scholarly journals Clinical indicators of treatment-resistant psychosis

2019 ◽  
Vol 216 (5) ◽  
pp. 259-266 ◽  
Author(s):  
Sophie E. Legge ◽  
Charlotte A. Dennison ◽  
Antonio F. Pardiñas ◽  
Elliott Rees ◽  
Amy J. Lynham ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Adjustment ◽  
Odds Ratio ◽  
Age At Onset ◽  
Learning Approaches ◽  
Regression Analyses ◽  
Treatment Resistant ◽  
Genetic Liability ◽  
Illness Onset ◽  
Risk Of Treatment

BackgroundAround 30% of individuals with schizophrenia remain symptomatic and significantly impaired despite antipsychotic treatment and are considered to be treatment resistant. Clinicians are currently unable to predict which patients are at higher risk of treatment resistance.AimsTo determine whether genetic liability for schizophrenia and/or clinical characteristics measurable at illness onset can prospectively indicate a higher risk of treatment-resistant psychosis (TRP).MethodIn 1070 individuals with schizophrenia or related psychotic disorders, schizophrenia polygenic risk scores (PRS) and large copy number variations (CNVs) were assessed for enrichment in TRP. Regression and machine-learning approaches were used to investigate the association of phenotypes related to demographics, family history, premorbid factors and illness onset with TRP.ResultsYounger age at onset (odds ratio 0.94,P= 7.79 × 10−13) and poor premorbid social adjustment (odds ratio 1.64,P= 2.41 × 10−4) increased risk of TRP in univariate regression analyses. These factors remained associated in multivariate regression analyses, which also found lower premorbid IQ (odds ratio 0.98,P= 7.76 × 10−3), younger father's age at birth (odds ratio 0.97,P= 0.015) and cannabis use (odds ratio 1.60,P= 0.025) increased the risk of TRP. Machine-learning approaches found age at onset to be the most important predictor and also identified premorbid IQ and poor social adjustment as predictors of TRP, mirroring findings from regression analyses. Genetic liability for schizophrenia was not associated with TRP.ConclusionsPeople with an earlier age at onset of psychosis and poor premorbid functioning are more likely to be treatment resistant. The genetic architecture of susceptibility to schizophrenia may be distinct from that of treatment outcomes.

scholarly journals Antipsychotic treatment resistance in first-episode psychosis: prevalence, subtypes and predictors

2017 ◽  
Vol 47 (11) ◽  
pp. 1981-1989 ◽  
Author(s):  
A. Demjaha ◽  
J. M. Lappin ◽  
D. Stahl ◽  
M. X. Patel ◽  
J. H. MacCabe ◽  
...  
Keyword(s):  
Negative Symptoms ◽  
Age At Onset ◽  
Treatment Resistance ◽  
First Episode Psychosis ◽  
First Episode ◽  
Antipsychotic Treatment ◽  
Treatment Resistant ◽  
Illness Onset ◽  
Episode Psychosis

BackgroundWe examined longitudinally the course and predictors of treatment resistance in a large cohort of first-episode psychosis (FEP) patients from initiation of antipsychotic treatment. We hypothesized that antipsychotic treatment resistance is: (a) present at illness onset; and (b) differentially associated with clinical and demographic factors.MethodThe study sample comprised 323 FEP patients who were studied at first contact and at 10-year follow-up. We collated clinical information on severity of symptoms, antipsychotic medication and treatment adherence during the follow-up period to determine the presence, course and predictors of treatment resistance.ResultsFrom the 23% of the patients, who were treatment resistant, 84% were treatment resistant from illness onset. Multivariable regression analysis revealed that diagnosis of schizophrenia, negative symptoms, younger age at onset, and longer duration of untreated psychosis predicted treatment resistance from illness onset.ConclusionsThe striking majority of treatment-resistant patients do not respond to first-line antipsychotic treatment even at time of FEP. Clinicians must be alert to this subgroup of patients and consider clozapine treatment as early as possible during the first presentation of psychosis.

scholarly journals Machine Learning Approaches to Predict Hepatotoxicity Risk in Patients Receiving Nilotinib

Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3300
Author(s):  
Jung-Sun Kim ◽  
Ji-Min Han ◽  
Yoon-Sook Cho ◽  
Kyung-Hee Choi ◽  
Hye-Sun Gwak
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Odds Ratio ◽  
Clinical Performance ◽  
Area Under The Curve ◽  
High Dose ◽  
Learning Approaches ◽  
Factors Affecting ◽  
H2 Blockers ◽  
Increased Risk

Background: Although nilotinib hepatotoxicity can cause severe clinical conditions and may alter treatment plans, risk factors affecting nilotinib-induced hepatotoxicity have not been investigated. This study aimed to elucidate the factors affecting nilotinib-induced hepatotoxicity. Methods: This retrospective cohort study was performed on patients using nilotinib from July of 2015 to June of 2020. We estimated the odds ratio and adjusted odds ratio from univariate and multivariate analyses, respectively. Several machine learning models were developed to predict risk factors of hepatotoxicity occurrence. The area under the curve (AUC) was analyzed to assess clinical performance. Results: Among 353 patients, the rate of patients with grade I or higher hepatotoxicity after nilotinib administration was 40.8%. Male patients and patients who received nilotinib at a dose of ≥300 mg had a 2.3-fold and a 3.5-fold increased risk for hepatotoxicity compared to female patients and compared with those who received <300 mg, respectively. H2 blocker use decreased hepatotoxicity by 11.6-fold. The area under the curve (AUC) values of machine learning methods ranged between 0.61–0.65 in this study. Conclusion: This study suggests that the use of H2 blockers was a reduced risk of nilotinib-induced hepatotoxicity, whereas male gender and a high dose were associated with increased hepatotoxicity.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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