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.