# Calibrating Mini-Mental State Examination Scores to Predict Misdiagnosed Dementia Patients
## 1 Introduction
While knowledge of these trends may be helpful to individual clinicians when asked for a prognosis, and to health economists, epidemiologists and policymakers when considering the burden of the disease on society, their applicability depends crucially on the accuracy of the recorded MMSE data. Such an assumption would, however, be unsafe given the tendency for such clinical instruments to be used and scored in different ways by different clinicians, not to mention for accidentally erroneous values to be entered in the record. Therefore, a challenge that arises in the interpretation of big data such as electronic patient records (EPR) is the identification of clinical episodes where the MMSE score has been incorrectly calculated or inaccurately recorded. Previous work [8] supports the assumption of a set of baseline rules for better identifying diagnostic inaccuracies, based on the differences between MMSE scores obtained on consecutive episodes of assessment. A more advanced approach, however, could attempt to identify an inaccurate MMSE using features associated with an individual episode. A further challenge is to also investigate whether the MMSE score values can be correctly and effectively predicted using information from electronic health records. This paper employs machine learning models to attempt an accurate MMSE score prediction, based on a set of clinical, molecular (e.g., APOE type), and demographic features associated with each patient episode. The main aim of this prediction is to determine whether instances of specious and/or inconsistent documentation of MMSE scores, which are frequently documented in clinical records, can be identified and corrected. In the setting of the current experiments, a patient's MMSE score was defined as erroneous if one of a number of conditions are met. These conditions, based on a priori assumptions derived from the clinical experience of one of the authors (PG), were as follows: if the change in MMSE value between two consecutive episodes of testing either (a) increased by more than five points during any interval up to a year, (b) increased by three or more points between the first and second years after diagnosis, or (c) increased by any number of pointsover an interval greater than 2 years, an erroneous MMSE score had been documented. The solution to the aforementioned problem entails two steps. First, these rules are used to isolate the assessments at which erroneous MMSE scores are recorded from those in which the value of the MMSE score is plausible. Secondly, to train and test an MMSE score prediction model to determine what proportion of miscalculated cases are replaced by a predicted MMSE value, and what proportion of the scores presumed to be correct are left unchanged by the prediction model. We hypothesise that the MMSE test and other clinical factors reported in electronic health records represent rich sources of information for cognitive assessment and AD severity. Built on this hypothesis, the predictive capacity of machine learning algorithms is exploited to forecast the MMSE value of a patient in a clinical episode and to classify if the patient is misdiagnosed or not. The accuracy of these models is experimentally assessed, and the observed results provide evidence of the importance that the clinical conditions have on the AD assessment. Further clinical interventions are required to validate the assumptions that guided our work. Nevertheless, the analytical validation based on traditional metrics that measure the accuracy of the predictive models, indicates that these results are promising and strengthen the premise that a holistic evaluation of a patient is required for an accurate diagnosis. Although several approaches in the literature report the use of machine learning on combined datasets that include MMSE test and other clinical factors [3, 9, 10, 11], to the best of our knowledge, the predictive problems stated in this paper have not modelled with machine learning models trained over the OPTIMA dataset [2, 12, 13].