scholarly journals Random Forest Model in the Diagnosis of Dementia Patients with Normal Mini-Mental State Examination Scores

2022 ◽  
Vol 12 (1) ◽  
pp. 37
Author(s):  
Jie Wang ◽  
Zhuo Wang ◽  
Ning Liu ◽  
Caiyan Liu ◽  
Chenhui Mao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Random Forest ◽  
Mental State ◽  
Mini Mental State Examination ◽  
Machine Learning Algorithms ◽  
Assessment Model ◽  
Test Time ◽  
Cognitive Screening ◽  
State Examination

Background: Mini-Mental State Examination (MMSE) is the most widely used tool in cognitive screening. Some individuals with normal MMSE scores have extensive cognitive impairment. Systematic neuropsychological assessment should be performed in these patients. This study aimed to optimize the systematic neuropsychological test battery (NTB) by machine learning and develop new classification models for distinguishing mild cognitive impairment (MCI) and dementia among individuals with MMSE ≥ 26. Methods: 375 participants with MMSE ≥ 26 were assigned a diagnosis of cognitively unimpaired (CU) (n = 67), MCI (n = 174), or dementia (n = 134). We compared the performance of five machine learning algorithms, including logistic regression, decision tree, SVM, XGBoost, and random forest (RF), in identifying MCI and dementia. Results: RF performed best in identifying MCI and dementia. Six neuropsychological subtests with high-importance features were selected to form a simplified NTB, and the test time was cut in half. The AUC of the RF model was 0.89 for distinguishing MCI from CU, and 0.84 for distinguishing dementia from nondementia. Conclusions: This simplified cognitive assessment model can be useful for the diagnosis of MCI and dementia in patients with normal MMSE. It not only optimizes the content of cognitive evaluation, but also improves diagnosis and reduces missed diagnosis.

Comparison of the Mini-Mental State Examination and Computerized Brief Cognitive Screening Test as Cognitive Screening Tools in Patients with Mild Cognitive Impairment

2021 ◽  
Vol 18 ◽  
Author(s):  
Che-Sheng Chu ◽  
I-Chen Lee ◽  
Chuan-Cheng Hung ◽  
I-Ching Lee ◽  
Chi-Fa Hung ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Normal Control ◽  
Internal Consistency ◽  
Mental State ◽  
Screening Test ◽  
Mini Mental State Examination ◽  
Cognitive Screening ◽  
Validity And Reliability ◽  
State Examination

Background: The aim of this study was to establish the validity and reliability of the Computerized Brief Cognitive Screening Test (CBCog) for early detection of cognitive impairment. Method: One hundred and sixty participants, including community-dwelling and out-patient volunteers (both men and women) aged ≥ 65 years, were enrolled in the study. All participants were screened using the CBCog and Mini-Mental State Examination (MMSE). The internal consistency of the CBCog was analyzed using Cronbach’s α test. Areas under the curves (AUCs) of receiver operating characteristic analyses were used to test the predictive accuracy of the CBCog in detecting mild cognitive impairment (MCI) in order to set an appropriate cutoff point. Results: The CBCog scores were positively correlated with the MMSE scores of patients with MCI-related dementia (r = 0.678, P < .001). The internal consistency of the CBCog (Cronbach’s α) was 0.706. It was found that the CBCog with a cutoff point of 19/20 had a sensitivity of 97.5% and a specificity of 53.7% for the diagnosis of MCI with education level ≥ 6 years. The AUC of the CBCog for discriminating the normal control elderly from patients with MCI (AUC = 0.827, P < 0.001) was larger than that of the MMSE for discriminating the normal control elderly from patients with MCI (AUC= 0.819, P < .001). Conclusion: The CBCog demonstrated to have sufficient validity and reliability to evaluate mild cognitive impairment, especially in highly educated elderly people.

