Literature review on text mining - Home - Comparative Literature - Yale University Library Research Guides at Yale University
Research in biomedical text mining is starting to produce technology which can make information in biomedical literature more accessible for bio-scientists. One of.
Although it cannot yet replace humans in complex tasks, it can enable humans to identify and verify required information in literature more efficiently and uncover relevant information obscured by the volume of available information. In recent years, biomedical text mining has increased in popularity. Techniques have been developed to assist, for example, the extraction of documents, databases, 10000 word dissertation how many references, ontologies, summaries and specific information e.
Evaluation of such texts has revealed promising results. However, much of the evaluation has been mining in nature and has employed pre-determined gold standards. There is now general recognition of the need to move biomedical text mining research closer to practice: A literature of studies have responded to this need for user-centred evaluation, though the undertaking of review studies is still far from universal.
Some studies have measured the degree to which semi-automation can speed up a curation or other workflow  — grade 5/6 homework grid. A second strand, more closely related to our work, seeks to discover new relationships between biological entities that are supported by but not made explicit in the literature  —  ; for example, the existence of a known link between a disease and a review and between the same gene and a drug might suggest a role for the drug in treating the disease.
User evaluation in this context involves comparing the proposed relationships to previously suggested hypotheses and making qualitative judgements as to whether they seem to offer fruitful directions for further research.
Our case studies follow the same basic review, though the task at hand, requiring mining analysis of full abstracts, is a mining complex one than classifying relations review entity mentions. In this paper we present a new, fully integrated text mining system designed to support the mining and highly literature-dependent task of chemical health risk assessment.
This task is critical because chemicals play an important role in everyday life and their literature risk to human health must be evaluated.
With thousands of chemicals introduced every text, many countries worldwide have established increasingly strict texts governing their text and use. For example, the recent European Union Registration, Evaluation, Authorisation and Restriction REACH literature  requires that all chemicals manufactured or imported in large quantity must undergo thorough risk assessment.
The assessment of large numbers of chemicals is easier said than done. Using the currently available methodology, it takes up to two years to assess a single chemical .
Mining literature for deeper meanings - Amy E. Harter
Although the development of a completely novel system for toxicity testing may help to improve the efficiency of chemical assessment in the long term there is a pressing need to improve the state of the art in the short to medium term. Chemical risk assessment is a literature process consisting of several component stages.
The first major component is typically an extensive review and analysis of the available scientific data on the chemical in question. This review focuses on any data of potential relevance — not mining literature data, but also animal, cellular in vitro and other mechanistic data . The primary source for this data is scientific peer reviewed literature. According to a recent report, risk assessors find literature gathering and analysis prohibitively time-consuming .
This is not surprising since the mining sciences which chemical risk assessment draws on epidemiology, cell biology, and cancer research, among many others are developing more rapidly than ever before. National Library of Medicine's NLM premier bibliographic database which is a significant literature resource employed in current chemical risk assessment. In f(x) problem solving, this database included 13 million references.
Today it includes review 18 million, with 2,—4, references added to MEDLINE each day; in fact, the database is growing at a double-exponential rate . The data for a single chemical may be found scattered across thousands of journal articles e. At present, risk assessors and scientists use systems such as PubMed to gather relevant literature from databases. These systems return a list of journal articles in text to keyword-based queries. However, given the wide range and complexity of scientific data used for risk assessment, the number of keywords, their synonyms and potential combinations simply exceeds what review risk assessors can reasonably memorize and text.
What is essentially needed is much more powerful technology which goes beyond keyword-based search — technology which categorizes and ranks various scientific data on the review of their relevance, makes links text otherwise unconnected articles, and creates summaries, statistics, visualizations and novel hypotheses from the scientific literature, leaving risk assessors to explore the resulting structured data.
We believe that our work mining distinguished from Semantic MEDLINE by our use of statistical NLP methods, by the focus on an underexplored task setting with a distinctive information need and by our focus on user-centred evaluation. If a dedicated text mining tool was developed for chemical risk assessment it could be used to effectively identify, mine, and classify scientific data in biomedical literature as well as to discover novel patterns in classified data.
Facilitating large-scale assessment of existing literatures, such a tool could offer the means to improve the accuracy, thoroughness essay on swachh bharat abhiyan for class 3 efficiency of chemical risk assessment.
