scholarly journals Channel selection for epilepsy seizure prediction method based on machine learning

2012 ◽  
Author(s):  
Nai-Fu Chang ◽  
Tung-Chien Chen ◽  
Cheng-Yi Chiang ◽  
Liang-Gee Chen
Keyword(s):  
Machine Learning ◽  
Prediction Method ◽  
Channel Selection ◽  
Seizure Prediction ◽  
Selection For

Identifying the underlying factors associated with antidepressant drug discontinuation: Content analysis of patients’ drug reviews (Preprint)

2020 ◽  
Author(s):  
Mohammad Alarifi ◽  
Somaieh Goudarzvand3 ◽  
Abdulrahman Jabour ◽  
Doreen Foy ◽  
Maryam Zolnoori
Keyword(s):  
Machine Learning ◽  
Antidepressant Drug ◽  
Prediction Method ◽  
Analytical Framework ◽  
Structured Data ◽  
Withdrawal Symptoms ◽  
Machine Learning Techniques ◽  
Drug Discontinuation ◽  
Factors Associated ◽  
Learning Techniques

BACKGROUND The rate of antidepressant prescriptions is globally increasing. A large portion of patients stop their medications which could lead to many side effects including relapse, and anxiety. OBJECTIVE The aim of this was to develop a drug-continuity prediction model and identify the factors associated with drug-continuity using online patient forums. METHODS We retrieved 982 antidepressant drug reviews from the online patient’s forum AskaPatient.com. We followed the Analytical Framework Method to extract structured data from unstructured data. Using the structured data, we examined the factors associated with antidepressant discontinuity and developed a predictive model using multiple machine learning techniques. RESULTS We tested multiple machine learning techniques which resulted in different performances ranging from accuracy of 65% to 82%. We found that Radom Forest algorithm provides the highest prediction method with 82% Accuracy, 78% Precision, 88.03% Recall, and 84.2% F1-Score. The factors associated with drug discontinuity the most were; withdrawal symptoms, effectiveness-ineffectiveness, perceived-distress-adverse drug reaction, rating, and perceived-distress related to withdrawal symptoms. CONCLUSIONS Although the nature of data available at online forums differ from data collected through surveys, we found that online patients forum can be a valuable source of data for drug-continuity prediction and understanding patients experience. The factors identified through our techniques were consistent with the findings of prior studies that used surveys.

scholarly journals IIMLP: integrated information-entropy-based method for LncRNA prediction

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Junyi Li ◽  
Huinian Li ◽  
Xiao Ye ◽  
Li Zhang ◽  
Qingzhe Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Sequences ◽  
Information Entropy ◽  
Area Under The Curve ◽  
Prediction Method ◽  
Machine Learning Algorithms ◽  
Reading Frame ◽  
Non Coding Rna ◽  
The One ◽  
Long Non Coding Rna

Abstract Background The prediction of long non-coding RNA (lncRNA) has attracted great attention from researchers, as more and more evidence indicate that various complex human diseases are closely related to lncRNAs. In the era of bio-med big data, in addition to the prediction of lncRNAs by biological experimental methods, many computational methods based on machine learning have been proposed to make better use of the sequence resources of lncRNAs. Results We developed the lncRNA prediction method by integrating information-entropy-based features and machine learning algorithms. We calculate generalized topological entropy and generate 6 novel features for lncRNA sequences. By employing these 6 features and other features such as open reading frame, we apply supporting vector machine, XGBoost and random forest algorithms to distinguish human lncRNAs. We compare our method with the one which has more K-mer features and results show that our method has higher area under the curve up to 99.7905%. Conclusions We develop an accurate and efficient method which has novel information entropy features to analyze and classify lncRNAs. Our method is also extendable for research on the other functional elements in DNA sequences.

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