ArticleMolecular psychiatry2012
Machine learning and data mining: strategies for hypothesis generation.
Article in Molecular psychiatry, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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
40 citing papers in PubMed.
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- Unsupervised Machine Learning in Identification of Septic Shock Phenotypes and Their In-Hospital Outcomes: A Multicenter Cohort Study.Journal of clinical medicine · 2025Article
- Reflections on the Potential and Risks of AI for Scientific Article Writing after the AI Endorsement by Some Scientific Publishers: Focusing on Scopus AI.Actas espanolas de psiquiatria · 2025Review
- Sociodemographic Variables in Offender and Non-Offender Patients Diagnosed with Schizophrenia Spectrum Disorders-An Explorative Analysis Using Machine Learning.Healthcare (Basel, Switzerland) · 2024Article
- Applications of artificial intelligence-machine learning for detection of stress: a critical overview.Molecular psychiatry · 2024Review
- Late-life suicide: machine learning predictors from a large European longitudinal cohort.Frontiers in psychiatry · 2024Article
- Offenders and non-offenders with schizophrenia spectrum disorders: the crime-preventive potential of sufficient embedment in the mental healthcare and support system.Frontiers in psychiatry · 2023Article
- Data Analytics in Healthcare: A Tertiary Study.SN computer science · 2023Review
- Offenders and non-offenders with schizophrenia spectrum disorders: Do they really differ in known risk factors for aggression?Frontiers in psychiatry · 2023Article
- COVID-19 Vaccination Intention and Factors Associated with Hesitance and Resistance in the Deep South: Montgomery, Alabama.Tropical medicine and infectious disease · 2022Article
- A Multidisciplinary Investigation into the Talent Development Processes at an English Football Academy: A Machine Learning Approach.Sports (Basel, Switzerland) · 2022Article
- Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD.Diagnostics (Basel, Switzerland) · 2022Article
- Deep neural networks learn general and clinically relevant representations of the ageing brain.NeuroImage · 2022Article
- A Machine Learning Approach for Detecting Cognitive Interference Based on Eye-Tracking Data.Frontiers in human neuroscience · 2022Article
- Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data.Translational psychiatry · 2021Article
- Article
- Patient journey through cases of depression from claims database using machine learning algorithms.PloS one · 2021Article
- Machine Learning With Neuroimaging: Evaluating Its Applications in Psychiatry.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2020Review
- At the Crossroads Between Psychiatry and Machine Learning: Insights Into Paradigms and Challenges for Clinical Applicability.Frontiers in psychiatry · 2020Article
- Brain-based ranking of cognitive domains to predict schizophrenia.Human brain mapping · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Strategies for generating knowledge in medicine have included observation of associations in clinical or research settings and more recently, development of pathophysiological models based on molecular biology. Although critically important, they limit hypothesis generation to an incremental pace. Machine learning and data mining are alternative approaches to identifying new vistas to pursue, as is already evident in the literature. In concert with these analytic strategies, novel approaches to data collection can enhance the hypothesis pipeline as well. In data farming, data are obtained in an 'organic' way, in the sense that it is entered by patients themselves and available for harvesting. In contrast, in evidence farming (EF), it is the provider who enters medical data about individual patients. EF differs from regular electronic medical record systems because frontline providers can use it to learn from their own past experience. In addition to the possibility of generating large databases with farming approaches, it is likely that we can further harness the power of large data sets collected using either farming or more standard techniques through implementation of data-mining and machine-learning strategies. Exploiting large databases to develop new hypotheses regarding neurobiological and genetic underpinnings of psychiatric illness is useful in itself, but also affords the opportunity to identify novel mechanisms to be targeted in drug discovery and development.
Indexed as
Identifiers
22230882What 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.