PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-6 (6)
 

Clipboard (0)
None
Journals
Authors
more »
Year of Publication
Document Types
1.  An evidential reasoning based model for diagnosis of lymph node metastasis in gastric cancer 
Background
Lymph node metastasis (LNM) in gastric cancer is a very important prognostic factor affecting long-term survival. Currently, several common imaging techniques are used to evaluate the lymph node status. However, they are incapable of achieving both high sensitivity and specificity simultaneously. In order to deal with this complex issue, a new evidential reasoning (ER) based model is proposed to support diagnosis of LNM in gastric cancer.
Methods
There are 175 consecutive patients who went through multidetector computed tomography (MDCT) consecutively before the surgery. Eight indicators, which are serosal invasion, tumor classification, tumor enhancement pattern, tumor thickness, number of lymph nodes, maximum lymph node size, lymph node station and lymph node enhancement are utilized to evaluate the tumor and lymph node through CT images. All of the above indicators reflect the biological behavior of gastric cancer. An ER based model is constructed by taking the above indicators as input index. The output index determines whether LNM occurs for the patients, which is decided by the surgery and histopathology. A technique called k-fold cross-validation is used for training and testing the new model. The diagnostic capability of LNM is evaluated by receiver operating characteristic (ROC) curves. A Radiologist classifies LNM by adopting lymph node size for comparison.
Results
134 out of 175 cases are cases of LNM, and the remains are not. Eight indicators have statistically significant difference between the positive and negative groups. The sensitivity, specificity and AUC of the ER based model are 88.41%, 77.57% and 0.813, respectively. However, for the radiologist evaluating LNM by maximum lymph node size, the corresponding values are only 63.4%, 75.6% and 0.757. Therefore, the proposed model can obtain better performance than the radiologist. Besides, the proposed model also outperforms other machine learning methods.
Conclusions
According to the biological behavior information of gastric cancer, the ER based model can diagnose LNM effectively and preoperatively.
doi:10.1186/1472-6947-13-123
PMCID: PMC3827004  PMID: 24195733
Gastric cancer; Lymph node metastasis; Evidential reasoning
2.  An update on Uniform Resource Locator (URL) decay in MEDLINE abstracts and measures for its mitigation 
Background
For years, Uniform Resource Locator (URL) decay or "link rot" has been a growing concern in the field of biomedical sciences. This paper addresses this issue by examining the status of the URLs published in MEDLINE abstracts, establishing current availability and estimating URL decay in these records from 1994 to 2006. We also reviewed the information provided by the URL to determine if the context that the author cited in writing the paper is the same information presently available in the URL. Lastly, with all the documented recommended methods to preserve URL links, we determined which among them have gained acceptance among authors and publishers.
Methods
MEDLINE records from 1994 to 2006 from the National Library of Medicine in Extensible Mark-up Language (XML) format were processed yielding 10,208 URL addresses. These were accessed once daily at random times for 30 days. Titles and abstracts were also searched for the presence of archival tools such as WebCite, Persistent URL (PURL) and Digital Object Identifier (DOI).
Results
Results showed that the average URL length ranged from 13 to 425 characters with a mean length of 35 characters [Standard Deviation (SD) = 13.51; 95% confidence interval (CI) 13.25 to 13.77]. The most common top-level domains were ".org" and ".edu", each with 34%. About 81% of the URL pool was available 90% to 100% of the time, but only 78% of these contained the actual information mentioned in the MEDLINE record. "Dead" URLs constituted 16% of the total. Finally, a survey of archival tool usage showed that since its introduction in 1998, only 519 of all abstracts reviewed had incorporated DOI addresses in their MEDLINE abstracts.
Conclusion
URL persistence parallels previous studies which showed approximately 81% general availability during the 1-month study period. As peer-reviewed literature remains to be the main source of information in biomedicine, we need to ensure the accuracy and preservation of these links.
doi:10.1186/1472-6947-8-23
PMCID: PMC2435527  PMID: 18547428
3.  Evidence-based medicine among internal medicine residents in a community hospital program using smart phones 
Background
This study implemented and evaluated a point-of-care, wireless Internet access using smart phones for information retrieval during daily clinical rounds and academic activities of internal medicine residents in a community hospital. We did the project to assess the feasibility of using smart phones as an alternative to reach online medical resources because we were unable to find previous studies of this type. In addition, we wanted to learn what Web-based information resources internal medicine residents were using and whether providing bedside, real-time access to medical information would be perceived useful for patient care and academic activities.
Methods
We equipped the medical teams in the hospital wards with smart phones (mobile phone/PDA hybrid devices) to provide immediate access to evidence-based resources developed at the National Library of Medicine as well as to other medical Websites. The emphasis of this project was to measure the convenience and feasibility of real-time access to current medical literature using smart phones.
Results
The smart phones provided real-time mobile access to medical literature during daily rounds and clinical activities in the hospital. Physicians found these devices easy to use. A post-study survey showed that the information retrieved was perceived to be useful for patient care and academic activities.
