Article
Calibrating Mini-Mental State Examination Scores to Predict
Misdiagnosed Dementia Patients
Akhilesh Vyas
1,
* , Fotis Aisopos
2
, Maria-Esther Vidal
1,3
, Peter Garrard
4
and George Paliouras
2
Citation: Vyas, A.; Aisopos, F.; Vidal,
M.-E.; Garrard, P.; Paliouras, G.
Calibrating Mini-Mental State
Examination Scores to Predict
Misdiagnosed Dementia Patients.
Appl. Sci. 2021, 11, 8055. https://
doi.org/10.3390/app11178055
Academic Editor: Keun Ho Ryu
Received: 29 July 2021
Accepted: 25 August 2021
Published: 30 August 2021
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1
L3S Research Center, Leibniz University of Hannover, 30167 Hannover, Germany; Maria.Vidal@tib.eu
2
National Centre for Scientific Research “Demokritos”, Institute of Informatics & Telecommunications,
15341 Athens, Greece; fotis.aisopos@iit.demokritos.gr (F.A.); paliourg@iit.demokritos.gr (G.P.)
3
TIB-Leibniz Information for Centre for Science and Technology, 30167 Hannover, Germany
4
Neuroscience Research Centre, Molecular and Clinical Sciences Research Institute,
St George’s University of London , London SW17 0RE, UK; pgarrard@sgul.ac.uk
* Correspondence: akhilesh.vyas@tib.eu
Abstract:
Mini-Mental State Examination (MMSE) is used as a diagnostic test for dementia to screen a
patient’s cognitive assessment and disease severity. However, these examinations are often inaccurate
and unreliable either due to human error or due to patients’ physical disability to correctly interpret
the questions as well as motor deficit. Erroneous data may lead to a wrong assessment of a specific
patient. Therefore, other clinical factors (e.g., gender and comorbidities) existing in electronic health
records, can also play a significant role, while reporting her examination results. This work considers
various clinical attributes of dementia patients to accurately determine their cognitive status in
terms of the Mini-Mental State Examination (MMSE) Score. We employ machine learning models
to calibrate MMSE score and classify the correctness of diagnosis among patients, in order to assist
clinicians in a better understanding of the progression of cognitive impairment and subsequent
treatment. For this purpose, we utilize a curated real-world ageing study data. A random forest
prediction model is employed to estimate the Mini-Mental State Examination score, related to the
diagnostic classification of patients.This model uses various clinical attributes to provide accurate
MMSE predictions, succeeding in correcting an important percentage of cases that contain previously
identified miscalculated scores in our dataset. Furthermore, we provide an effective classification
mechanism for automatically identifying patient episodes with inaccurate MMSE values with high
confidence. These tools can be combined to assist clinicians in automatically finding episodes within
patient medical records where the MMSE score is probably miscalculated and estimating what
the correct value should be. This provides valuable support in the decision making process for
diagnosing potential dementia patients.
Keywords:
dementia; mini mental score examination; machine learning; classification; regression;
random forest; predictive models
1. Introduction
There are more than 8.7 million people across Europe living with dementia (Mapping-
dementia-friendly-communities-across-europe https://ec.europa.eu/eip/ageing/library/
mapping-dementia-friendly-communities-across-europe_en.html (accessed on 28 July 2021)).
With an ageing population and no effective treatment, this number is set to rise to 152 mil-
lion globally by 2050 (Dementia-number-of-people-affected-to-triple-in-next-30-years https:
//www.who.int/news/item/07-12-2017-dementia-number-of-people-affected-to-triple-in-
next-30-years (accessed on 28 July 2021)), highlighting the huge unmet need for better
managing this condition [
1
]. The sheer number of people living with the disease and the
direct and indirect costs of providing care and support for them have made dementia
quite challenging.
Appl. Sci. 2021, 11, 8055. https://doi.org/10.3390/app11178055 https://www.mdpi.com/journal/applsci
