Precipitating factors for falls among patients with dementia on a psychogeriatric ward

2010 ◽  
Vol 22 (4) ◽  
pp. 641-649 ◽  
Author(s):  
Sofie Tängman ◽  
Staffan Eriksson ◽  
Yngve Gustafson ◽  
Lillemor Lundin-Olsson
Keyword(s):  
Side Effects ◽  
Side Effect ◽  
Hospital Ward ◽  
Acute Disease ◽  
Drug Side Effects ◽  
Precipitating Factors ◽  
Drug Side Effect ◽  
The North ◽  
People With Dementia ◽  
Relevant Factors

ABSTRACTBackground:Predisposing factors alone explain only a limited proportion of the variation in fall events, especially in people with dementia. The aim of this study was to identify precipitating factors for falls among people with dementia.Methods:We examined prospective fall registrations over a two-year period on a psychogeriatric hospital ward in the north of Sweden. Circumstances associated with each fall event were analyzed by independent reviewers, possible precipitating factors were documented, evaluated and the most likely precipitating factors were identified. In total, 223 patients with any type of diagnosed dementia were admitted to the ward and 91 fell at least once. Of these, 46 were women and 45 were men (mean age 80.3 years, range 60–94).Results:A total of 298 falls were registered, 62% of which were sustained by men. The most likely factor or combination of factors could be ascertained in 247 falls (83%). Falls took place at all hours but almost half of the falls (44%) occurred during the nightshift (between 9pm and 7am). Acute disease or symptoms of disease and/or acute drug side-effects were, alone or in combination with other factors, judged to precipitate more than three out of four falls.Conclusion:It is possible to identify many precipitating factors that may contribute to a fall. Falls in people with dementia should be regarded as a symptom of acute disease or as a drug side-effect until proven otherwise. Prompt detection of these relevant factors is, therefore, essential.

scholarly journals MONITORING OF ANTICONVULSANT DRUG SIDE EFFECTS IN OUTPATIENTS WITH EPILEPSY

2018 ◽  
Vol 10 (1) ◽  
pp. 303
Author(s):  
Santi Purna Sari ◽  
Natasha Kurnia Salma S ◽  
Alfina Rianti
Keyword(s):  
Valproic Acid ◽  
Side Effects ◽  
Medical Records ◽  
Side Effect ◽  
Anticonvulsant Drug ◽  
Secondary Data ◽  
Drug Side Effects ◽  
Inclusion Criteria ◽  
Descriptive Data ◽  
Effect Monitoring

Objective: This study aimed to monitor the side effects of carbamazepine, phenytoin, and valproic acid, and combinations of these drugs in adultpatients with epilepsy, to raise awareness of the importance of drug side effect monitoring in hospitals.Methods: In this prospective study, descriptive data were collected from patients who met the inclusion criteria of complete samples. Primary datawere obtained using questionnaires, secondary data were collected from medical records, and analyses were performed using the Naranjo algorithm.Results: Among the 54 included patients, 38 (70.37%) of them experienced drug side effects, and the most frequently observed side effect occurredin 48.15% of study subjects.Conclusion: No correlation was identified between side effects and age (p=0.903) or gender (p=1.000).

scholarly journals Predicting Drug Indications and Side Effects Using Deep Learning and Transfer Learning

2021 ◽  
Vol 36 (1) ◽  
pp. 281-289
Author(s):  
D. Mohanapriya ◽  
Dr.R. Beena
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Side Effects ◽  
Transfer Learning ◽  
Language Processing ◽  
Side Effect ◽  
Topic Modeling ◽  
Distance Measure ◽  
Classification And Regression Tree ◽  
Drug Side Effect

