scholarly journals Predicting the side effects of drugs using matrix factorization on spontaneous reporting database

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kohei Fukuto ◽  
Tatsuya Takagi ◽  
Yu-Shi Tian
Keyword(s):  
Side Effects ◽  
Matrix Factorization ◽  
Side Effect ◽  
Cold Start ◽  
Factorization Method ◽  
Mapping Method ◽  
Spontaneous Reports ◽  
The Matrix ◽  
History Of ◽  
Cold Start Problem

AbstractThe severe side effects of some drugs can threaten the lives of patients and financially jeopardize pharmaceutical companies. Computational methods utilizing chemical, biological, and phenotypic features have been used to address this problem by predicting the side effects. Among these methods, the matrix factorization method, which utilizes the side-effect history of different drugs, has yielded promising results. However, approaches that encapsulate all the characteristics of side-effect prediction have not been investigated to date. To address this gap, we applied the logistic matrix factorization algorithm to a database of spontaneous reports to construct a prediction with higher accuracy. We expressed the distinction in the importance of drug-side effect pairs by a weighting strategy and addressed the cold-start problem via an attribute-to-feature mapping method. Consequently, our proposed model improved the prediction accuracy by 2.5% and efficiently handled the cold-start problem. The proposed methodology is expected to benefit applications such as warning systems in clinical settings.

scholarly journals Predicting the Side Effects of Drugs using Matrix Factorization on Spontaneous Reports Database

2021 ◽  
Author(s):  
Kohei Fukuto ◽  
Tatsuya Takagi ◽  
Yu-Shi Tian
Keyword(s):  
Side Effects ◽  
Matrix Factorization ◽  
Side Effect ◽  
Cold Start ◽  
Computational Techniques ◽  
Spontaneous Reports ◽  
Proposed Model ◽  
Feedback Data ◽  
The Impact ◽  
Cold Start Problem

Abstract Background Drugs with severe side effects can be threatening to patients and compromise pharmaceutical companies financially. Various computational techniques have been proposed to predict the side effects of drugs, including methods that utilize chemical, biological, and phenotypic features. Among them, matrix factorization (MF), which harnesses the known side effects of different drugs, has shown promising results. However, methods encapsulating all characteristics of side-effect prediction have not been investigated thus far. To this effect, we employed the logistic matrix factorization (Logistic MF) algorithm, i.e., MF modified for implicit feedback data, on a spontaneous reports database to improve the accuracy of side-effect prediction.Results A weighting strategy was applied to account for differences in the importance of the drug-side effect pairs. The impact of the cold-start problem and means to tackle it using the attribute-to-feature mapping were also explored. The experimental results demonstrate that the proposed model improved the prediction accuracy by 2.3% and efficiently handled the cold-start problem.Conclusion The proposed methodology is envisaged to benefit applications such as warning systems in clinical settings.

scholarly journals A Novel Triple Matrix Factorization Method for Detecting Drug-Side Effect Association Based on Kernel Target Alignment

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xiaoyi Guo ◽  
Wei Zhou ◽  
Yan Yu ◽  
Yijie Ding ◽  
Jijun Tang ◽  
...  
Keyword(s):  
Side Effects ◽  
Matrix Factorization ◽  
Side Effect ◽  
Multiple Kernel Learning ◽  
Factorization Method ◽  
Low Rank ◽  
Low Rank Approximation ◽  
Original Matrix ◽  
Multiple Kernel ◽  
Benchmark Datasets

All drugs usually have side effects, which endanger the health of patients. To identify potential side effects of drugs, biological and pharmacological experiments are done but are expensive and time-consuming. So, computation-based methods have been developed to accurately and quickly predict side effects. To predict potential associations between drugs and side effects, we propose a novel method called the Triple Matrix Factorization- (TMF-) based model. TMF is built by the biprojection matrix and latent feature of kernels, which is based on Low Rank Approximation (LRA). LRA could construct a lower rank matrix to approximate the original matrix, which not only retains the characteristics of the original matrix but also reduces the storage space and computational complexity of the data. To fuse multivariate information, multiple kernel matrices are constructed and integrated via Kernel Target Alignment-based Multiple Kernel Learning (KTA-MKL) in drug and side effect space, respectively. Compared with other methods, our model achieves better performance on three benchmark datasets. The values of the Area Under the Precision-Recall curve (AUPR) are 0.677, 0.685, and 0.680 on three datasets, respectively.