Machine Learning Algorithms in the Prediction of Early Death in Elderly Cancer Patients (Preprint)

2019 ◽  
Author(s):  
Gabrielle Sena ◽  
Tiago Pessoa Lima ◽  
Jurema Telles Lima ◽  
Maria Julia Mello ◽  
Luiz Claudio Thuler
Keyword(s):  
Machine Learning ◽  
Cancer Patients ◽  
Mental State ◽  
Mini Mental State Examination ◽  
Early Death ◽  
Machine Learning Algorithms ◽  
Karnofsky Performance Scale ◽  
Elderly Cancer Patients ◽  
State Examination ◽  
Performance Scale

BACKGROUND The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of Machine Learning (ML) algorithms. The International Society of Geriatric Oncology (SIOG) recommends the use of the Comprehensive Geriatric Assessment (CGA), a multidisciplinary tool to evaluate health domains, for the follow-up of elderly cancer patients. However, none ML application have been proposed using CGA to classify elderly cancer patients. OBJECTIVE To propose and develop predictive models, using ML and CGA, to estimate the risk of early death in elderly cancer patients. METHODS The ability of ML algorithms to predict early mortality in a cohort involving 608 elderly cancer patients was evaluated. The CGA was conducted during admission by a multidisciplinary team and included the following questionnaires: Mini-Mental State Examination, Geriatric Depression Scale, International Physical Activity Questionnaire, Timed Get-Up and Go, Katz Index, Charlson Comorbidity Index, Karnofsky Performance Scale, Polypharmacy, Mini Nutritional Assessment. The K-fold Cross Validation algorithm was used to evaluate all possible combinations of these questionnaires to estimate the risk of early death, considered when occurring within six months of diagnosis, in a variety of ML classifiers, including Naive Bayes (NB), Decision Tree (J48) and Multilayer Perceptron (MLP). On each fold of evaluation, tie-breaking is handled by choosing the smallest set of questionnaires. RESULTS It was possible to select CGA questionnaire subsets with high predictive capacity for early death, either statistically similar (NB) or higher (J48 and MLP) compared to the use of all questionnaires investigated. These results show that CGA questionnaire selection can improve accuracy rates and decrease the time spent to evaluate elderly cancer patients. The only questionnaire selected in all folds was the Mini Nutrition Evaluation. The Karnofsky Performance Scale was selected in all folds by the NB and MLP, while the Mini Mental State Examination was selected in all folds by the NB. CONCLUSIONS A simplified predictive model aiming to estimate the risk of early death in elderly cancer patients is proposed herein, minimally composed by the Mini Nutrition Evaluation accompanied or not by the Karnofsky Performance Scale and/or the Mini-Mental State Examination.

Comparison of the Greek Version of the Quick Mild Cognitive Impairment Screen and Standardised Mini-Mental State Examination

2020 ◽  
Author(s):  
Lambros Messinis ◽  
Mark R O’Donovan ◽  
D William Molloy ◽  
Antonis Mougias ◽  
A Grigorios Nasios ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Mental State ◽  
Mini Mental State Examination ◽  
Area Under The Curve ◽  
Cognitive Screening ◽  
Subjective Memory ◽  
Mild Dementia ◽  
Similar Accuracy ◽  
State Examination

Abstract Introduction Short cognitive screening instruments (CSIs) are widely used to stratify patients presenting with cognitive symptoms. The Quick Mild Cognitive Impairment (Qmci) screen is a new, brief (&lt;5mins) CSI designed to identify mild cognitive impairment (MCI), which can be used across the spectrum of cognitive decline. Here we present the translation of the Qmci into Greek (Qmci-Gr) and its validation against the widely-used Standardised Mini-Mental State Examination (SMMSE). Methods Consecutive patients aged ≥55 years presenting with cognitive complaints were recruited from two outpatient clinics in Greece. All patients completed the Qmci-Gr and SMMSE and underwent an independent detailed neuropsychological assessment to determine a diagnostic classification. Results In total, 140 patients, median age 75 years, were included; 30 with mild dementia (median SMMSE 23/30), 76 with MCI and 34 with subjective memory complaints (SMC) but normal cognition. The Qmci-Gr had similar accuracy in differentiating SMC from cognitive impairment (MCI & mild dementia) compared with SMMSE, area under the curve (AUC) of 0.84 versus 0.79, respectively; while accuracy was higher for the Qmci-Gr, this finding was not significantly different, (p = .19). Similarly, the Qmci-Gr had similar accuracy in separating SMC from MCI, AUC of 0.79 versus 0.73 (p = .23). Conclusions The Qmci-Gr compared favorably with the SMMSE. Further research with larger samples and comparison with other instruments such as the Montreal Cognitive Assessment is needed to confirm these findings but given its established brevity, it may be a better choice in busy clinical practice in Greece.