In the area of biology, text mining is commonly used since it obtains the unknown relationship among medicines, phenotypes and syndromes from much information. Enhanced Topic modeling with Improved Predict drug Indications and Side effects using Topic modelling and Natural language processing (ETP-IPISTON) has been employed to predict the drug-phenotype and drug-side effect association. Initially, corpus documents are collected from the literature data and the topics in the data are modeled using logistic Linear Discriminative Analysis (LDA) and Bi-directional Long-Short Term Memory-Conditional Random Field (BILSTM-CRF). From the sentences in the literature data, a dependency graph was constructed which discovered the relations between gene and drug. The product of the drug on phenotype rule was identified by the Gene Regulation Score (GRS) which creates the drug-topic probability matrix. The probability matrix and a syntactic distance measure was processed in Classification and Regression Tree (CART), Naïve Bayes (NB), logistic regression and Convolutional Neural Network (CNN) classifiers for estimating the drug-gene and drug-side effects. Besides the literature data, social media offers various promising resources with massive volume of data that can be useful in the drug-phenotype and drug-side effect association prediction. So in this paper, drug information with gene, disease and side effects are extracted from different social media such as Twitter, Facebook and LinkedIn and it can be used with the literature data to provide more relevant disease and drug relations. In addition to this, topic modeling with transfer learning is introduced to consider the element categories, probability of overlapping elements and deep contextual significance of a text for better modeling of topics. The topic modeling with transfer learning shares as much knowledge as possible between the literature data and social media information for topic modeling. The topics from social media and literature data are used for creating the drug-topic matrix. The probability matrix and syntactic distance measure are given as input to CART, NB, logistic regression and CNN for estimating the drug-gene and drug-side effect association. This proposed work is named as Enhanced Topic Modeling with Transfer Leaning- IPISTON (ETPTL-IPISTON). The simulation findings exhibit that the efficiency of ETPTL-IPISTON than the traditional methods.

scholarly journals The Geometric Sparse Matrix Completion Model for Predicting Drug Side effects

2019 ◽  
Author(s):  
Diego Galeano ◽  
Alberto Paccanaro
Keyword(s):  
Clinical Trials ◽  
Side Effects ◽  
Side Effect ◽  
Large Scale ◽  
Learning Algorithm ◽  
Sparse Matrix ◽  
Matrix Completion ◽  
Optimal Solution ◽  
Drug Side Effects ◽  
Chemical Structures

AbstractPair-input associations for drug-side effects are obtained through expensive placebo-controlled experiments in human clinical trials. An important challenge in computational pharmacology is to predict missing associations given a few entries in the drug-side effect matrix, as these predictions can be used to direct further clinical trials. Here we introduce the Geometric Sparse Matrix Completion (GSMC) model for predicting drug side effects. Our high-rank matrix completion model learns non-negative sparse matrices of coefficients for drugs and side effects by imposing smoothness priors that exploit a set of pharmacological side information graphs, including information about drug chemical structures, drug interactions, molecular targets, and disease indications. Our learning algorithm is based on the diagonally rescaled gradient descend principle of non-negative matrix factorization. We prove that it converges to a globally optimal solution with a first-order rate of convergence. Experiments on large-scale side effect data from human clinical trials show that our method achieves better prediction performance than six state-of-the-art methods for side effect prediction while offering biological interpretability and favouring explainable predictions.

scholarly journals Relating Substructures and Side Effects of Drugs with Chemical-chemical Interactions

2020 ◽  
Vol 23 (4) ◽  
pp. 285-294 ◽  
Author(s):  
Bo Zhou ◽  
Xian Zhao ◽  
Jing Lu ◽  
Zuntao Sun ◽  
Min Liu ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Side Effects ◽  
Computational Methods ◽  
Side Effect ◽  
Permutation Test ◽  
Human Life ◽  
Statistical Significance ◽  
Drug Side Effects ◽  
Computational Investigation ◽  
The Relationship

Background:Drugs are very important for human life because they can provide treatment, cure, prevention, or diagnosis of different diseases. However, they also cause side effects, which can increase the risks for humans and pharmaceuticals companies. It is essential to identify drug side effects in drug discovery. To date, lots of computational methods have been proposed to predict the side effects of drugs and most of them used the fact that similar drugs always have similar side effects. However, previous studies did not analyze which substructures are highly related to which kind of side effect.Method:In this study, we conducted a computational investigation. In this regard, we extracted a drug set for each side effect, which consisted of drugs having the side effect. Also, for each substructure, a set was constructed by picking up drugs owing such substructure. The relationship between one side effect and one substructure was evaluated based on linkages between drugs in their corresponding drug sets, resulting in an Es value. Then, the statistical significance of Es value was measured by a permutation test.Results and Conclusion:A number of highly related pairs of side effects and substructures were obtained and some were extensively analyzed to confirm the reliability of the results reported in this study.

