Importance of serum albumin in machine learning-based prediction of cognitive function in the elderly using a basic blood test.

Introduction: In this study, we investigated the correlation between serum albumin levels and cognitive function, and examined the impact of including serum albumin values in the input layer on the prediction accuracy when forecasting cognitive function using deep learning and other machine learning models.

Methods: We analyzed the electronic health record data from Osaka Medical and Pharmaceutical University Hospital between 2014 and 2021. The study included patients who underwent cognitive function tests during this period; however, patients from whom blood test data was not obtained up to 30 days before the cognitive function tests and those with values due to measurement error in blood test results were excluded.

Relationship Between Cognitive Function, Oral Conditions and Systemic Metabolic Function in the Elderly.

Systemic metabolic disorders, including lifestyle-related diseases, are known risk factors for dementia. Furthermore, oral diseases such as periodontal disease and tooth decay are also associated with systemic metabolic disorders such as lifestyle-related diseases, and have also been reported to be indicators of risk factors for developing dementia. In this study, we investigated the relationship between cognitive function, oral conditions and systemic metabolic function in the elderly.

Identification of Nutritional Factors to Evaluate Periodontal Clinical Parameters in Patients with Systemic Diseases.

Nutritional factors reflect the periodontal parameters accompanying periodontal status. In this study, the associations between nutritional factors, blood biochemical items, and clinical parameters were examined in patients with systemic diseases. The study participants were 94 patients with heart disease, dyslipidemia, kidney disease, or diabetes mellitus.

Estimation of Human Cerebral Atrophy Based on Systemic Metabolic Status Using Machine Learning.

Background: Based on the assumption that systemic metabolic disorders affect cognitive function, we have developed a deep neural network (DNN) model that can estimate cognitive function based on basic blood test data that do not contain dementia-specific biomarkers. In this study, we used the same DNN model to assess whether basic blood data can be used to estimate cerebral atrophy.

Methods: We used data from 1,310 subjects (58.

Machine Learning-Based Assessment of Cognitive Impairment Using Time-Resolved Near-Infrared Spectroscopy and Basic Blood Test.

We have demonstrated that machine learning allows us to predict cognitive function in aged people using near-infrared spectroscopy (NIRS) data or basic blood test data. However, the following points are not yet clear: first, whether there are differences in prediction accuracy between NIRS and blood test data; second, whether there are differences in prediction accuracy for cognitive function in linear models and non-linear models; and third, whether there are changes in prediction accuracy when both NIRS and blood test data are added to the input layer. We used a linear regression model (LR) for the linear model and random forest (RF) and deep neural network (DNN) for the non-linear model.

Relationship Between Decrease of Oxygenation During Incremental Exercise and Partial Pressure End-Tidal Carbon Dioxide: Near-Infrared Spectroscopy Vector Analysis.

A previous study considered that a decrease in cerebral oxyhemoglobin (OHb) immediately before maximal exercise during incremental exercise is related to cerebral blood flow (CBF) and partial pressure end-tidal carbon dioxide (PCO). This study aimed to investigate the relationship between OHb, PCO, and the estimated value of cerebral blood volume (CBV) with cerebral oxygen exchange (COE) by using vector analysis. Twenty-four healthy young men participated in this study.

Event-Related NIRS and EEG Analysis for Mental Stress Monitoring.

Mental disorders caused by chronic stress are difficult to identify, and colleagues in the work environment may suddenly report symptoms. Social barriers exist including the financial cost of medical services and the lack of a perceived need for treatment even if potential patients have a desire to receive mental healthcare. Self-report inventories such as the Beck Depression Inventory (BDI-II) and State-Trait Anxiety Inventory (STAI) can assess the emotional valence for mental health assessment, but medical expertise may be required for interpretation of the results.

Deep Learning-Based Screening Test for Cognitive Impairment Using Basic Blood Test Data for Health Examination.

In order to develop a new screening test of cognitive impairment, we studied whether cognitive function can be estimated from basic blood test data by applying deep learning models. This model was constructed based on the effects of systemic metabolic disorders on cognitive function. We employed a deep neural network (DNN) to predict cognitive function based on subject's age and blood test items (23 items).

Prediction of MMSE Score Using Time-Resolved Near-Infrared Spectroscopy.

Time-resolved near-infrared spectroscopy (TRS) enables assessment of baseline concentrations of hemoglobin (Hb) in the prefrontal cortex, which reflects regional cerebral blood flow and neuronal activity at rest. In a previous study, we demonstrated that baseline concentrations of oxy-Hb, deoxy-Hb, total-Hb, and oxygen saturation (SO) measured by TRS were correlated with mini mental state examination (MMSE) scores. In the present study, we investigated whether Hb concentrations measured with TRS at rest can predict MMSE scores in aged people with various cognitive functions.

Temporal Comparison Between NIRS and EEG Signals During a Mental Arithmetic Task Evaluated with Self-Organizing Maps.

Simultaneous monitoring of brain activity with near-infrared spectroscopy and electroencephalography allows spatiotemporal reconstruction of the hemodynamic response regarding the concentration changes in oxyhemoglobin and deoxyhemoglobin that are associated with recorded brain activity such as cognitive functions. However, the accuracy of state estimation during mental arithmetic tasks is often different depending on the length of the segment for sampling of NIRS and EEG signals. This study compared the results of a self-organizing map and ANOVA, which were both used to assess the accuracy of state estimation.