scholarly journals Identifying clinical clusters with distinct trajectories in first-episode psychosis through an unsupervised machine learning technique

2021 ◽  
Author(s):  
Silvia Amoretti ◽  
Norma Verdolini ◽  
Gisela Mezquida ◽  
Francisco Diego Rabelo-da-Ponte ◽  
Manuel J Cuesta ◽  
...  
Keyword(s):  
Machine Learning ◽  
First Episode Psychosis ◽  
First Episode ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Episode Psychosis ◽  
Learning Technique

Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique

2021 ◽  
Author(s):  
Alexandre Oliveira Marques ◽  
Aline Nonato Sousa ◽  
Veronica Pereira Bernardes ◽  
Camila Hipolito Bernardo ◽  
Danielle Monique Reis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brazilian Coast ◽  
Ecological Distribution ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique

scholarly journals Utilizing Unsupervised Machine Learning in BSM Physics Searches At The LHC

2020 ◽  
Vol 245 ◽  
pp. 06021
Author(s):  
Adam Leinweber ◽  
Martin White
Keyword(s):  
Machine Learning ◽  
Large Hadron Collider ◽  
Hadron Collider ◽  
New Physics ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique ◽  
Bsm Physics ◽  
Supersymmetric Particles ◽  
One Machine

Recent searches for supersymmetric particles at the Large Hadron Collider have been unsuccessful in detecting any BSM physics. This is partially because the exact masses of supersymmetric particles are not known, and as such, searching for them is very difficult. The method broadly used in searching for new physics requires one to optimise on the signal being searched for, potentially suppressing sensitivity to new physics which may actually be present that does not resemble the chosen signal. The problem with this approach is that, in order to detect something with this method, one must already know what to look for. I will showcase one machine-learning technique that can be used to define a “signal-agnostic” search. This is a search that does not make any assumptions about the signal being searched for, allowing it to detect a signal in a more general way. This method is applied to simulated BSM physics data and the results are explored.

scholarly journals Predicting One-Year Outcome in First Episode Psychosis using Machine Learning

2018 ◽  
Author(s):  
Samuel Leighton ◽  
Rajeev Krishnadas ◽  
Kelly Chung ◽  
Alison Blair ◽  
Susie Brown ◽  
...  
Keyword(s):  
Machine Learning ◽  
First Episode Psychosis ◽  
Elastic Net ◽  
Training Dataset ◽  
Validation Dataset ◽  
First Episode ◽  
Episode Psychosis ◽  
Symptom Remission ◽  
One Year ◽  
Independent Cohort

BackgroundEarly illness course correlates with long-term outcome in psychosis. Accurate prediction could allow more focused intervention. Earlier intervention corresponds to significantly better symptomatic and functional outcomes. Our study objective is to use routinely collected baseline demographic and clinical characteristics to predict employment, education or training (EET) status, and symptom remission in patients with first episode psychosis (FEP) at one-year.Methods and findings83 FEP patients were recruited from National Health Service (NHS) Glasgow between 2011 and 2014 to a 24-month prospective cohort study with regular assessment of demographic and psychometric measures. An external independent cohort of 79 FEP patients were recruited from NHS Glasgow and Edinburgh during a 12-month study between 2006 and 2009. Elastic net regularised logistic regression models were built to predict binary EET status, period and point remission outcomes at one-year on 83 Glasgow patients (training dataset). Models were externally validated on an independent dataset of 79 patients from Glasgow and Edinburgh (validation dataset). Only baseline predictors shared across both cohorts were made available for model training and validation. After excluding participants with missing outcomes, models were built on the training dataset for EET status, period and point remission outcomes and externally validated on the validation dataset. Models predicted EET status, period and point remission with ROC area under curve (AUC) performances of 0.876 (95%CI: 0.864, 0.887), 0.630 (95%CI: 0.612, 0.647) and 0.652 (95%CI: 0.635, 0.670) respectively. Positive predictors of EET included baseline EET and living with spouse/children. Negative predictors included higher PANSS suspiciousness, hostility and delusions scores. Positive predictors for symptom remission included living with spouse/children, and affective symptoms on the Positive and Negative Syndrome Scale (PANSS). Negative predictors of remission included passive social withdrawal symptoms on PANSS. A key limitation of this study is the small sample size (n) relative to the number of predictors (p), whereby p approaches n. The use of elastic net regularised regression rather than ordinary least squares regression helped circumvent this difficulty. Further, we did not have information for biological and additional social variables, such as nicotine dependence, which observational studies have linked to outcomes in psychosis. Conclusions and RelevanceUsing advanced statistical machine learning techniques we provide the first externally validated evidence, in a temporally and geographically independent cohort, for the ability to predict one-year EET status and symptom remission in individual FEP patients.

scholarly journals Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence

2019 ◽  
Vol 46 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Sandra Vieira ◽  
Qi-yong Gong ◽  
Walter H L Pinaya ◽  
Cristina Scarpazza ◽  
Stefania Tognin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cortical Thickness ◽  
Gray Matter Volume ◽  
Diagnostic Value ◽  
First Episode Psychosis ◽  
Current Evidence ◽  
First Episode ◽  
Structural Neuroimaging ◽  
Individual Level ◽  
Episode Psychosis

Abstract Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing literature. This study aimed to elucidate the extent to which the application of ML to neuroanatomical data allows detection of first episode psychosis (FEP), while putting in place methodological precautions to avoid overoptimistic results. We tested both traditional ML and an emerging approach known as deep learning (DL) using 3 feature sets of interest: (1) surface-based regional volumes and cortical thickness, (2) voxel-based gray matter volume (GMV) and (3) voxel-based cortical thickness (VBCT). To assess the reliability of the findings, we repeated all analyses in 5 independent datasets, totaling 956 participants (514 FEP and 444 within-site matched controls). The performance was assessed via nested cross-validation (CV) and cross-site CV. Accuracies ranged from 50% to 70% for surfaced-based features; from 50% to 63% for GMV; and from 51% to 68% for VBCT. The best accuracies (70%) were achieved when DL was applied to surface-based features; however, these models generalized poorly to other sites. Findings from this study suggest that, when methodological precautions are adopted to avoid overoptimistic results, detection of individuals in the early stages of psychosis is more challenging than originally thought. In light of this, we argue that the current evidence for the diagnostic value of ML and structural neuroimaging should be reconsidered toward a more cautious interpretation.

scholarly journals Predicting one-year outcome in first episode psychosis using machine learning

PLoS ONE ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. e0212846 ◽  
Author(s):  
Samuel P. Leighton ◽  
Rajeev Krishnadas ◽  
Kelly Chung ◽  
Alison Blair ◽  
Susie Brown ◽  
...  
Keyword(s):  
Machine Learning ◽  
First Episode Psychosis ◽  
First Episode ◽  
Episode Psychosis ◽  
One Year
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