Supervised machine learning to predict reduced depression severity in people with epilepsy through epilepsy self-management intervention

2022 ◽  
Vol 127 ◽  
pp. 108548
Author(s):  
Edward J. Camp ◽  
Robert J. Quon ◽  
Martha Sajatovic ◽  
Farren Briggs ◽  
Brittany Brownrigg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Self Management ◽  
Depression Severity ◽  
Management Intervention

scholarly journals Relative importance of perceived physical and social neighborhood characteristics for depression: a machine learning approach

2019 ◽  
Vol 55 (5) ◽  
pp. 599-610 ◽  
Author(s):  
Marco Helbich ◽  
Julian Hagenauer ◽  
Hannah Roberts
Keyword(s):  
Machine Learning ◽  
Low Income ◽  
Neighborhood Environment ◽  
Supervised Machine Learning ◽  
Relative Importance ◽  
Neighborhood Characteristics ◽  
Depression Severity ◽  
Cross Sectional ◽  
Neighborhood Environments ◽  
Low Educated

Abstract Purpose The physical and social neighborhood environments are increasingly recognized as determinants for depression. There is little evidence on combined effects of multiple neighborhood characteristics and their importance. Our aim was (1) to examine associations between depression severity and multiple perceived neighborhood environments; and (2) to assess their relative importance. Methods Cross-sectional data were drawn from a population-representative sample (N = 9435) from the Netherlands. Depression severity was screened with the Patient Health Questionnaire (PHQ-9) and neighborhood perceptions were surveyed. Supervised machine learning models were employed to assess depression severity-perceived neighborhood environment associations. Results We found indications that neighborhood social cohesion, pleasantness, and safety inversely correlate with PHQ-9 scores, while increasing perceived distance to green space and traffic were correlated positively. Perceived distance to blue space and urbanicity seemed uncorrelated. Young adults, low-income earners, low-educated, unemployed, and divorced persons were more likely to have higher PHQ-9 scores. Neighborhood characteristics appeared to be less important than personal attributes (e.g., age, marital and employment status). Results were robust across different ML models. Conclusions This study suggested that the perceived social environment plays, independent of socio-demographics, a role in depression severity. Contrasted with person-level and social neighborhood characteristics, the prominence of the physical neighborhood environment should not be overstated.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

2020 ◽  
Vol 28 (2) ◽  
pp. 253-265 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Amauri Duarte da Silva ◽  
Walter Filgueira de Azevedo
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Cyclin Dependent Kinase ◽  
Learning Models ◽  
Learning Techniques ◽  
Machine Learning Models

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

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