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Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data
Kang MJ, Kim SY, Na DL, Kim BC, Yang DW, Kim EJ, Na HR, Han HJ et al.. BMC medical informatics and decision making, 2019-11-21
Topic: Risk, prevention and care in population studies
Background Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data. Methods Multi-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimer's disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination. Results The ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The 'time orientation' and '3-word recall' score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment. Conclusions The machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting.
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APA 7th
Kang, M. J., Kim, S. Y., Na, D. L., Kim, B. C., Yang, D. W., Kim, E. J., et al. (2019). Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data. BMC medical informatics and decision making. https://doi.org/10.1186/s12911-019-0974-x
Vancouver
Kang MJ, Kim SY, Na DL, Kim BC, Yang DW, Kim EJ, et al. Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data. BMC medical informatics and decision making. 2019. doi:10.1186/s12911-019-0974-x. PMID: 31752864.
BibTeX
@article{kang2019-31752864,
author = {Kang, M. J. and Kim, S. Y. and Na, D. L. and Kim, B. C. and Yang, D. W. and Kim, E. J. and Na, H. R. and Han, H. J. and others},
title = {{Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data}},
journal = {BMC medical informatics and decision making},
year = {2019},
doi = {10.1186/s12911-019-0974-x},
pmid = {31752864},
url = {https://doi.org/10.1186/s12911-019-0974-x}
}
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Key findings
- Results The ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%.
Why it matters
A machine learning model trained on multi-center neuropsychological test data identified cognitive impairment and distinguished normal cognition, mild cognitive impairment and Alzheimer's disease dementia with high accuracy. This could help streamline diagnosis, but it does not provide a treatment, cure or prevention.
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Study details
- Effect size0.52%; 0.32%; 1.03%
- Evidence strengthLow
How this study scored 45/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, 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 (BMC medical informatics and decision making) |
| Journal's citation record | 1 / 4 | BMC Medical Informatics and Decision Making: journal h-index 145 (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 | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 69 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 7.7 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 3 / 6 | Cited 5.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 2 / 4 | Cited 26 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Young Chul Youn: h-index 36 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Seoul National University: institution h-index 1,053 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 31 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31752864) |
| Free to read | 2 / 2 | Free to read |
| Total | 45 / 100 | — |
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Read the original
- DOI: 10.1186/s12911-019-0974-x
- PubMed: 31752864
- Original source →
- Free full text: BMC Medical Informatics and Decision Making →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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