, T. J., L. L. Van Sambeek, and F. D. Hansen. Case studies of sealing methods and materials used in the salt and potash mining industries. Office of Scientific and Technical Information (OSTI), November 1995. http://dx.doi.org/10.2172/188537.

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White, D. J., Z. Hajnal, B. Roberts, I. Györfi, B. Reilkoff, G. Bellefleur, C. Mueller, et al. Seismic methods for uranium exploration: an overview of EXTECH IV seismic studies at the McArthur River mining camp, Athabasca Basin, Saskatchewan. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2007. http://dx.doi.org/10.4095/223782.

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Paynter, Robin A., Celia Fiordalisi, Elizabeth Stoeger, Eileen Erinoff, Robin Featherstone, Christiane Voisin, and Gaelen P. Adam. A Prospective Comparison of Evidence Synthesis Search Strategies Developed With and Without Text-Mining Tools. Agency for Healthcare Research and Quality (AHRQ), March 2021. http://dx.doi.org/10.23970/ahrqepcmethodsprospectivecomparison.

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Abstract:
Background: In an era of explosive growth in biomedical evidence, improving systematic review (SR) search processes is increasingly critical. Text-mining tools (TMTs) are a potentially powerful resource to improve and streamline search strategy development. Two types of TMTs are especially of interest to searchers: word frequency (useful for identifying most used keyword terms, e.g., PubReminer) and clustering (visualizing common themes, e.g., Carrot2). Objectives: The objectives of this study were to compare the benefits and trade-offs of searches with and without the use of TMTs for evidence synthesis products in real world settings. Specific questions included: (1) Do TMTs decrease the time spent developing search strategies? (2) How do TMTs affect the sensitivity and yield of searches? (3) Do TMTs identify groups of records that can be safely excluded in the search evaluation step? (4) Does the complexity of a systematic review topic affect TMT performance? In addition to quantitative data, we collected librarians' comments on their experiences using TMTs to explore when and how these new tools may be useful in systematic review search¬¬ creation. Methods: In this prospective comparative study, we included seven SR projects, and classified them into simple or complex topics. The project librarian used conventional “usual practice” (UP) methods to create the MEDLINE search strategy, while a paired TMT librarian simultaneously and independently created a search strategy using a variety of TMTs. TMT librarians could choose one or more freely available TMTs per category from a pre-selected list in each of three categories: (1) keyword/phrase tools: AntConc, PubReMiner; (2) subject term tools: MeSH on Demand, PubReMiner, Yale MeSH Analyzer; and (3) strategy evaluation tools: Carrot2, VOSviewer. We collected results from both MEDLINE searches (with and without TMTs), coded every citation’s origin (UP or TMT respectively), deduplicated them, and then sent the citation library to the review team for screening. When the draft report was submitted, we used the final list of included citations to calculate the sensitivity, precision, and number-needed-to-read for each search (with and without TMTs). Separately, we tracked the time spent on various aspects of search creation by each librarian. Simple and complex topics were analyzed separately to provide insight into whether TMTs could be more useful for one type of topic or another. Results: Across all reviews, UP searches seemed to perform better than TMT, but because of the small sample size, none of these differences was statistically significant. UP searches were slightly more sensitive (92% [95% confidence intervals (CI) 85–99%]) than TMT searches (84.9% [95% CI 74.4–95.4%]). The mean number-needed-to-read was 83 (SD 34) for UP and 90 (SD 68) for TMT. Keyword and subject term development using TMTs generally took less time than those developed using UP alone. The average total time was 12 hours (SD 8) to create a complete search strategy by UP librarians, and 5 hours (SD 2) for the TMT librarians. TMTs neither affected search evaluation time nor improved identification of exclusion concepts (irrelevant records) that can be safely removed from the search set. Conclusion: Across all reviews but one, TMT searches were less sensitive than UP searches. For simple SR topics (i.e., single indication–single drug), TMT searches were slightly less sensitive, but reduced time spent in search design. For complex SR topics (e.g., multicomponent interventions), TMT searches were less sensitive than UP searches; nevertheless, in complex reviews, they identified unique eligible citations not found by the UP searches. TMT searches also reduced time spent in search strategy development. For all evidence synthesis types, TMT searches may be more efficient in reviews where comprehensiveness is not paramount, or as an adjunct to UP for evidence syntheses, because they can identify unique includable citations. If TMTs were easier to learn and use, their utility would be increased.
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