Conclusion
In community hospitals and ambulatory clinics without wireless networks where the majority of physicians work, real-time access to current medical literature may be achieved through smart phones. Immediate availability of reliable and updated information obtained from authoritative sources on the Web makes evidence-based practice in a community hospital a reality.
doi:10.1186/1472-6947-7-5
PMCID: PMC1805745  PMID: 17313680
4.  A UMLS-based spell checker for natural language processing in vaccine safety 
Background
The Institute of Medicine has identified patient safety as a key goal for health care in the United States. Detecting vaccine adverse events is an important public health activity that contributes to patient safety. Reports about adverse events following immunization (AEFI) from surveillance systems contain free-text components that can be analyzed using natural language processing. To extract Unified Medical Language System (UMLS) concepts from free text and classify AEFI reports based on concepts they contain, we first needed to clean the text by expanding abbreviations and shortcuts and correcting spelling errors. Our objective in this paper was to create a UMLS-based spelling error correction tool as a first step in the natural language processing (NLP) pipeline for AEFI reports.
Methods
We developed spell checking algorithms using open source tools. We used de-identified AEFI surveillance reports to create free-text data sets for analysis. After expansion of abbreviated clinical terms and shortcuts, we performed spelling correction in four steps: (1) error detection, (2) word list generation, (3) word list disambiguation and (4) error correction. We then measured the performance of the resulting spell checker by comparing it to manual correction.
Results
We used 12,056 words to train the spell checker and tested its performance on 8,131 words. During testing, sensitivity, specificity, and positive predictive value (PPV) for the spell checker were 74% (95% CI: 74–75), 100% (95% CI: 100–100), and 47% (95% CI: 46%–48%), respectively.
Conclusion
We created a prototype spell checker that can be used to process AEFI reports. We used the UMLS Specialist Lexicon as the primary source of dictionary terms and the WordNet lexicon as a secondary source. We used the UMLS as a domain-specific source of dictionary terms to compare potentially misspelled words in the corpus. The prototype sensitivity was comparable to currently available tools, but the specificity was much superior. The slow processing speed may be improved by trimming it down to the most useful component algorithms. Other investigators may find the methods we developed useful for cleaning text using lexicons specific to their area of interest.
doi:10.1186/1472-6947-7-3
PMCID: PMC1805499  PMID: 17295907
5.  SLIM: an alternative Web interface for MEDLINE/PubMed searches – a preliminary study 
Background
With the rapid growth of medical information and the pervasiveness of the Internet, online search and retrieval systems have become indispensable tools in medicine. The progress of Web technologies can provide expert searching capabilities to non-expert information seekers. The objective of the project is to create an alternative search interface for MEDLINE/PubMed searches using JavaScript slider bars. SLIM, or Slider Interface for MEDLINE/PubMed searches, was developed with PHP and JavaScript. Interactive slider bars in the search form controlled search parameters such as limits, filters and MeSH terminologies. Connections to PubMed were done using the Entrez Programming Utilities (E-Utilities). Custom scripts were created to mimic the automatic term mapping process of Entrez. Page generation times for both local and remote connections were recorded.
Results
Alpha testing by developers showed SLIM to be functionally stable. Page generation times to simulate loading times were recorded the first week of alpha and beta testing. Average page generation times for the index page, previews and searches were 2.94 milliseconds, 0.63 seconds and 3.84 seconds, respectively. Eighteen physicians from the US, Australia and the Philippines participated in the beta testing and provided feedback through an online survey. Most users found the search interface user-friendly and easy to use. Information on MeSH terms and the ability to instantly hide and display abstracts were identified as distinctive features.
Conclusion
SLIM can be an interactive time-saving tool for online medical literature research that improves user control and capability to instantly refine and refocus search strategies. With continued development and by integrating search limits, methodology filters, MeSH terms and levels of evidence, SLIM may be useful in the practice of evidence-based medicine.
doi:10.1186/1472-6947-5-37
PMCID: PMC1318459  PMID: 16321145
6.  askMEDLINE: a free-text, natural language query tool for MEDLINE/PubMed 
Background
Plain language search tools for MEDLINE/PubMed are few. We wanted to develop a search tool that would allow anyone using a free-text, natural language query and without knowing specialized vocabularies that an expert searcher might use, to find relevant citations in MEDLINE/PubMed. This tool would translate a question into an efficient search.
Results
The accuracy and relevance of retrieved citations were compared to references cited in BMJ POEMs and CATs (critically appraised topics) questions from the University of Michigan Department of Pediatrics. askMEDLINE correctly matched the cited references 75.8% in POEMs and 89.2 % in CATs questions on first pass. When articles that were deemed to be relevant to the clinical questions were included, the overall efficiency in retrieving journal articles was 96.8% (POEMs) and 96.3% (CATs.)
Conclusion
askMEDLINE might be a useful search tool for clinicians, researchers, and other information seekers interested in finding current evidence in MEDLINE/PubMed. The text-only format could be convenient for users with wireless handheld devices and those with low-bandwidth connections in remote locations.
doi:10.1186/1472-6947-5-5
PMCID: PMC1079856  PMID: 15760470

Results 1-6 (6)