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A Quantitative Assessment of Time, Frequency, and Time-frequency Algorithms for Automated Seizure Detection and Monitoring

Vangal et al. | Sep 28, 2020

A Quantitative Assessment of Time, Frequency, and Time-frequency Algorithms for Automated Seizure Detection and Monitoring

Each year, over 100,000 patients die from Sudden Unexpected Death in Epilepsy (SUDEP). A reliable seizure warning system can help patients stay safe. This work presents a comprehensive, comparative analysis of three different signal processing algorithms for automated seizure/ictal detection. The experimental results show that the proposed methods can be effective for accurate automated seizure detection and monitoring in clinical care.

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Who is at Risk for a Spinal Fracture? – A Comparative Study of National Health and Nutrition Examination Survey Data

He et al. | Mar 01, 2018

Who is at Risk for a Spinal Fracture? – A Comparative Study of National Health and Nutrition Examination Survey Data

One common age-related health problem is the loss of bone mineral density (BMD), which can lead to a variety of negative health outcomes, including increased risk of spinal fracture. In this study, the authors investigate risk factors that may be predictive of an individual's risk of spinal fracture. Their findings provide valuable information that clinicians can use in patient evaluations.

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Deciphering correlation and causation in risk factors for heart disease with Mendelian randomization

Singh et al. | Feb 08, 2023

Deciphering correlation and causation in risk factors for heart disease with Mendelian randomization
Image credit: Robina Weermeijer

Here, seeking to identify the risk of coronary artery disease (CAD), a major cause of cardiovascular disease, the authors used Mendelian randomization. With this method they identified several traits such as blood pressure readings, LDL cholesterol and BMI as significant risk factors. While other traits were not found to be significant risk factors.

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Risk assessment modeling for childhood stunting using automated machine learning and demographic analysis

Sirohi et al. | Sep 25, 2022

Risk assessment modeling for childhood stunting using automated machine learning and demographic analysis

Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.

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QuitPuff: A Simple Method Using Saliva to Assess the Risk of Oral Pre-Cancerous Lesions and Oral Squamous Cell Carcinoma in Chronic Smokers

Shamsher et al. | Mar 27, 2019

QuitPuff: A Simple Method Using Saliva to Assess the Risk of Oral Pre-Cancerous Lesions and Oral Squamous Cell Carcinoma in Chronic Smokers

Smoking generates free radicals and reactive oxygen species which induce cell damage and lipid peroxidation. This is linked to the development of oral cancer in chronic smokers. The authors of this study developed Quitpuff, simple colorimetric test to measure the extent of lipid peroxidation in saliva samples. This test detected salivary lipid peroxidation with 96% accuracy in test subjects and could serve as an inexpensive, non-invasive test for smokers to measure degree of salivary lipid peroxidation and potential risk of oral cancer.

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Culturally Adapted Assessment Tool for Autism Spectrum Disorder and its Clinical Significance

Das et al. | Apr 19, 2021

Culturally Adapted Assessment Tool for Autism Spectrum Disorder and its Clinical Significance

Diagnosing of Autism Spectrum Disorder (ASD) using tools developed in the West is challenging in the Indian setting due to a huge diversity in sociocultural and economic backgrounds. Here, the authors developed a home-based, audiovisual game app (Autest) suitable for ASD risk assessment in Indian children under 10 years of age. Ratings suggested that the tool is effective and can reduce social inhibition and facilitate assessment. Further usage and development of Autest can improve risk assessment and early intervention measures for children with ASD in India.

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