Machine learning-based screening for outpatients with dementia using drawing features from the clock drawing test.

In geriatrics and dementia care, early diagnosis is crucial. We developed a dementia screening model using drawing features from clock drawing tests (CDT) and investigated the features contributing to the discrimination of dementia and its screening performance. This study included 129 older adults attending a dementia outpatient clinic.

Novel machine learning method allerStat identifies statistically significant allergen-specific patterns in protein sequences.

Cutting-edge technologies such as genome editing and synthetic biology allow us to produce novel foods and functional proteins. However, their toxicity and allergenicity must be accurately evaluated. It is known that specific amino acid sequences in proteins make some proteins allergic, but many of these sequences remain uncharacterized.

Dementia Screening Based on SVM Using Qualitative Drawing Error of Clock Drawing Test.

In the welfare of the elderly, it is important to detect the signs of dementia at an early stage and prevent it from becoming serious. We evaluated the performance of SVM-based cognitive function classification models and investigated the drawing features that contribute to distinguishing the severity of cognitive functions. Clock drawing test (CDT) was conducted on three groups of elderly people with different degrees of cognitive impairment.

Easy Screening for Mild Alzheimer's Disease and Mild Cognitive Impairment from Elderly Speech.

Objective: This study presents a novel approach for early detection of cognitive impairment in the elderly. The approach incorporates the use of speech sound analysis, multivariate statistics, and data-mining techniques. We have developed a speech prosody-based cognitive impairment rating (SPCIR) that can distinguish between cognitively normal controls and elderly people with mild Alzheimer's disease (mAD) or mild cognitive impairment (MCI) using prosodic signals extracted from elderly speech while administering a questionnaire.

Detection of mild Alzheimer's disease and mild cognitive impairment from elderly speech: Binary discrimination using logistic regression.

In this research, we have developed a novel data-mining approach for detection of cognitive impairment, SPCIR (Speech Prosody-Based Cognitive Impairment Rating), which can discriminate between mild cognitive impairment and mild Alzheimer's disease from elderly using prosodic sign extracted from elderly speech during questionnaire test. This paper proposes a binary discrimination model of SPCIR using multivariate logistic regression and model selection using receiver operating characteristic (ROC) curve analysis, and reports the sensitivity and specificity of SPCIR for diagnosis (control; mild cognitive impairment/mild Alzheimer's disease).

Early detection of cognitive impairment in the elderly based on Bayesian mining using speech prosody and cerebral blood flow activation.

With the aim of providing computer aided diagnosis of dementia, we have developed a non-invasive screening system of the elderly with cognitive impairment. In our previous research, we have studied two data-mining approaches by focusing on speech-prosody and cerebral blood flow (CBF) activation during cognitive tests. On the power of these research results, this paper presents a prosody-CBF hybrid screening system of the elderly with cognitive impairment based on a Bayesian approach.