A Diverse Assimilation of Sequence and Structure Dependent Features for Amyloid Plaque Prediction Using Random Forests

2013 ◽  
Vol 10 (1) ◽  
pp. 38-44
Author(s):  
Smitha Sunil Nair ◽  
N. V. Reddy ◽  
K. Hareesha ◽  
S. Balaji
2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  

**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.


2020 ◽  
Vol 19 (9) ◽  
pp. 676-690 ◽  
Author(s):  
Roma Ghai ◽  
Kandasamy Nagarajan ◽  
Meenakshi Arora ◽  
Parul Grover ◽  
Nazakat Ali ◽  
...  

Alzheimer’s Disease (AD) is a chronic, devastating dysfunction of neurons in the brain leading to dementia. It mainly arises due to neuronal injury in the cerebral cortex and hippocampus area of the brain and is clinically manifested as a progressive mental failure, disordered cognitive functions, personality changes, reduced verbal fluency and impairment of speech. The pathology behind AD is the formation of intraneuronal fibrillary tangles, deposition of amyloid plaque and decline in choline acetyltransferase and loss of cholinergic neurons. Tragically, the disease cannot be cured, but its progression can be halted. Various cholinesterase inhibitors available in the market like Tacrine, Donepezil, Galantamine, Rivastigmine, etc. are being used to manage the symptoms of Alzheimer’s disease. The paper’s objective is to throw light not only on the cellular/genetic basis of the disease, but also on the current trends and various strategies of treatment including the use of phytopharmaceuticals and nutraceuticals. Enormous literature survey was conducted and published articles of PubMed, Scifinder, Google Scholar, Clinical Trials.org and Alzheimer Association reports were studied intensively to consolidate the information on the strategies available to combat Alzheimer’s disease. Currently, several strategies are being investigated for the treatment of Alzheimer’s disease. Immunotherapies targeting amyloid-beta plaques, tau protein and neural pathways are undergoing clinical trials. Moreover, antisense oligonucleotide methodologies are being approached as therapies for its management. Phytopharmaceuticals and nutraceuticals are also gaining attention in overcoming the symptoms related to AD. The present review article concludes that novel and traditional therapies simultaneously promise future hope for AD treatment.


Sign in / Sign up

Export Citation Format

Share Document