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Исследование влияния пола и психологических характеристик автора на количественные параметры его текста с использованием программы Linguistic Inquiry and Word Count

Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.

Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging

Predicting the Big 5 personality traits from digital footprints on social media: A meta-analysis

Clustering of short texts, such as snippets, presents great challenges in existing aggregated search techniques due to the problem of data sparseness and the complex semantics of natural language. As short texts do not provide sufficient term occurring information, traditional text representation methods, such as ``bag of words" model, have several limitations when directly applied to short texts tasks. In this paper, we propose a novel framework to improve the performance of short texts clustering by exploiting the internal semantics from original text and external concepts from world knowledge. The proposed method employs a hierarchical three-level structure to tackle the data sparsity problem of original short texts and reconstruct the corresponding feature space with the integration of multiple semantic knowledge bases -- Wikipedia and WordNet. Empirical evaluation with Reuters and real web dataset demonstrates that our approach is able to achieve significant improvement as compared to the state-of-the-art methods.

Exploiting internal and external semantics for the clustering of short texts using world knowledge

A variety of approaches have been recently proposed to automatically infer users' personality from their user generated content in social media. Approaches differ in terms of the machine learning algorithms and the feature sets used, type of utilized footprint, and the social media environment used to collect the data. In this paper, we perform a comparative analysis of state-of-the-art computational personality recognition methods on a varied set of social media ground truth data from Facebook, Twitter and YouTube. We answer three questions: (1) Should personality prediction be treated as a multi-label prediction task (i.e., all personality traits of a given user are predicted at once), or should each trait be identified separately? (2) Which predictive features work well across different on-line environments? and (3) What is the decay in accuracy when porting models trained in one social media environment to another?

/pdf/computational-personality-recognition-in-social-media-20odm2xwhw.pdf

Computational personality recognition in social media

An aggregated search interface is designed to integrate search results from different sources (web, image, video, blog, etc) into a single result page. This paper presents two user studies investigating factors affecting users click-through behavior on aggregated search interfaces. We tested two aggregated search interfaces: one where results from the different sources are blended into a single list (called blended), and another, where results from each source are presented in a separate panel (called non-blended). A total of 1,296 search sessions performed by 48 participants were analysed in our study. Our results suggest that 1) the position of search results is significant only in the blended and not in the non-blended design; 2) participants' click-through behavior on videos is different from other sources; and finally 3) capturing a task's orientation towards particular sources is an important factor for further investigation and research.

/pdf/factors-affecting-click-through-behavior-in-aggregated-8pl18fb726.pdf

Factors affecting click-through behavior in aggregated search interfaces

The increasing availability of digital health records should ideally improve accountability in healthcare. In this context, the study of predictive modeling of healthcare costs forms a foundation for accountable care, at both population and individual patient-level care. In this research we use machine learning algorithms for accurate predictions of healthcare costs on publicly available claims and survey data. Specifically, we investigate the use of the regression trees, M5 model trees and random forest, to predict healthcare costs of individual patients given their prior medical (and cost) history. Overall, three observations showcase the utility of our research: (a) prior healthcare cost alone can be a good indicator for future healthcare cost, (b) M5 model tree technique led to very accurate future healthcare cost prediction, and (c) although state-of-the-art machine learning algorithms are also limited by skewed cost distributions in healthcare, for a large fraction (75%) of population, we were able to predict with higher accuracy using these algorithms. In particular, using M5 model trees we were able to accurately predict costs within less than $125 for 75% of the population when compared to prior techniques. Since models for predicting healthcare costs are often used to ascertain overall population health, our work is useful to evaluate future costs for large segments of disease populations with reasonably low error as demonstrated in our results on real-world publicly available datasets.

Population Cost Prediction on Public Healthcare Datasets

Research in psychology has suggested that behavior of individuals can be explained to a great extent by their underlying personality traits. In this paper, we focus on predicting how the personality of YouTube video bloggers is perceived by their viewers. Our approach to personality recognition is multimodal in the sense that we use audio-video features, as well as textual (emotional and linguistic) features extracted from the transcripts of vlogs. Based on these features, we predict the extent to which the video blogger is perceived to exhibit each of the traits of the Big Five personality model. In addition, we explore 5 multivariate regression techniques and contrast them with a single target approach for predicting personality impression scores. All 6 algorithms are able to outperform the average baseline model for all 5 personality traits on a dataset of 404 YouTube videos. This is interesting because previously published methods for the same dataset show an improvement over the baseline for the majority of personality traits, but not for all simultaneously.

/pdf/a-multivariate-regression-approach-to-personality-impression-rghfiy3uqi.pdf

A Multivariate Regression Approach to Personality Impression Recognition of Vloggers

This paper presents a user study that evaluated the effectiveness of an aggregated search interface in the context of non-navigational search tasks. An experimental system was developed to present search results aggregated from multiple information sources, and compared to a conventional tabbed interface. Sixteen participants were recruited to evaluate the performance of the two interfaces. Our results suggest that the aggregated search interface is a promising way of supporting non-navigational search tasks. The quantity and diversity of the retrieved items which participants accessed to complete a task, increased in the aggregated interface. Participants also found the aggregated presentation easier to access to retrieved items and to find relevant information, compared to the conventional interface.

/pdf/a-task-based-evaluation-of-an-aggregated-search-interface-40deh6hvgy.pdf

Shanu Sushmita

Papers

Computational personality recognition in social media

Factors affecting click-through behavior in aggregated search interfaces

Population Cost Prediction on Public Healthcare Datasets

A Multivariate Regression Approach to Personality Impression Recognition of Vloggers

A Task-Based Evaluation of an Aggregated Search Interface