scholarly journals Early Detection of Depression using Screening Tools and Electroencephalogram (EEG) Measurements

2020 ◽  
Vol 12 (6) ◽  
Author(s):  
Wong Hui Ming ◽  
◽  
Arifah Bahar ◽  
Khairul Radhi Bahar ◽  
Mitra Mohd.Addi ◽  
...  
Keyword(s):  
Early Detection ◽  
Screening Tools ◽  
Electroencephalogram Eeg ◽  
Detection Of Depression

scholarly journals Metabolomic Biomarkers for the Detection of Obesity-Driven Endometrial Cancer

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 718
Author(s):  
Kelechi Njoku ◽  
Amy E. Campbell ◽  
Bethany Geary ◽  
Michelle L. MacKintosh ◽  
Abigail E. Derbyshire ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Early Detection ◽  
Minimally Invasive ◽  
Cancer Detection ◽  
Test Performance ◽  
Female Genital Tract ◽  
Screening Tools ◽  
Normal Endometrium ◽  
Endometrioid Endometrial Cancer ◽  
Post Menopausal

Endometrial cancer is the most common malignancy of the female genital tract and a major cause of morbidity and mortality in women. Early detection is key to ensuring good outcomes but a lack of minimally invasive screening tools is a significant barrier. Most endometrial cancers are obesity-driven and develop in the context of severe metabolomic dysfunction. Blood-derived metabolites may therefore provide clinically relevant biomarkers for endometrial cancer detection. In this study, we analysed plasma samples of women with body mass index (BMI) ≥ 30 kg/m2 and endometrioid endometrial cancer (cases, n = 67) or histologically normal endometrium (controls, n = 69), using a mass spectrometry-based metabolomics approach. Eighty percent of the samples were randomly selected to serve as a training set and the remaining 20% were used to qualify test performance. Robust predictive models (AUC > 0.9) for endometrial cancer detection based on artificial intelligence algorithms were developed and validated. Phospholipids were of significance as biomarkers of endometrial cancer, with sphingolipids (sphingomyelins) discriminatory in post-menopausal women. An algorithm combining the top ten performing metabolites showed 92.6% prediction accuracy (AUC of 0.95) for endometrial cancer detection. These results suggest that a simple blood test could enable the early detection of endometrial cancer and provide the basis for a minimally invasive screening tool for women with a BMI ≥ 30 kg/m2.

scholarly journals Predicting Liver Disease Risk Using a Combination of Common Clinical Markers: A Screening Model from Routine Health Check-Up

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yi Wang ◽  
Yanni Li ◽  
Xiaoyi Wang ◽  
Ranko Gacesa ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Liver Disease ◽  
Early Detection ◽  
Predictive Power ◽  
Disease Screening ◽  
Health Check ◽  
Initial Presentation ◽  
Screening Tools ◽  
Clinical Markers ◽  
Liver Disorders ◽  
High Predictive Power

Background. Early detection is crucial for the prognosis of patients with autoimmune liver disease (AILD). Due to the relatively low incidence, developing screening tools for AILD remain a challenge. Aims. To analyze clinical characteristics of AILD patients at initial presentation and identify clinical markers, which could be useful for disease screening and early detection. Methods. We performed observational retrospective study and analyzed 581 AILD patients who were hospitalized in the gastroenterology department and 1000 healthy controls who were collected from health management center. Baseline characteristics at initial presentation were used to build regression models. The model was validated on an independent cohort of 56 patients with AILD and 100 patients with other liver disorders. Results. Asymptomatic AILD individuals identified by the health check-up are increased yearly (from 31.6% to 68.0%, p<0.001). The cirrhotic rates at an initial presentation are decreased in the past 18 years (from 52.6% to 20.0%, p<0.001). Eight indicators, which are common in the health check-up, are independent risk factors of AILD. Among them, abdominal lymph node enlargement (LN) positive is the most significant different (OR 8.85, 95% CI 2.73-28.69, p<0.001). The combination of these indicators shows high predictive power (AUC=0.98, sensitivity 89.0% and specificity 96.4%) for disease screening. Except two liver or cholangetic injury makers, the combination of AGE, GENDER, GLB, LN, concomitant extrahepatic autoimmune diseases, and familial history also shows a high predictive power for AILD in other liver disorders (AUC=0.91). Conclusion. Screening for AILD with described parameters can detect AILD in routine health check-up early, effectively and economically. Eight variables in routine health check-up are associated with AILD and the combination of them shows good ability of identifying high-risk individuals.

