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Mild Cognitive Impairment: Data-Driven Prediction, Risk Factors, and Workup
Fouladvand S, Noshad M, Goldstein MK, Periyakoil VJ, Chen JH.. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2023-06-16
Topic: Risk, prevention and care in population studies
Over 78 million people will suffer from dementia by 2030, emphasizing the need for early identification of patients with mild cognitive impairment (MCI) at risk, and personalized clinical evaluation steps to diagnose potentially reversible causes. Here, we leverage real-world electronic health records in the observational medical outcomes partnership (OMOP) data model to develop machine learning models to predict MCI up to a year in advance of recorded diagnosis. Our experimental results with logistic regression, random forest, and xgboost models trained and evaluated on more than 531K patient visits show random forest model can predict MCI onset with ROC-AUC of 68.2±0.7. We identify the clinical factors mentioned in clinician notes that are most predictive of MCI. Using similar association mining techniques, we develop a data-driven list of clinical procedures commonly ordered in the workup of MCI cases, that could be used as a basis for guidelines and clinical order set templates.
Key findings
- Our experimental results with logistic regression, random forest, and xgboost models trained and evaluated on more than 531K patient visits show random forest model can predict MCI onset with ROC-AUC of 68.2±0.7.
Why it matters
This study used machine learning on electronic health records to try to predict mild cognitive impairment up to a year before it was diagnosed, achieving a prediction accuracy (ROC-AUC) of 68.2±0.7. It also produced a data-driven list of clinical procedures commonly ordered during MCI workups, which could help guide clinical guidelines and order sets.
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Study details
- Evidence strengthLow
How this study scored 40/100
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. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 23 of the 58 points we could judge it on, which is 40 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 3 / 5 | 2023 — within 6 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 | — / 7 | No citation count (OpenAlex dates this DOI to 2016, the record says 2023; OpenAlex values not used) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Library citations not trusted (see above) — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 37350911) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 23 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 23 of the 58 points we could judge it on is 40 out of 100. |
| Total | 40 / 100 | — |
What each of these 20 components measures, and what this score is not →
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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. Check the citation against the original before you use it.
APA 7th
Fouladvand, S., Noshad, M., Goldstein, M. K., Periyakoil, V. J., & Chen, J. H. (2023). Mild Cognitive Impairment: Data-Driven Prediction, Risk Factors, and Workup. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. https://doi.org/10.1145/2939672.2939785
Vancouver
Fouladvand S, Noshad M, Goldstein MK, Periyakoil VJ, Chen JH. Mild Cognitive Impairment: Data-Driven Prediction, Risk Factors, and Workup. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. 2023. doi:10.1145/2939672.2939785. PMID: 37350911.
BibTeX
@article{fouladvand2023-37350911,
author = {Fouladvand, S. and Noshad, M. and Goldstein, M. K. and Periyakoil, V. J. and Chen, J. H.},
title = {{Mild Cognitive Impairment: Data-Driven Prediction, Risk Factors, and Workup}},
journal = {AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science},
year = {2023},
doi = {10.1145/2939672.2939785},
pmid = {37350911},
url = {https://doi.org/10.1145/2939672.2939785}
}
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