Automatically extracting sentences from Medline citations to support clinicians' information needs

Jonnalagadda, Siddhartha; Fiol, Guilherme Del; Medlin, Richard; Weir, Charlene R.; Fiszman, Marcelo; Mostafa, Javed; Liu, Hongfang

doi:10.1136/amiajnl-2012-001347

Cited by 35 publications

(21 citation statements)

References 28 publications

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“…The third study generated AD-related proteins connectivity maps using protein interaction database and MEDLINE (56). The fourth study described a system which can identify highly relevant (84.5% accuracy) AD-related sentences from MEDLINE (57). …”

Section: Resultsmentioning

confidence: 99%

Advancing Alzheimer’s research: A review of big data promises

Zhang

Simon

2017

International Journal of Medical Informatics

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Objective To review the current state of science using big data to advance AD research and practice. In particular, we analyzed the types of research foci addressed and corresponding methods employed and study findings reported using big data in AD. Method Systematic review was conducted for articles published in PubMed from January 1, 2010 through December 31, 2015. Keywords with AD and big data analytics were used for literature retrieval. Articles were reviewed and included if they met the eligibility criteria. Results Thirty-eight articles were included in this review. They can be categorized into six research foci: diagnosing AD or mild cognitive impairment (MCI) (n=10), predicting MCI to AD conversion (n=13), stratifying risks for AD (n=5), mining the literature for knowledge discovery (n=4), predicting AD progression (n=2), describing clinical care for persons with AD (n=3) and understanding the relationship between cognition and AD (n=3). The most commonly used datasets are Alzheimer’s Disease Neuroimaging Initiative (ADNI) (n= 16), electronic health records (EHR) (n=11), MEDLINE (n=3), and other research datasets (n=8). Logistic regression (n=9) and support vector machine (n=8) are the most used methods for data analysis. Conclusion Big data are increasingly used to address AD related research questions. While existing research datasets are frequently used, other datasets such as EHR data provide a unique, yet under-tapped opportunity for advancing AD research.

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Section: Resultsmentioning

confidence: 99%

Advancing Alzheimer’s research: A review of big data promises

Zhang

Simon

2017

International Journal of Medical Informatics

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“…Features of the CKS user interface are described in Section 3. Details of the CKS architecture, the HL7 Infobutton Standard, and the summarization algorithms are described elsewhere [18–21,23–25]. …”

Section: Methodsmentioning

confidence: 99%

Formative evaluation of a patient-specific clinical knowledge summarization tool

Fiol

Mostafa

et al. 2016

International Journal of Medical Informatics

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Objective To iteratively design a prototype of a computerized clinical knowledge summarization (CKS) tool aimed at helping clinicians finding answers to their clinical questions; and to conduct a formative assessment of the usability, usefulness, efficiency, and impact of the CKS prototype on physicians’ perceived decision quality compared with standard search of UpToDate and PubMed. Materials and methods Mixed-methods observations of the interactions of 10 physicians with the CKS prototype vs. standard search in an effort to solve clinical problems posed as case vignettes. Results The CKS tool automatically summarizes patient-specific and actionable clinical recommendations from PubMed (high quality randomized controlled trials and systematic reviews) and UpToDate. Two thirds of the study participants completed 15 out of 17 usability tasks. The median time to task completion was less than 10 s for 12 of the 17 tasks. The difference in search time between the CKS and standard search was not significant (median = 4.9 vs. 4.5 min). Physician’s perceived decision quality was significantly higher with the CKS than with manual search (mean = 16.6 vs. 14.4; p = 0.036). Conclusions The CKS prototype was well-accepted by physicians both in terms of usability and usefulness. Physicians perceived better decision quality with the CKS prototype compared to standard search of PubMed and UpToDate within a similar search time. Due to the formative nature of this study and a small sample size, conclusions regarding efficiency and efficacy are exploratory.

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“…Jonnalagadda et al studied system construction to present important parts of structured abstracts using a medical thesaurus to identify information in the literature for the information needs of the clinician [1]. Structured abstracts are often used in the medical field, but are not used in many other fields.…”

Section: Related Workmentioning

confidence: 99%

Extraction of Key Expressions Indicating the Important Sentence from Article Abstracts

Otani

Tomiura

2014

2014 IIAI 3rd International Conference on Advanced Applied Informatics

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In this study, we aim to extract key expressions that indicate the important sentence describing the originalities or contributions from article abstracts. The expense of searching academic information increases because of increases in the number of articles, discipline subdivisions, and promotion of interdisciplinary research. Improving the extraction and presentation of the main points from article abstracts will contribute to reducing academic information search expenses. We extracted pseudo-important sentences from each article abstract based on the ratio of the number of words in the identified sentence that appear in the article title to the number of all words in the sentence. After that, we evaluated the ratio of the number of the pseudo-important sentences including each Ngram to the number of all sentences including that N-gram. We then extracted the N-grams with a ratio as high as the key expressions.

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Automatically extracting sentences from Medline citations to support clinicians' information needs

Cited by 35 publications

References 28 publications

Advancing Alzheimer’s research: A review of big data promises

Advancing Alzheimer’s research: A review of big data promises

Formative evaluation of a patient-specific clinical knowledge summarization tool

Extraction of Key Expressions Indicating the Important Sentence from Article Abstracts

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