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.

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.

scholarly journals Developing Machine Learning Algorithms for the Prediction of Early Death in Elderly Cancer Patients: Usability Study (Preprint)

2018 ◽  
Author(s):  
Gabrielle Ribeiro Sena ◽  
Tiago Pessoa Ferreira Lima ◽  
Maria Julia Gonçalves Mello ◽  
Luiz Claudio Santos Thuler ◽  
Jurema Telles Oliveira Lima
Keyword(s):  
Machine Learning ◽  
Cancer Patients ◽  
Geriatric Assessment ◽  
Short Form ◽  
Geriatric Oncology ◽  
Depression Scale ◽  
Mini Nutritional Assessment ◽  
Early Death ◽  
Machine Learning Algorithms ◽  
Elderly Cancer Patients

BACKGROUND The importance of classifying cancer patients into high- or low-risk groups has led many research teams, from the biomedical and bioinformatics fields, to study the application of machine learning (ML) algorithms. The International Society of Geriatric Oncology 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, no applications of ML have been proposed using CGA to classify elderly cancer patients. OBJECTIVE The aim of this study was 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 (MMSE), geriatric depression scale-short form, international physical activity questionnaire-short form, timed up and go, Katz index of independence in activities of daily living, Charlson comorbidity index, Karnofsky performance scale (KPS), polypharmacy, and mini nutritional assessment-short form (MNA-SF). The 10-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 6 months of diagnosis, in a variety of ML classifiers, including Naive Bayes (NB), decision tree algorithm J48 (J48), and multilayer perceptron (MLP). On each fold of evaluation, tiebreaking 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, which were either statistically similar (NB) or higher (J48 and MLP) when compared with 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. CONCLUSIONS A simplified predictive model aiming to estimate the risk of early death in elderly cancer patients is proposed herein, minimally composed by the MNA-SF and KPS. We strongly recommend that these questionnaires be incorporated into regular geriatric assessment of older patients with cancer.

scholarly journals Developing Machine Learning Algorithms for the Prediction of Early Death in Elderly Cancer Patients: Usability Study

JMIR Cancer ◽  
10.2196/12163 ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. e12163 ◽  
Author(s):  
Gabrielle Ribeiro Sena ◽  
Tiago Pessoa Ferreira Lima ◽  
Maria Julia Gonçalves Mello ◽  
Luiz Claudio Santos Thuler ◽  
Jurema Telles Oliveira Lima
Keyword(s):  
Machine Learning ◽  
Cancer Patients ◽  
Geriatric Assessment ◽  
Short Form ◽  
Geriatric Oncology ◽  
Depression Scale ◽  
Mini Nutritional Assessment ◽  
Early Death ◽  
Machine Learning Algorithms ◽  
Elderly Cancer Patients

Background The importance of classifying cancer patients into high- or low-risk groups has led many research teams, from the biomedical and bioinformatics fields, to study the application of machine learning (ML) algorithms. The International Society of Geriatric Oncology 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, no applications of ML have been proposed using CGA to classify elderly cancer patients. Objective The aim of this study was 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 (MMSE), geriatric depression scale-short form, international physical activity questionnaire-short form, timed up and go, Katz index of independence in activities of daily living, Charlson comorbidity index, Karnofsky performance scale (KPS), polypharmacy, and mini nutritional assessment-short form (MNA-SF). The 10-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 6 months of diagnosis, in a variety of ML classifiers, including Naive Bayes (NB), decision tree algorithm J48 (J48), and multilayer perceptron (MLP). On each fold of evaluation, tiebreaking 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, which were either statistically similar (NB) or higher (J48 and MLP) when compared with 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. Conclusions A simplified predictive model aiming to estimate the risk of early death in elderly cancer patients is proposed herein, minimally composed by the MNA-SF and KPS. We strongly recommend that these questionnaires be incorporated into regular geriatric assessment of older patients with cancer.

Mini-Mental-State

2009 ◽  
Vol 22 (1) ◽  
pp. 11-16 ◽  
Author(s):  
Andreas Kaiser ◽  
Renate Gusner-Pfeiffer ◽  
Hermann Griessenberger ◽  
Bernhard Iglseder
Keyword(s):  
Mental State ◽  
Mini Mental State Examination ◽  
Oder Nicht ◽  
State Examination

Im folgenden Artikel werden fünf verschiedene Versionen der Mini-Mental-State-Examination dargestellt, die alle auf der Grundlage des Originals von Folstein erstellt wurden, sich jedoch deutlich voneinander unterscheiden und zu unterschiedlichen Ergebnissen kommen, unabhängig davon, ob das Screening von erfahrenen Untersuchern durchgeführt wird oder nicht. Besonders auffällig ist, dass Frauen die Aufgaben «Wort rückwärts» hoch signifikant besser lösten als das «Reihenrechnen». An Hand von Beispielen werden Punkteunterschiede aufgezeigt.

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