scholarly journals Early Detection of Depression Indication from Social Media Analysis

2021 ◽  
Vol 40 ◽  
pp. 03029
Author(s):  
Maharukh Syed ◽  
Meera Narvekar
Keyword(s):  
Social Media ◽  
Early Detection ◽  
Media Analysis ◽  
Social Media Analysis ◽  
The Past ◽  
Course Of Action ◽  
Negative Thoughts ◽  
Depression Detection ◽  
Suicidal Thoughts And Behaviors ◽  
Detection Of Depression

Depression that stems through social media has been steadily growing since the past few years but with the current inclination towards social media reliance it is highly imperative to detect the early signs. Continuous observation of a user's social media interests and activities may highlight suspicious and negative thoughts. This observation can help in understanding their future course of action and also indicate any suicidal thoughts and behaviors. By using the machine learning models, early indications of depression detection can be addressed. This work studies different word embedding techniques for early detection of depression from social media posts. Further, this work develops a model using various NLP processes in order to address the issue of early detection. The recommendations can be useful as a Decision Support System for counselors, psychologist and also can be of good use by the cyber-crime cell department for criminal investigations.

FRAILTY DETECTION WITH THE GERONTOPOLE FRAILTY SCREENING TOOL (GFST)

2013 ◽  
pp. 1-3
Author(s):  
L. DEMOUGEOT ◽  
G. ABELLAN VAN KAN ◽  
B. VELLAS ◽  
P. DE SOUTO BARRETO
Keyword(s):  
Older Adults ◽  
Clinical Practice ◽  
Early Detection ◽  
General Practitioners ◽  
Older Persons ◽  
Screening Tool ◽  
Research Field ◽  
Screening Tools ◽  
Frail Older Adults ◽  
Over Time

Frailty is commonly regarded as a pre-disability condition of older persons. Its importance in theelderly should be more carefully taken into account in the clinical practice. To implement interventions aimed atpreventing disability in frail older adults, screening tools for the early detection of this syndrome are needed. Inthis context, the Gérontopôle Frailty Screening Tool (GFST) has been recently proposed as an instrument forassisting general practitioners in the detection of non-disabled frail older adults. In the present paper, we brieflydiscuss about the difficulties of translating knowledge from the frailty research field to the clinical practice. Suchdifficulties are illustrated by presenting the evolution of the GFST over time. The use of frailty screening tools,such as the GFST, in the clinical practice is necessary to support the identification of older persons at risk ofadverse events and promote the implementation of individualized strategies against disability.

scholarly journals Insomnia and depression: Japanese hospital workers questionnaire survey

Open Medicine ◽  
2017 ◽  
Vol 12 (1) ◽  
pp. 391-398
Author(s):  
Fumihiko Koyama ◽  
Takeshi Yoda ◽  
Tomohiro Hirao
Keyword(s):  
Metabolic Syndrome ◽  
Chronic Pain ◽  
Early Detection ◽  
Wide Spectrum ◽  
Chronic Insomnia ◽  
Depression Symptoms ◽  
Preventative Care ◽  
Partial Regression Coefficient ◽  
Lifestyle Related Diseases ◽  
Detection Of Depression

AbstractObjectivesThis study aimed to identify a correlation between insomnia and the occurrence of depression among Japanese hospital employees using the data obtained from a self-reported questionnaire.MethodsA self-administered questionnaire on sleeping patterns, depression, fatigue, lifestyle-related diseases, and chronic pain was given to 7690 employees aged 20-60 years, and 5,083 employees responded.ResultsAn insomnia score of >2 was observed in 840 (13%) respondents. Chronic insomnia correlated significantly with gender, occupation, overtime work, metabolic syndrome, chronic pain, fatigue, and depression. Moreover, significant negative effects on depression scores were observed in males aged 30-39 (partial regression coefficient: b=0.357, p=0.016), females aged 20-29 (b=0.494, p<0.001), male administrative staff (b=0.475, p=0.003), males with metabolic syndrome (b=0.258, p=0.023), and both genders with chronic insomnia (male; b=0.480, p<0.001: female; b=0.485, p<0.001), and fatigue (male; b=1.180, p<0.001: female; b=1.151, p<0.001).DiscussionInsomnia is a risk factor for depression and for other lifestyle-related diseases. The insomnia score may be useful in preventative care settings because it is associated with a wide spectrum of diseases and serves as a valuable marker for early detection of depression. Thus, our future studies will focus on establishing a method for early detection of depression symptoms among workers across various job profiles.

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