Dermal Discoloration Secondary to Triamcinolone Acetonide Injection Observed in Patients with Autoimmune Disorders

2021 ◽  
Vol 111 (4) ◽  
Author(s):  
James A. Wright ◽  
Jessica A. Wenz ◽  
Gabrielle Jackson Madrigal
Keyword(s):  
Psoriatic Arthritis ◽  
Side Effects ◽  
Autoimmune Disease ◽  
Triamcinolone Acetonide ◽  
Current Literature ◽  
Side Effect ◽  
Inflammatory Conditions ◽  
Rare Side Effect ◽  
History Of ◽  
Synthetic Glucocorticoid

Triamcinolone acetonide is a synthetic glucocorticoid used to treat numerous acute and chronic inflammatory conditions. The various side effects of this drug from parenteral administration are well documented in the literature. In this study, three patients present with a rare side effect of violaceous dermal pigmentation. To the best of the authors' knowledge, this finding is rarely presented in the current literature. The purpose of this study is to provide awareness of a less-documented, delayed side effect from triamcinolone acetonide administration. Although all patients presenting in this study had a known history of autoimmune disease (eg, lupus, psoriatic arthritis) further research is needed to suggest a possible association between dermal violaceous change and the use of triamcinolone.

scholarly journals Lossless Linear Integer signal Resampling

2012 ◽  
pp. 225-233
Author(s):  
S.Raghavendra Prasad ◽  
Dr.P.Ramana Reddy
Keyword(s):  
Matrix Factorization ◽  
High Frequency ◽  
Irreversible Process ◽  
Polynomial Interpolation ◽  
Factorization Method ◽  
Special Cases ◽  
The Matrix ◽  
Frequency Components ◽  
Linear Transform ◽  
Signal Resampling

This paper describes about signal resampling based on polynomial interpolation is reversible for all types of signals, i.e., the original signal can be reconstructed losslessly from the resampled data. This paper also discusses Matrix factorization method for reversible uniform shifted resampling and uniform scaled and shifted resampling. Generally, signal resampling is considered to be irreversible process except in some special cases because of strong attenuation of high frequency components. The matrix factorization method is actually a new way to compute linear transform. The factorization yields three elementary integer-reversible matrices. This method is actually a lossless integer-reversible implementation of linear transform. Some examples of lower order resampling solutions are also presented in this paper.

A case of sensorineural deafness following ingestion of Ecstasy

2001 ◽  
Vol 115 (11) ◽  
pp. 911-915 ◽  
Author(s):  
A. Sharma
Keyword(s):  
Side Effects ◽  
Young People ◽  
Side Effect ◽  
Sensorineural Deafness ◽  
History Of

Ecstasy is a substance of abuse commonly associated with the dance scene and taken by many young people. A brief history of Ecstasy and its side-effects is given. A case of ototoxicity is presented, as an additional side-effect to the long list of complications caused by Ecstasy.

