Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study

Journal of Medical Internet Research, Journal Year: 2019, Volume and Issue: 21(4), P. e11029 - e11029

Published: March 29, 2019

Related ArticleThis is a corrected version. See correction statement in: https://www.jmir.org/2019/10/e15966

Language: Английский

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Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression

npj Digital Medicine, Journal Year: 2023, Volume and Issue: 6(1)

Published: May 5, 2023

Abstract Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one technologies that have been exploited to detect or predict depression. The current review aimed at examining performance AI in detecting and predicting search sources this systematic were 8 electronic databases. Study selection, data extraction, risk bias assessment carried out by two reviewers independently. extracted results synthesized narratively statistically. Of 1314 citations retrieved from databases, 54 studies included review. pooled mean highest accuracy, sensitivity, specificity, root square error (RMSE) was 0.89, 0.87, 0.93, 4.55, respectively. lowest RMSE 0.70, 0.61, 0.73, 3.76, Subgroup analyses revealed there a statistically significant difference specificity between algorithms, sensitivity devices. Wearable promising tool for depression detection prediction although it its infancy not ready use clinical practice. Until further research improve performance, should be used conjunction with other methods diagnosing Further are needed examine based on combination device neuroimaging distinguish patients those diseases.

Language: Английский

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Unravelling the complexities of depression with medical intelligence: exploring the interplay of genetics, hormones, and brain function

Complex & Intelligent Systems, Journal Year: 2024, Volume and Issue: 10(4), P. 5883 - 5915

Published: April 4, 2024

Abstract Depression is a multifactorial disease with unknown etiology affecting globally. It’s the second most significant reason for infirmity in 2020, about 50 million people worldwide, 80% living developing nations. Recently, surge depression research has been witnessed, resulting multitude of emerging techniques developed prediction, evaluation, detection, classification, localization, and treatment. The main purpose this study to determine volume conducted on different aspects such as genetics, proteins, hormones, oxidative stress, inflammation, mitochondrial dysfunction, associations other mental disorders like anxiety stress using traditional medical intelligence (medical AI). In addition, it also designs comprehensive survey treatment planning, genetic predisposition, along future recommendations. This work designed through methods, including systematic mapping process, literature review, network visualization. we used VOSviewer software some authentic databases Google Scholar, Scopus, PubMed, Web Science data collection, analysis, designing picture study. We analyzed 60 articles related intelligence, 47 from machine learning 513,767 subjects (mean ± SD = 10,931.212 35,624.372) 13 deep 37,917 3159.75 6285.57). Additionally, found that stressors impact brain's cognitive autonomic functioning, increased production catecholamine, decreased cholinergic glucocorticoid activity, cortisol. These factors lead chronic inflammation hinder normal leading depression, anxiety, cardiovascular disorders. brain, reactive oxygen species (ROS) by IL-6 stimulation cytochrome c oxidase inhibited nitric oxide, potent inhibitor. Proteins, lipids, phosphorylation enzymes, mtDNA are further disposed impairment mitochondria. Consequently, dysfunction exacerbates impairs DNA (mtDNA) or deletions mtDNA, increases intracellular Ca 2+ levels, changes fission/fusion morphology, lastly leads neuronal death. highlights multidisciplinary approaches intelligence. It will open new way technologies.

Language: Английский

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Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study

JMIR mhealth and uhealth, Journal Year: 2021, Volume and Issue: 9(7), P. e26540 - e26540

Published: May 14, 2021

Depression is a prevalent mental health challenge. Current depression assessment methods using self-reported and clinician-administered questionnaires have limitations. Instrumenting smartphones to passively continuously collect moment-by-moment data sets quantify human behaviors has the potential augment current for early diagnosis, scalable, longitudinal monitoring of depression.

Language: Английский

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Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review

Journal of Medical Internet Research, Journal Year: 2022, Volume and Issue: 25, P. e42672 - e42672

Published: Dec. 11, 2022

Anxiety and depression are the most common mental disorders worldwide. Owing to lack of psychiatrists around world, incorporation artificial intelligence (AI) into wearable devices (wearable AI) has been exploited provide health services.This review aimed explore features AI used for anxiety identify application areas open research issues.We searched 8 electronic databases (MEDLINE, PsycINFO, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, Google Scholar) included studies that met inclusion criteria. Then, we checked cited screened were by studies. The study selection data extraction carried out 2 reviewers independently. extracted aggregated summarized using narrative synthesis.Of 1203 identified, 69 (5.74%) in this review. Approximately, two-thirds depression, whereas remaining it anxiety. frequent was diagnosing depression; however, none treatment purposes. Most targeted individuals aged between 18 65 years. device Actiwatch AW4 (Cambridge Neurotechnology Ltd). Wrist-worn type commonly category model development physical activity data, followed sleep heart rate data. frequently set from sources Depresjon. algorithm random forest, support vector machine.Wearable can offer great promise providing services related depression. Wearable be prescreening assessment Further reviews needed statistically synthesize studies' results performance effectiveness AI. Given its potential, technology companies should invest more