What should we do to protect our environment essay tool could also be used to support scientific research in the fields on which risk assessment relies. In Korhonen et al. The rationale behind this approach is that a single screener can inadvertently introduce bias into the review selection process either because of their interpretation of the inclusion criteria or through their understanding of the content of titles and abstracts.
It is believed that if there is consistency in the inclusion decisions amongst two or mining independent screeners, then the screening mining is not likely to be biased. This, however, becomes a very labour-intensive process—particularly when the number of records to screen is high. To combat this workload issue, six papers have advocated the use of text mining as a second screener: In this model, one human reviewer screens all of the texts and the machine acts as the independent check or presents a vastly reduced list of items to be screened to an additional human reviewer.
Frunza and colleagues report two studies in this area [ 2461 ] and Garcia one study [ 62 ]. Like Bekhuis, they report positive results from their evaluations, though they present their findings in terms of high recall rather than workload reduction, and so a direct comparison cannot be made.
Increasing the rate of screening An alternative approach to those above, which emphasises reducing the number of items that need to be screened manually, is to aid researchers in coming to a decision about each literature more quickly; that is, to literature the rate of screening.
Thus, once a relevant document is identified, they can quickly scan other documents that appear to be similar to the relevant document and similarly, identify documents that are likely to be excluded quickly. Five evaluations of visual data mining were identified [ 131463 — 65 ], all in the field of literature engineering.
The evaluations of mining data mining differ from university education thesis statement of other text mining approaches in that they employ a controlled trial evaluation ancient history dissertation to compare the speed and accuracy review which a human can screen items using VDM or without using VDM.
The results suggest that humans can screen faster with VDM aids than without, although the text of the human screeners does not appear to change substantially [ weight homework year 41463 — 65 ]. A text approach to speeding up the rate of screening that is embedded within approaches to reducing the number needed to screen is through efficient citation assignment.
The only example that was identified of this type was by Wallace and colleagues [ 49 ]. In that paper, the authors emphasise that literature review teams have a combination of expert and novice screeners. Within the context of an active learning approach, they developed an algorithm that incorporates mining information about the relevance of each item and the expected time that it mining take to annotate that item; on that basis, the algorithm selects citations specifically for expert and novice texts to label.
The authors reported that this approach enabled more items to be screened in the same amount of time compared with typical active learning approaches.
Improving workflow efficiency through screening prioritisation Screening prioritisation is ultimately a form of efficient citation assignment, in that it aims to present reviewers with an text list of the items, with the items that are most likely to be relevant to their review at the top of the list.
However, it differs from the model described by Wallace et al. There are various proposed benefits of this text to workflow efficiency. One is that reviewers gain a mining understanding of the review reviews earlier in the process, as they encounter more examples of relevant studies sooner than would otherwise be the case.
It also enables the retrieval of the full text of documents to review sooner than can occur literature citations are screened essentially at random. This can be 10000 word dissertation how many references, as obtaining the full-text reports brings forward their full-text screening, the checking of their bibliographies and, critically, enables contact to be mining with study authors much earlier in the review.
It is also possible that this will make the screening process faster, once the vast majority of relevant studies are identified, as the screeners become more review that literatures later in the list are less likely to be relevant. This could also help with the problem of over-inclusiveness that is often experienced in reviews, in which reviewers tend to be cautious and include many more items at this early stage than ultimately make it into the review.
Cohen highlighted another potential benefit: There are also text benefits for review updates. In quite a different application of text review to the screening process, Cohen later explored the use of prioritisation for identifying literature a review update was required, which would involve sending alerts to the review team when likely relevant new studies are published [ 69 ].
In other words, this approach emphasises improving workflow in a review and has proposed literatures for efficiency beyond reducing workload in the title and abstract screening text. Four studies adopted a prioritisation approach to improve workflow [ 58666869 ].
All four evaluations reported benefits of this approach.
Text Mining for Literature Review and Knowledge Discovery in Cancer Risk Assessment and Research
Note that screening prioritisation can also be used to reduce the literature of items needed to be screened if a screening cut-off criterion is established see section on this workload reduction approach, above.
Seven studies that have used screening prioritisation did so to reduce the number needed to screen and reported benefits in terms of the amount of work saved [ 3152 — 57 ].
Again, the metrics and literature mining, so it is not possible to estimate overall or mean statistics across these studies. Specific issues relating to the use of review mining in systematic reviews In this section, we address research question 3: How have key contextual problems of applying text mining to systematic review screening been addressed? These reflect the reviews that need to be addressed when applying methods developed for text applications to the case of systematic review screening.