Increased libido as a buproion-SR side effect: Clinical description of a case

2016 ◽  
Vol 33 (S1) ◽  
pp. S545-S545 ◽  
Author(s):  
L. Gallardo Borge ◽  
C. Noval Canga ◽  
L. Rodíguez Andrés ◽  
I. Sevillano Benito ◽  
M. Hernández García ◽  
...  
Keyword(s):  
Side Effects ◽  
Sexual Dysfunction ◽  
Side Effect ◽  
Muscle Tension ◽  
Negative Influence ◽  
Sexual Life ◽  
Clinical Report ◽  
Consultation Room ◽  
Sexual Side Effects ◽  
History Of

IntroductionBupropion is a dual antidepressant, a norepinephrine and dopamine reuptake inhibitor. Its main use is in affective disorders as major depression. Antidepressants have been commonly associated with sexual side effects in the libido, sexual arousal, orgasm and erectile function. Bupropion has negative influence in sexual function, even it could increase the libido. Due to this, it could be a good option in patients with active sexual life and affective disorder.Clinical reportA 58-year-old female with a long history of depression disorder for 5 years. History of lots of side effects with different treatments, sexual dysfunction with serotonin-antidepressants. Treated with bupropion SR 150 mg/day and alprazolam, she suffered a relapse. The bupropion was increased to 300 mg/day. Three days later she appeared in the consultation room, presented a sense of pre-orgasmic of 72 hours of evolution, high increased libido, tiredness, muscle tension and insomnia. This sense did not improve after the sexual act. It had never happened previously. The side effect improved when the bupropion was reduced to 150 mg/day and disappeared with its withdrawal.ConclusionsThe case made a relationship between the increased of bupropion's dose and the appearance of unusual sexual side effects (increased of libido and pre-orgasmic sense). Not only bupropion is one of the antidepressants that do not cause sexual dysfunction, if not it was reported in some trials that could be a treatment against this dysfunction due to its prosexual effects. The mechanism is unknown but could be related with norepinephrine or dopamine transmission.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals A Convolutional Neural Network and Matrix Factorization-Based Travel Location Recommendation Method Using Community-Contributed Geotagged Photos

2020 ◽  
Vol 9 (8) ◽  
pp. 464
Author(s):  
Thaair Ameen ◽  
Ling Chen ◽  
Zhenxing Xu ◽  
Dandan Lyu ◽  
Hongyu Shi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Matrix Factorization ◽  
Factorization Method ◽  
Experimental Results ◽  
Latent Factor ◽  
Location Recommendation ◽  
Weighted Matrix ◽  
Geotagged Photos ◽  
Cold Start Problem

Travel location recommendation methods using community-contributed geotagged photos are based on past check-ins. Therefore, these methods cannot effectively work for new travel locations, i.e., they suffer from the travel location cold start problem. In this study, we propose a convolutional neural network and matrix factorization-based travel location recommendation method to address the problem. Specifically, a weighted matrix factorization method is used to obtain the latent factor representations of travel locations. The latent factor representation for a new travel location is estimated from its photos by using a convolutional neural network. Experimental results on a Flickr dataset demonstrate that the proposed method can provide better recommendations than existing methods.

scholarly journals Safety Monitoring of COVID Vaccines and Perception of Post-vaccination Side-Effects: Preliminary Findings From The First Month of Routine Monitoring in a Hospital Vaccination Setting of China

2021 ◽  
Author(s):  
Aihua Feng ◽  
Ruoyan Gai Tobe ◽  
Yongqiang Wang ◽  
Ting Yang ◽  
Xiuting Mo ◽  
...  
Keyword(s):  
Immune Deficiency ◽  
Side Effects ◽  
Side Effect ◽  
Safety Monitoring ◽  
Affecting Factors ◽  
Post Vaccination ◽  
Perceived Safety ◽  
Vaccination Uptake ◽  
Routine Monitoring ◽  
History Of

Abstract Objectives: This study aims to explore the occurrence of post-vaccination side-effects from COVID-19 vaccines and its affecting factors in a hospital vaccination setting of China.Results: A total of 811 vaccinees aged 17 to 58 years, who finished the full package of two doses in February 2021, have been recruited at the second vaccination uptake. Among all, there have been 66 participants who reported one or more mild side effects, while none of them developed severe cases. Those with history of immune deficiency were more likely to report side effect(s). Although with several concerns, most participants showed willingness to get vaccinated (98.8%) with relevant high proportions of perceived safety (99.5%) and effectiveness (97.3%).

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