Predicting new drug indications from network analysis

2017 ◽  
Vol 28 (09) ◽  
pp. 1750118
Author(s):  
Yousoff Effendy Mohd Ali ◽  
Kiam Heong Kwa ◽  
Kurunathan Ratnavelu
Keyword(s):  
Side Effects ◽  
Network Analysis ◽  
Side Effect ◽  
Drug Repositioning ◽  
Centrality Measures ◽  
Drug Side Effect ◽  
Alternative Approach ◽  
Therapeutic Properties ◽  
Basic Hypothesis ◽  
Optimum Threshold

This work adapts centrality measures commonly used in social network analysis to identify drugs with better positions in drug-side effect network and drug-indication network for the purpose of drug repositioning. Our basic hypothesis is that drugs having similar phenotypic profiles such as side effects may also share similar therapeutic properties based on related mechanism of action and vice versa. The networks were constructed from Side Effect Resource (SIDER) 4.1 which contains 1430 unique drugs with side effects and 1437 unique drugs with indications. Within the giant components of these networks, drugs were ranked based on their centrality scores whereby 18 prominent drugs from the drug-side effect network and 15 prominent drugs from the drug-indication network were identified. Indications and side effects of prominent drugs were deduced from the profiles of their neighbors in the networks and compared to existing clinical studies while an optimum threshold of similarity among drugs was sought for. The threshold can then be utilized for predicting indications and side effects of all drugs. Similarities of drugs were measured by the extent to which they share phenotypic profiles and neighbors. To improve the likelihood of accurate predictions, only profiles such as side effects of common or very common frequencies were considered. In summary, our work is an attempt to offer an alternative approach to drug repositioning using centrality measures commonly used for analyzing social networks.

Predicting Drug Side Effects with Compact Integration of Heterogeneous Networks

2019 ◽  
Vol 14 (8) ◽  
pp. 709-720 ◽  
Author(s):  
Xian Zhao ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Tao Liu
Keyword(s):  
Side Effects ◽  
Network Model ◽  
Heterogeneous Networks ◽  
Side Effect ◽  
Correlation Coefficients ◽  
Machine Learning Algorithms ◽  
Drug Side Effects ◽  
Essential Information ◽  
Approved Drugs ◽  
Feature Dimension

Background: The side effects of drugs are not only harmful to humans but also the major reasons for withdrawing approved drugs, bringing greater risks for pharmaceutical companies. However, detecting the side effects for a given drug via traditional experiments is time- consuming and expensive. In recent years, several computational methods have been proposed to predict the side effects of drugs. However, most of the methods cannot effectively integrate the heterogeneous properties of drugs. Methods: In this study, we adopted a network embedding method, Mashup, to extract essential and informative drug features from several drug heterogeneous networks, representing different properties of drugs. For side effects, a network was also built, from where side effect features were extracted. These features can capture essential information about drugs and side effects in a network level. Drug and side effect features were combined together to represent each pair of drug and side effect, which was deemed as a sample in this study. Furthermore, they were fed into a random forest (RF) algorithm to construct the prediction model, called the RF network model. Results: The RF network model was evaluated by several tests. The average of Matthews correlation coefficients on the balanced and unbalanced datasets was 0.640 and 0.641, respectively. Conclusion: The RF network model was superior to the models incorporating other machine learning algorithms and one previous model. Finally, we also investigated the influence of two feature dimension parameters on the RF network model and found that our model was not very sensitive to these parameters.

scholarly journals Advances in Understanding the Initial Steps of Pruritoceptive Itch: How the Itch Hits the Switch

2020 ◽  
Vol 21 (14) ◽  
pp. 4883
Author(s):  
Shirin Kahremany ◽  
Lukas Hofmann ◽  
Arie Gruzman ◽  
Guy Cohen
Keyword(s):  
Side Effects ◽  
Molecular Mechanism ◽  
Side Effect ◽  
Drug Side Effects ◽  
Stinging Nettle ◽  
Comprehensive View ◽  
Itch Sensation ◽  
Critical Aspects

Pruritoceptive (dermal) itch was long considered an accompanying symptom of diseases, a side effect of drug applications, or a temporary sensation induced by invading pruritogens, as produced by the stinging nettle. Due to extensive research in recent years, it was possible to provide detailed insights into the mechanism of itch mediation and modulation. Hence, it became apparent that pruritus is a complex symptom or disease in itself, which requires particular attention to improve patients’ health. Here, we summarize recent findings in pruritoceptive itch, including how this sensation is triggered and modulated by diverse endogenous and exogenous pruritogens and their receptors. A differentiation between mediating pruritogen and modulating pruritogen seems to be of great advantage to understand and decipher the molecular mechanism of itch perception. Only a comprehensive view on itch sensation will provide a solid basis for targeting this long-neglected adverse sensation accompanying numerous diseases and many drug side effects. Finally, we identify critical aspects of itch perception that require future investigation.

Sign in / Sign up

Export Citation Format

Share Document