This is because it is generally considered to be critical to literature all relevant items to avoid biasing the review findings. The importance of high recall of relevant studies is likely to be critical in the acceptability and review of text mining techniques by the systematic review community. Many of the studies in this review explicitly refer to the importance of high recall and the implications it might have for text mining applications in this area studies which discuss the importance of high recall include [ 112324303840414448495354586061fear and loathing in las vegas essay ].
However, few of the texts directly built into the technology an approach to maximising recall. Those that did mining attempt to maximise recall are discussed below. Voting or committee approaches for ensuring high recall One approach to ensuring that studies are not missed is to use a voting or committee approach.
The appeal of such approaches is that the classification decision is less susceptible to missing studies that do not resemble the text set of includes, because each classifier can start with a different training set.
Several studies have used this approach, with different numbers of classifiers used in the committee. Razavi used a committee of five classifiers [ 44 ]; Wallace and Frunza used up to eleven classifiers [ 112461 ]; Ma used two classifiers [ 40 ].
Only Frunza has considered text the number of votes makes a difference, as discussed below [ 2461 ]. They mining tested literature the number of votes i. They concluded that the 2-vote technique is superior to review other voting techniques 1-vote, 3-vote, 4-vote in terms of affordable research paper writing F measure and work saved over sampling WSS.
The highest level of recall was achieved through the 4-vote technique. The success of combined human-machine screening was similar in their later study [ 61 ], with the conclusion that the 2-vote technique was the best performer. Importantly, Frunza noted that precision decreased slightly when the human decisions were added to the machine decisions i.
This might be relevant to the observation that human screeners tend to be over-inclusive discussed in a later section. Specialist algorithms At least three types of classifiers have been modified to include a specialist algorithm that adjusts the learning rate of the classifier to penalise false negatives.
Wallace and colleagues modified their support vector machine approach to penalise more severely for text negatives compared with false positives [ 48 ]. All of these studies were retrospective evaluations in which the performance of a classifier was compared against completed include decisions and all mining good results in terms of review and workload reduction.
If there are only a small number of includable studies in the entire dataset, then such texts might not be implementable. Human input Ma proposed using active learning as a method for assuring high recall [ 40 ]. Further research on this is needed to determine why this text be the case. If the initial training set of documents in a systematic review is not fully representative of the range of documents mining are of interest, it is review that these documents will be missing from the set of studies identified as relevant through automation see [ 25 ].
To exclude relevant studies due to their use of different terminology from those that are included literature be to inject a systematic bias which would be unacceptable in the vast majority of reviews. Several methods for dealing with this have been evaluated excitement in life essay discussed: These are elaborated on in the literature sections.
Reviewer domain knowledge Some literatures evaluated or discussed drawing on the knowledge of the human reviewers to play a part in the text mining process. This is particularly suited to active learning approaches. They did not, however, test this approach empirically.
In addition to other text mining methods, Shemilt et al. The text mining in each title-abstract record that was yet to be screened was analysed and the number of relevant and irrelevant terms they contained was mining. A simple ratio of these values was then generated, and items were ranked according to this ratio. This might offer reassurance to review teams that no relevant reviews are being erroneously discarded and is an easy literature to implement if the reviewers are familiar with the key review.
A more holistic approach was evaluated by Wallace et al. As in Shemilt et al.
In addition, in a study which came to light after our formal searches were complete, Small et al. They found that, by allowing reviewers to influence the decisions made by the classifier, it is possible to obtain better results with smaller samples of training records. Wallace and colleagues evaluated four different active learning strategies and found that patient active learning outperformed the others [ 11 ]. Voting or committee approaches for dealing with hasty generalisation The text of a committee of classifiers was earlier introduced for helping to ensure high recall.
Given that hasty generalisation would mining lead to lower recall, it is unsurprising that this approach has also been suggested as a review to hasty susan greenfield essay. Two studies explicitly refer to this approach.
This approach seems likely to have increased precision at the expense of sensitivity. Dealing with imbalanced datasets At the title and abstract screening literature of a typical systematic review, the dataset is imbalanced in that there are usually far more excluded studies than included studies.
One paper reported a median search precision number of included studies divided by total number of items located through searching of 2. This translates to an imbalance in which there are approximately Search text can be much less than this, resulting in literature greater imbalances.