scholarly journals A feasibility study on identifying drinking-related contents in Facebook through mining heterogeneous data

2018 ◽  
Vol 25 (4) ◽  
pp. 1756-1767 ◽  
Author(s):  
Omar ElTayeby ◽  
Todd Eaglin ◽  
Malak Abdullah ◽  
David Burlinson ◽  
Wenwen Dou ◽  
...  
Keyword(s):  
Social Media ◽  
Feasibility Study ◽  
Prediction Models ◽  
Heterogeneous Data ◽  
Video Data ◽  
Data Types ◽  
Text Data ◽  
Related Text ◽  
Text Images ◽  
Severe Health Problem

Binge drinking is a severe health problem faced by many US colleges and universities. College students often post drinking-related text and images on social media, portraying their alcohol use as socially desirable. In this project, we investigated the feasibility of mining the heterogeneous data (e.g. text, images, and videos) on Facebook to identify drinking-related contents. We manually annotated 4266 posts during 21 October 2011 and 3 November 2014 from “I’m Shmacked” group on Facebook, where 511 posts were drinking-related. Our machine learning models show that by combining heterogeneous data types, we were able to identify drinking-related posts with an F1-score of 0.81. Prediction models built on text data were more reliable compared to those built on image and video data for predicting drinking-related contents. As the first step of our efforts in this direction, this feasibility study showed promise toward unleashing the potential of mining social media to identify students who binge drink.

scholarly journals Two-step multi-omics modelling of drug sensitivity in cancer cell lines to identify driving mechanisms

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0238961
Author(s):  
Nina Kusch ◽  
Andreas Schuppert
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Prediction Models ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
Heterogeneous Data ◽  
Omics Data ◽  
Classification Models ◽  
Data Types

Drug sensitivity prediction models for human cancer cell lines constitute important tools in identifying potential computational biomarkers for responsiveness in a pre-clinical setting. Integrating information derived from a range of heterogeneous data is crucial, but remains non-trivial, as differences in data structures may hinder fitting algorithms from assigning adequate weights to complementary information that is contained in distinct omics data. In order to counteract this effect that tends to lead to just one data type dominating supposedly multi-omics models, we developed a novel tool that enables users to train single-omics models separately in a first step and to integrate them into a multi-omics model in a second step. Extensive ablation studies are performed in order to facilitate an in-depth evaluation of the respective contributions of singular data types and of combinations thereof, effectively identifying redundancies and interdependencies between them. Moreover, the integration of the single-omics models is realized by a range of distinct classification algorithms, thus allowing for a performance comparison. Sets of molecular events and tissue types found to be related to significant shifts in drug sensitivity are returned to facilitate a comprehensive and straightforward analysis of potential computational biomarkers for drug responsiveness. Our two-step approach yields sets of actual multi-omics pan-cancer classification models that are highly predictive for a majority of drugs in the GDSC data base. In the context of targeted drugs with particular modes of action, its predictive performances compare favourably to those of classification models that incorporate multi-omics data in a simple one-step approach. Additionally, case studies demonstrate that it succeeds both in correctly identifying known key biomarkers for sensitivity towards specific drug compounds as well as in providing sets of potential candidates for additional computational biomarkers.

scholarly journals Combining structured and unstructured data for predictive models: a deep learning approach

2020 ◽  
Author(s):  
Dongdong Zhang ◽  
Changchang Yin ◽  
Jucheng Zeng ◽  
Xiaohui Yuan ◽  
Ping Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Medical Information ◽  
Prediction Models ◽  
Short Term Memory ◽  
Heterogeneous Data ◽  
Structured Data ◽  
Unstructured Data ◽  
Data Types ◽  
Clinical Notes

