AIMC Topic: Self Report

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Early detection of COVID-19 in the UK using self-reported symptoms: a large-scale, prospective, epidemiological surveillance study.

The Lancet. Digital health
BACKGROUND: Self-reported symptoms during the COVID-19 pandemic have been used to train artificial intelligence models to identify possible infection foci. To date, these models have only considered the culmination or peak of symptoms, which is not s...

Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective.

Sensors (Basel, Switzerland)
Cognitive fatigue is a psychological state characterised by feelings of tiredness and impaired cognitive functioning arising from high cognitive demands. This paper examines the recent research progress on the assessment of cognitive fatigue and prov...

Comparing stress prediction models using smartwatch physiological signals and participant self-reports.

Computer methods and programs in biomedicine
Recent advances in wearable technology have facilitated the non-obtrusive monitoring of physiological signals, creating opportunities to monitor and predict stress. Researchers have utilized machine learning methods using these physiological signals ...

Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence.

Journal of medical Internet research
BACKGROUND: The COVID-19 pandemic has disrupted human societies around the world. This public health emergency was followed by a significant loss of human life; the ensuing social restrictions led to loss of employment, lack of interactions, and burg...

App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning.

PloS one
BACKGROUND: Tests are scarce resources, especially in low and middle-income countries, and the optimization of testing programs during a pandemic is critical for the effectiveness of the disease control. Hence, we aim to use the combination of sympto...

Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set.

Journal of medical Internet research
BACKGROUND: In the United States, the rapidly evolving COVID-19 outbreak, the shortage of available testing, and the delay of test results present challenges for actively monitoring its spread based on testing alone.

A machine learning approach to modeling PTSD and difficulties in emotion regulation.

Psychiatry research
Despite evidence for the association between emotion regulation difficulties and posttraumatic stress disorder (PTSD), less is known about the specific emotion regulation abilities that are most relevant to PTSD severity. This study examined both ite...

Estimating the health-related quality of life of kidney stone patients: initial results from the Wisconsin Stone Quality of Life Machine-Learning Algorithm (WISQOL-MLA).

BJU international
OBJECTIVE: To build the Wisconsin Stone Quality of Life Machine-Learning Algorithm (WISQOL-MLA) to predict urolithiasis patients' health-related quality of life (HRQoL) based on demographic, symptomatic and clinical data collected for the validation ...

Comparing supervised and unsupervised approaches to emotion categorization in the human brain, body, and subjective experience.

Scientific reports
Machine learning methods provide powerful tools to map physical measurements to scientific categories. But are such methods suitable for discovering the ground truth about psychological categories? We use the science of emotion as a test case to expl...

Using Machine Learning to Predict Suicide Attempts in Military Personnel.

Psychiatry research
Identifying predictors of suicide attempts is critical in intervention and prevention efforts, yet finding predictors has proven difficult due to the low base rate and underpowered statistical approaches. The objective of the current study was to use...