SynthesisJournal of medical systems2018
Data Mining Algorithms and Techniques in Mental Health: A Systematic Review.
Synthesis in Journal of medical systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identifying reproductive-aged women with physical and sensory disabilities in administrative health data: A systematic review.Disability and health journal · 2020Pooled it
- Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM).Biomimetics (Basel, Switzerland) · 2024Review
- Treatment Algorithm for the Resorption of Calcific Tendinitis Using Extracorporeal Shockwave Therapy: A Data Mining Study.Orthopaedic journal of sports medicine · 2024Article
- Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace.Journal of multidisciplinary healthcare · 2024Review
- A multistart tabu search-based method for feature selection in medical applications.Scientific reports · 2023Article
- Public mental health through social media in the post COVID-19 era.Frontiers in public health · 2023Article
- Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review.Health science reports · 2023Article
- Automated Detection of Substance-Use Status and Related Information from Clinical Text.Sensors (Basel, Switzerland) · 2022Article
- Detection of Depression Severity Using Bengali Social Media Posts on Mental Health: Study Using Natural Language Processing Techniques.JMIR formative research · 2022Article
- Classification of Depressive and Schizophrenic Episodes Using Night-Time Motor Activity Signal.Healthcare (Basel, Switzerland) · 2022Article
- Natural Language Processing Methods and Bipolar Disorder: Scoping Review.JMIR mental health · 2022Article
- iHealth: The ethics of artificial intelligence and big data in mental healthcare.Internet interventions · 2022Article
- Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia.Sensors (Basel, Switzerland) · 2022Article
- Empirical analysis based on the related factors of college students' mental health problems.Frontiers in psychology · 2022Article
- The Integrated Analysis Identifies Three Critical Genes as Novel Diagnostic Biomarkers Involved in Immune Infiltration in Atherosclerosis.Frontiers in immunology · 2022Article
- A CNN-Based Framework for Predicting Public Emotion and Multi-Level Behaviors Based on Network Public Opinion.Frontiers in psychology · 2022Article
- Impact of a computer system as a triage tool in the management of pulmonary tuberculosis in a HIV reference center in Brazil.Revista da Sociedade Brasileira de Medicina Tropical · 2022Article
- Classification of psychiatric symptoms using deep interaction networks: the CASPIAN-IV study.Scientific reports · 2021Article
- Analysis of Mental Health Disease Trends Using BeGraph Software in Spanish Health Care Centers: Case Study.JMIR medical informatics · 2021Article
- Discovery of Association Rules Patterns and Prevalence of Comorbidities in Adult Patients Hospitalized with Mental and Behavioral Disorders.Healthcare (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data Mining in medicine is an emerging field of great importance to provide a prognosis and deeper understanding of disease classification, specifically in Mental Health areas. The main objective of this paper is to present a review of the existing research works in the literature, referring to the techniques and algorithms of Data Mining in Mental Health, specifically in the most prevalent diseases such as: Dementia, Alzheimer, Schizophrenia and Depression. Academic databases that were used to perform the searches are Google Scholar, IEEE Xplore, PubMed, Science Direct, Scopus and Web of Science, taking into account as date of publication the last 10 years, from 2008 to the present. Several search criteria were established such as 'techniques' AND 'Data Mining' AND 'Mental Health', 'algorithms' AND 'Data Mining' AND 'dementia' AND 'schizophrenia' AND 'depression', etc. selecting the papers of greatest interest. A total of 211 articles were found related to techniques and algorithms of Data Mining applied to the main Mental Health diseases. 72 articles have been identified as relevant works of which 32% are Alzheimer's, 22% dementia, 24% depression, 14% schizophrenia and 8% bipolar disorders. Many of the papers show the prediction of risk factors in these diseases. From the review of the research articles analyzed, it can be said that use of Data Mining techniques applied to diseases such as dementia, schizophrenia, depression, etc. can be of great help to the clinical decision, diagnosis prediction and improve the patient's quality of life.
Indexed as
Identifiers
30030644What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.