Background: The broad adoption of Electronic Health Records (EHRs) provides great opportunities to conduct health care research and solve various clinical problems in medicine. With recent advances and success, methods based on machine learning and deep learning have become increasingly popular in medical informatics. However, while many research studies utilize temporal structured data on predictive modeling, they typically neglect potentially valuable information in unstructured clinical notes. Integrating heterogeneous data types across EHRs through deep learning techniques may help improve the performance of prediction models. Methods: In this research, we proposed 2 general-purpose multi-modal neural network architectures to enhance patient representation learning by combining sequential unstructured notes with structured data. The proposed fusion models leverage document embeddings for the representation of long clinical note documents and either convolutional neural network or long short-term memory networks to model the sequential clinical notes and temporal signals, and one-hot encoding for static information representation. The concatenated representation is the final patient representation which is used to make predictions. Results: We evaluate the performance of proposed models on 3 risk prediction tasks (i.e., in-hospital mortality, 30-day hospital readmission, and long length of stay prediction) using derived data from the publicly available Medical Information Mart for Intensive Care III dataset. Our results show that by combining unstructured clinical notes with structured data, the proposed models outperform other models that utilize either unstructured notes or structured data only. Conclusions: The proposed fusion models learn better patient representation by combining structured and unstructured data. Integrating heterogeneous data types across EHRs helps improve the performance of prediction models and reduce errors.

scholarly journals Combining structured and unstructured data for predictive models: a deep learning approach

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Dongdong Zhang ◽  
Changchang Yin ◽  
Jucheng Zeng ◽  
Xiaohui Yuan ◽  
Ping Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Medical Information ◽  
Prediction Models ◽  
Short Term Memory ◽  
Heterogeneous Data ◽  
Structured Data ◽  
Unstructured Data ◽  
Data Types ◽  
Clinical Notes

Abstract Background The broad adoption of electronic health records (EHRs) provides great opportunities to conduct health care research and solve various clinical problems in medicine. With recent advances and success, methods based on machine learning and deep learning have become increasingly popular in medical informatics. However, while many research studies utilize temporal structured data on predictive modeling, they typically neglect potentially valuable information in unstructured clinical notes. Integrating heterogeneous data types across EHRs through deep learning techniques may help improve the performance of prediction models. Methods In this research, we proposed 2 general-purpose multi-modal neural network architectures to enhance patient representation learning by combining sequential unstructured notes with structured data. The proposed fusion models leverage document embeddings for the representation of long clinical note documents and either convolutional neural network or long short-term memory networks to model the sequential clinical notes and temporal signals, and one-hot encoding for static information representation. The concatenated representation is the final patient representation which is used to make predictions. Results We evaluate the performance of proposed models on 3 risk prediction tasks (i.e. in-hospital mortality, 30-day hospital readmission, and long length of stay prediction) using derived data from the publicly available Medical Information Mart for Intensive Care III dataset. Our results show that by combining unstructured clinical notes with structured data, the proposed models outperform other models that utilize either unstructured notes or structured data only. Conclusions The proposed fusion models learn better patient representation by combining structured and unstructured data. Integrating heterogeneous data types across EHRs helps improve the performance of prediction models and reduce errors.

scholarly journals A Densely Connected GRU Neural Network Based on Coattention Mechanism for Chinese Rice-Related Question Similarity Matching

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1307
Author(s):  
Haoriqin Wang ◽  
Huaji Zhu ◽  
Huarui Wu ◽  
Xiaomin Wang ◽  
Xiao Han ◽  
...  
Keyword(s):  
Related Question ◽  
Similarity Matching ◽  
Frequency Method ◽  
Text Data ◽  
Information Platform ◽  
Document Frequency ◽  
Proposed Model ◽  
Related Text ◽  
Text Features ◽  
Matching Models