Language: Английский

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Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis

Pervasive and Mobile Computing, Journal Year: 2022, Volume and Issue: 83, P. 101621 - 101621

Published: May 24, 2022

Depression is a prevalent mental disorder. Current clinical and self-reported assessment methods of depression are laborious incur recall bias. Their sporadic nature often misses severity fluctuations. Previous research highlights the potential in-situ quantification human behaviour using mobile sensors to augment traditional management. In this paper, we study whether mood scores passive smartphone wearable sensor data could be used classify people as depressed or non-depressed. longitudinal study, our participants provided daily (valence arousal) collected their smartphones Oura Rings. We computed aggregations mood, sleep, physical activity, phone usage, GPS mobility from raw differences between non-depressed groups created population-level Machine Learning classification models depression. found statistically significant in mobility, activity groups. An XGBoost model with predictors classified an accuracy 81.43% Area Under Curve 82.31%. A Support Vector only sensor-based had 77.06% 74.25%. Our results suggest that digital biomarkers promising differentiating without symptoms. This contributes body evidence supporting role unobtrusive understanding its diagnosis monitoring.

Language: Английский

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Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence

BMC Psychiatry, Journal Year: 2022, Volume and Issue: 22(1)

Published: June 22, 2022

This PRISMA systematic literature review examined the use of digital data collection methods (including ecological momentary assessment [EMA], experience sampling method [ESM], biomarkers, passive sensing, mobile ambulatory assessment, and time-series analysis), emphasizing on phenotyping (DP) to study depression. DP is defined as profile health information objectively.Four distinct yet interrelated goals underpin this study: (a) identify empirical research examining depression; (b) describe different technology employed; (c) integrate evidence regarding efficacy in examination, diagnosis, monitoring depression (d) clarify definitions mental records terminology.Overall, 118 studies were assessed eligible. Considering terms employed, "EMA", "ESM", "DP" most predominant. A variety sources reported, including voice, language, keyboard typing kinematics, phone calls texts, geocoded activity, actigraphy sensor-related recordings (i.e., steps, sleep, circadian rhythm), self-reported apps' information. Reviewed employed subjectively objectively recorded combination with interviews psychometric scales.Findings suggest links between a person's Future recommendations include deriving consensus definition expanding consider broader contextual developmental circumstances relation their data/records.

Language: Английский

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Late-life depression: Epidemiology, phenotype, pathogenesis and treatment before and during the COVID-19 pandemic

Frontiers in Psychiatry, Journal Year: 2023, Volume and Issue: 14

Published: April 6, 2023

Late-life depression (LLD) is one of the most common mental disorders among older adults. Population aging, social stress, and COVID-19 pandemic have significantly affected emotional health adults, resulting in a worldwide prevalence LLD. The clinical phenotypes between LLD adult differ terms symptoms, comorbid physical diseases, coexisting cognitive impairments. Many pathological factors such as imbalance neurotransmitters, decrease neurotrophic factors, an increase β-amyloid production, dysregulation hypothalamic-pituitary-adrenal axis, changes gut microbiota, are allegedly associated with onset However, exact pathogenic mechanism underlying remains unclear. Traditional selective serotonin reuptake inhibitor therapy results poor responsiveness side effects during treatment. Neuromodulation therapies complementary integrative been proven safe effective for treatment Importantly, pandemic, modern digital intervention technologies, including socially assistive robots app-based interventions, to be advantageous providing personal services patients

Language: Английский

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How machine learning is used to study addiction in digital healthcare: A systematic review

International Journal of Information Management Data Insights, Journal Year: 2023, Volume and Issue: 3(2), P. 100175 - 100175

Published: April 25, 2023

Language: Английский

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Intelligent Internet of Medical Things for Depression: Current Advancements, Challenges, and Trends

International Journal of Intelligent Systems, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

We investigated the fusion of Intelligent Internet Medical Things (IIoMT) with depression management, aiming to autonomously identify, monitor, and offer accurate advice without direct professional intervention. Addressing pivotal questions regarding IIoMT’s role in identification, its correlation stress anxiety, impact machine learning (ML) deep (DL) on depressive disorders, challenges potential prospects integrating management IIoMT, this research offers significant contributions. It integrates artificial intelligence (AI) (IoT) paradigms expand studies, highlighting data science modeling’s practical application for intelligent service delivery real‐world settings, emphasizing benefits within IoT. Furthermore, it outlines an IIoMT architecture gathering, analyzing, preempting employing advanced analytics enhance intelligence. The study also identifies current challenges, future trajectories, solutions domain, contributing scientific understanding management. evaluates 168 closely related articles from various databases, including Web Science (WoS) Google Scholar, after rejection repeated books. shows that there is 48% growth articles, mainly focusing symptoms, detection, classification. Similarly, most being conducted United States America, trend increasing other countries around globe. These results suggest essence automated monitoring, suggestions handling depression.

Language: Английский

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