AIMC Topic: Schizophrenia
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Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach.
BMC medical informatics and decision making
Mar 13, 2019
BACKGROUND: Medications are frequently used for treating schizophrenia, however, anti-psychotic drug use is known to lead to cases of pneumonia. The purpose of our study is to build a model for predicting hospital-acquired pneumonia among schizophren...
Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder.
NeuroImage. Clinical
Feb 18, 2019
Brain imaging studies have revealed that functional and structural brain connectivity in the so-called triple network (i.e., default mode network (DMN), salience network (SN) and central executive network (CEN)) are consistently altered in schizophre...
Resting-state anticorrelated networks in Schizophrenia.
Psychiatry research. Neuroimaging
Dec 25, 2018
Converging evidences from different lines of research suggest abnormalities in functional brain connectivity in schizophrenia. While positively correlated brain networks have been well researched, anticorrelated functional connectivity remains under ...
Machine learning for predicting psychotic relapse at 2 years in schizophrenia in the national FACE-SZ cohort.
Progress in neuro-psychopharmacology & biological psychiatry
Dec 12, 2018
BACKGROUND: Predicting psychotic relapse is one of the major challenges in the daily care of schizophrenia.
From cognitive and clinical substrates to functional profiles: Disentangling heterogeneity in schizophrenia.
Psychiatry research
Dec 6, 2018
The relationship between neurocognition and functioning among patients with schizophrenia is well documented. However, integrating neuropsychological, clinical and psychopathological data to better investigate functional outcome still constitutes a c...
Disease comorbidity-guided drug repositioning: a case study in schizophrenia.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Dec 5, 2018
UNLABELLED: The key to any computational drug repositioning is the availability of relevant data in machine-understandable format. While large amount of genetic, genomic and chemical data are publicly available, large-scale higher-level disease and d...
Supervised machine learning to decipher the complex associations between neuro-immune biomarkers and quality of life in schizophrenia.
Metabolic brain disease
Nov 22, 2018
Stable phase schizophrenia is characterized by altered patterning in tryptophan catabolites (TRYCATs) and memory impairments, which are associated with PHEMN (psychosis, hostility, excitation, mannerism and negative) and DAPS (depression, anxiety and...
Comparative Evaluation of Machine Learning Strategies for Analyzing Big Data in Psychiatry.
International journal of molecular sciences
Oct 29, 2018
The requirement of innovative big data analytics has become a critical success factor for research in biological psychiatry. Integrative analyses across distributed data resources are considered essential for untangling the biological complexity of m...
Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study.
Human brain mapping
Oct 11, 2018
Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large nu...
Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis.
NeuroImage. Clinical
Oct 10, 2018
Cognitive behavioural therapy for psychosis (CBTp) involves helping patients to understand and reframe threatening appraisals of their psychotic experiences to reduce distress and increase functioning. Whilst CBTp is effective for many, it is not eff...
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