Mini-Mental State Examination (MMSE) for the early detection of dementia in people with mild cognitive impairment (MCI)

2021 ◽  
Vol 2021 (7) ◽  
Author(s):  
Ingrid Arevalo-Rodriguez ◽  
Nadja Smailagic ◽  
Marta Roqué-Figuls ◽  
Agustín Ciapponi ◽  
Erick Sanchez-Perez ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Mental State ◽  
Mini Mental State Examination ◽  
State Examination

scholarly journals Preoperative Cognitive Impairment as a Predictor of Postoperative Outcomes in Elderly Patients Undergoing Spinal Surgery for Degenerative Spinal Disease

2021 ◽  
Vol 10 (7) ◽  
pp. 1385
Author(s):  
Hyung Cheol Kim ◽  
Seong Bae An ◽  
Hyeongseok Jeon ◽  
Tae Woo Kim ◽  
Jae Keun Oh ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Elderly Patients ◽  
Mental State ◽  
Mini Mental State Examination ◽  
Operation Time ◽  
Cognitive Status ◽  
Blood Analysis ◽  
Spinal Disease ◽  
Degenerative Spinal Disease ◽  
State Examination

Cognitive status has been reported to affect the peri-operative and post-operative outcomes of certain surgical procedures. This prospective study investigated the effect of preoperative cognitive impairment on the postoperative course of elderly patients (n = 122, >65 years), following spine surgery for degenerative spinal disease. Data on demographic characteristics, medical history, and blood analysis results were collected. Preoperative cognition was assessed using the mini-mental state examination, and patients were divided into three groups: normal cognition, mild cognitive impairment, and moderate-to-severe cognitive impairment. Discharge destinations (p = 0.014) and postoperative cardiopulmonary complications (p = 0.037) significantly differed based on the cognitive status. Operation time (p = 0.049), white blood cell count (p = 0.022), platelet count (p = 0.013), the mini-mental state examination score (p = 0.033), and the Beck Depression Inventory score (p = 0.041) were significantly associated with the length of hospital stay. Our investigation demonstrated that improved understanding of preoperative cognitive status may be helpful in surgical decision-making and postoperative care of elderly patients with degenerative spinal disease.

scholarly journals Can the MMSE help clinicians predict progression from mild cognitive impairment to dementia?

2015 ◽  
Vol 21 (6) ◽  
pp. 363-366 ◽  
Author(s):  
Alex J. Mitchell
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Risk Prediction ◽  
Mental State ◽  
Mini Mental State Examination ◽  
Case Finding ◽  
Cognitive Test ◽  
Prediction Tool ◽  
State Examination

SummaryThe Mini-Mental State Examination (MMSE) is the most widely used bedside cognitive test. It has previously been shown to be poor as a case-finding tool for both dementia and mild cognitive impairment (MCI). This month's Cochrane Corner review examines whether the MMSE might be used as a risk prediction tool for later dementia in those with established MCI. From 11 studies of modest quality, it appears that the MMSE alone should not be relied on to predict later deterioration in people with MCI. As this is the case, it is likely that only a combination of predictors would be able to accurately predict progression from MCI to dementia.

scholarly journals Detection of mild cognitive impairment in middle-aged and older adults with obstructive sleep apnoea

2018 ◽  
Vol 52 (5) ◽  
pp. 1801137 ◽  
Author(s):  
Katia Gagnon ◽  
Andrée-Ann Baril ◽  
Jacques Montplaisir ◽  
Julie Carrier ◽  
Sirin Chami ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Obstructive Sleep Apnoea ◽  
Mental State ◽  
Cognitive Assessment ◽  
Mini Mental State Examination ◽  
Sleep Apnoea ◽  
Screening Tests ◽  
Obstructive Sleep ◽  
State Examination