In the question-and-answer (Q&A) communities of the “China Agricultural Technology Extension Information Platform”, thousands of rice-related Chinese questions are newly added every day. The rapid detection of the same semantic question is the key to the success of a rice-related intelligent Q&A system. To allow the fast and automatic detection of the same semantic rice-related questions, we propose a new method based on the Coattention-DenseGRU (Gated Recurrent Unit). According to the rice-related question characteristics, we applied word2vec with the TF-IDF (Term Frequency–Inverse Document Frequency) method to process and analyze the text data and compare it with the Word2vec, GloVe, and TF-IDF methods. Combined with the agricultural word segmentation dictionary, we applied Word2vec with the TF-IDF method, effectively solving the problem of high dimension and sparse data in the rice-related text. Each network layer employed the connection information of features and all previous recursive layers’ hidden features. To alleviate the problem of feature vector size increasing due to dense splicing, an autoencoder was used after dense concatenation. The experimental results show that rice-related question similarity matching based on Coattention-DenseGRU can improve the utilization of text features, reduce the loss of features, and achieve fast and accurate similarity matching of the rice-related question dataset. The precision and F1 values of the proposed model were 96.3% and 96.9%, respectively. Compared with seven other kinds of question similarity matching models, we present a new state-of-the-art method with our rice-related question dataset.

Extracting insights from big social data for smarter tourism destination management

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gianluca Solazzo ◽  
Ylenia Maruccia ◽  
Gianluca Lorenzo ◽  
Valentina Ndou ◽  
Pasquale Del Vecchio ◽  
...  
Keyword(s):  
Heterogeneous Data ◽  
Destination Image ◽  
Data Types ◽  
Social Data ◽  
Content Type ◽  
Management And Development ◽  
Destination Attractiveness ◽  
Tourist Demand ◽  
Tourist Behaviour

Purpose This paper aims to highlight how big social data (BSD) and analytics exploitation may help destination management organisations (DMOs) to understand tourist behaviours and destination experiences and images. Gathering data from two different sources, Flickr and Twitter, textual and visual contents are used to perform different analytics tasks to generate insights on tourist behaviour and the affective aspects of the destination image. Design/methodology/approach This work adopts a method based on a multimodal approach on BSD and analytics, considering multiple BSD sources, different analytics techniques on heterogeneous data types, to obtain complementary results on the Salento region (Italy) case study. Findings Results show that the generated insights allow DMOs to acquire new knowledge about discovery of unknown clusters of points of interest, identify trends and seasonal patterns of tourist demand, monitor topic and sentiment and identify attractive places. DMOs can exploit insights to address its needs in terms of decision support for the management and development of the destination, the enhancement of destination attractiveness, the shaping of new marketing and communication strategies and the planning of tourist demand within the destination. Originality/value The originality of this work is in the use of BSD and analytics techniques for giving DMOs specific insights on a destination in a deep and wide fashion. Collected data are used with a multimodal analytic approach to build tourist characteristics, images, attitudes and preferred destination attributes, which represent for DMOs a unique mean for problem-solving, decision-making, innovation and prediction.

scholarly journals Algorithmic and data modeling: Will algorithmic modeling improve predictions of traits evaluated on ordinal scales?

2020 ◽  
Author(s):  
Zhanyou Xu ◽  
Andreomar Kurek ◽  
Steven B. Cannon ◽  
Williams D. Beavis
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Ridge Regression ◽  
Genomic Prediction ◽  
Ordinal Data ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Types

AbstractSelection of markers linked to alleles at quantitative trait loci (QTL) for tolerance to Iron Deficiency Chlorosis (IDC) has not been successful. Genomic selection has been advocated for continuous numeric traits such as yield and plant height. For ordinal data types such as IDC, genomic prediction models have not been systematically compared. The objectives of research reported in this manuscript were to evaluate the most commonly used genomic prediction method, ridge regression and it’s equivalent logistic ridge regression method, with algorithmic modeling methods including random forest, gradient boosting, support vector machine, K-nearest neighbors, Naïve Bayes, and artificial neural network using the usual comparator metric of prediction accuracy. In addition we compared the methods using metrics of greater importance for decisions about selecting and culling lines for use in variety development and genetic improvement projects. These metrics include specificity, sensitivity, precision, decision accuracy, and area under the receiver operating characteristic curve. We found that Support Vector Machine provided the best specificity for culling IDC susceptible lines, while Random Forest GP models provided the best combined set of decision metrics for retaining IDC tolerant and culling IDC susceptible lines.

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