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Natural Language Processing for the Identification of Silent Brain Infarcts From Neuroimaging Reports
Fu S, Leung LY, Wang Y, Raulli AO, Kallmes DF, Kinsman KA, Nelson KB, Clark MS et al.. JMIR medical informatics, 2019-04-21
Topic: Brain proteins, microglia and lab models
Features for rule-based systems, including significant words and patterns related to SBI, were generated using pointwise mutual information. The performance of the NLP algorithm was compared with a manually created gold standard. Results Among the 400 reports selected to determine interannotator agreement, 5 reports were removed due to invalid scan types.
Key findings
- Features for rule-based systems, including significant words and patterns related to SBI, were generated using pointwise mutual information.
- The performance of the NLP algorithm was compared with a manually created gold standard.
- Results Among the 400 reports selected to determine interannotator agreement, 5 reports were removed due to invalid scan types.
Why it matters
This work developed a natural language processing method to identify silent brain infarcts from neuroimaging reports, using features generated by pointwise mutual information and compared against a manually created gold standard. It is about detecting a related brain condition from reports and does not test any treatment, cure or prevention for Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 42/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (JMIR medical informatics) |
| Journal's citation record | 1 / 4 | JMIR Medical Informatics: journal h-index 82 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 62 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 6.9 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 3 / 6 | Cited 4.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 2 / 4 | Cited 17 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Hongfang Liu: h-index 59 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Mayo Clinic: institution h-index 1,606 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31066686) |
| Free to read | 2 / 2 | Free to read |
| Total | 42 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Fu, S., Leung, L. Y., Wang, Y., Raulli, A. O., Kallmes, D. F., Kinsman, K. A., et al. (2019). Natural Language Processing for the Identification of Silent Brain Infarcts From Neuroimaging Reports. JMIR medical informatics. https://doi.org/10.2196/12109
Vancouver
Fu S, Leung LY, Wang Y, Raulli AO, Kallmes DF, Kinsman KA, et al. Natural Language Processing for the Identification of Silent Brain Infarcts From Neuroimaging Reports. JMIR medical informatics. 2019. doi:10.2196/12109. PMID: 31066686.
BibTeX
@article{fu2019-31066686,
author = {Fu, S. and Leung, L. Y. and Wang, Y. and Raulli, A. O. and Kallmes, D. F. and Kinsman, K. A. and Nelson, K. B. and Clark, M. S. and others},
title = {{Natural Language Processing for the Identification of Silent Brain Infarcts From Neuroimaging Reports}},
journal = {JMIR medical informatics},
year = {2019},
doi = {10.2196/12109},
pmid = {31066686},
url = {https://doi.org/10.2196/12109}
}
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