Obstructive sleep apnoea increases the risk for mild cognitive impairment and dementia. The present study aimed to characterise the ability of two cognitive screening tests, the Mini-Mental State Examination and the Montreal Cognitive Assessment, to detect mild cognitive impairment in adults aged 55–85 years with and without obstructive sleep apnoea.We included 42 subjects with mild and 67 subjects with moderate-to-severe obstructive sleep apnoea. We compared them to 22 control subjects. Mild cognitive impairment was diagnosed by a comprehensive neuropsychological assessment. We used receiver operating characteristic curves to assess the ability of the two screening tests to detect mild cognitive impairment.The two screening tests showed similar discriminative ability in control subjects. However, among the mild and the moderate-to-severe obstructive sleep apnoea groups, the Mini-Mental State Examination was not able to correctly identify subjects with mild cognitive impairment. The Montreal Cognitive Assessment's discriminant ability was acceptable in both sleep apnoea groups and was comparable to what was observed in controls.The Mini-Mental State Examination should not be used to screen for cognitive impairment in patients with obstructive sleep apnoea. The Montreal Cognitive Assessment could be used in clinical settings. However, clinicians should refer patients for neuropsychological assessment when neurodegenerative processes are suspected.

scholarly journals The Mini Mental State Examination does not accurately screen for objective cognitive impairment in Fabry Disease

JIMD Reports ◽  
2019 ◽  
Vol 48 (1) ◽  
pp. 53-59
Author(s):  
Simon Körver ◽  
Sara A. J. Schraaf ◽  
Gert J. Geurtsen ◽  
Carla E. M. Hollak ◽  
Ivo N. Schaik ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Fabry Disease ◽  
Mental State ◽  
Mini Mental State Examination ◽  
State Examination

scholarly journals Sleep Disorders and Cognitive Impairment in Peritoneal Dialysis: A Multicenter Prospective Cohort Study

2019 ◽  
Vol 44 (5) ◽  
pp. 1115-1127 ◽  
Author(s):  
Youlu Zhao ◽  
Yuhui Zhang ◽  
Zhikai Yang ◽  
Jinwei Wang ◽  
Zuying Xiong ◽  
...  
Keyword(s):  
Cohort Study ◽  
Cognitive Impairment ◽  
Peritoneal Dialysis ◽  
Cognitive Function ◽  
Sleep Disorders ◽  
Mental State ◽  
Mini Mental State Examination ◽  
Multicenter Prospective Cohort Study ◽  
State Examination ◽  
Multicenter Prospective Cohort

Background: Patients with chronic kidney disease experience a high burden of sleep disorders, and there are associations between sleep disorders and cognitive impairment. Objectives: Based on our previous cross-sectional survey on cognitive impairment in peritoneal dialysis, we further explored the relationship between sleep disorders and cognitive impairment, and predictors for declining cognitive function. Method: We conducted a multicenter prospective cohort study enrolling 458 clinically stable patients on peritoneal dialysis who were then followed up for 2 years.Demographic data, comorbidities, depression, and biochemistry data were collected at baseline. Sleep disorders including insomnia, restless legs syndrome, sleep apnea syndrome, excessive daytime sleepiness, possible narcolepsy, sleep walking and nightmares, and possible rapid eye movement behavior disorders were assessed using a panel of specific sleep questionnaires at baseline and in a second survey. Global cognitive function was measured at baseline and in a second survey, using the Modified Mini-Mental State Examination. Specific cognitive domains were evaluated using Trail-Making Test Forms A and B for executive function, and subtests of the Battery for the Assessment of Neuropsychological Status were used to asses immediate and delayed memory, visuospatial skills, and language ability. Results: Sleep disorders were common among peritoneal dialysis patients. The prevalence of cognitive impairment evaluated by the Modified Mini-Mental State Examination (3MS) increased from 19.8 to 23.9%. Possible narcolepsy was associated with decreased Modified Mini-Mental State Examination scores at baseline. During follow-up, sleepwalking and nightmares were associated with higher risks of declined delayed memory in the longitudinal study. Conclusions: Possible narcolepsy was associated with general cognitive dysfunction, and sleep walking and nightmares were risk factors for impaired delayed memory.

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