Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation

Early recognition of risky trajectories during an Intensive Care Unit (ICU) stay is one of the key steps towards improving patient survival. Learning trajectories from physiological signals continuously measured during an ICU stay requires learning time-series features that are robust and discriminative across diverse patient populations. Patients within different ICU populations (referred here as domains) vary by age, conditions and interventions. Thus, mortality prediction models using patient data from a particular ICU population may perform suboptimally in other populations because the features used to train such models have different distributions across the groups. In this paper, we explore domain adaptation strategies in order to learn mortality prediction models that extract and transfer complex temporal features from multivariate time-series ICU data. Features are extracted in a way that the state of the patient in a certain time depends on the previous state. This enables dynamic predictions and creates a mortality risk space that describes the risk of a patient at a particular time. Experiments based on cross-ICU populations reveals that our model outperforms all considered baselines. Gains in terms of AUC range from 4% to 8% for early predictions when compared with a recent state-of-the-art representative for ICU mortality prediction. In particular, models for the Cardiac ICU population achieve AUC numbers as high as 0.88, showing excellent clinical utility for early mortality prediction. Finally, we present an explanation of factors contributing to the possible ICU outcomes, so that our models can be used to complement clinical reasoning

Sentiment analysis in tweets

Orientador: Jacques WainerDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Análise do sentimento é um campo de estudo de recente popularização devido ao crescimento da Internet e ao conteúdo gerado por seus usuários. Mais recentemente, as redes sociais surgiram, nessas redes as pessoas publicam suas opiniões em linguagem coloquial e compacta. Isto é o que acontece, por exemplo, no Twitter, uma ferramenta de comunicação que pode ser facilmente utilizada como fonte de informação para várias ferramentas automatizadas de inferência de sentimento. Esforços de pesquisa foram direcionados para lidar com o problema da análise do sentimento nas redes sociais do ponto de vista de um problema de classificação, onde não há consenso sobre qual é o melhor classificador, qual a melhor forma de pré- processamento entre outros. O objetivo desta dissertação é investigar a influência de algumas técnicas de pré-processamento, da técnica TF-IDF, do volume do conjunto de treinamento e de técnicas ensembles na acurácia de alguns classificadores supervisionadosAbstract: Sentiment analysis is a field of study that shows recent popularization due to the growth of Internet and the content that is generated by its users. More recently, social networks have emerged, where people post their opinions in colloquial and compact language. This is what happens in Twitter, a communication tool that can easily be used as a source of information for various automatic tools of sentiment inference. Research efforts have been directed to deal with the problem of sentiment analysis in social networks from the point of view of a classification problem, where there is no consensus about what the best classifier is, and what is the best configuration provided by the feature engineering process. The objective of this dissertation is to investigate the influence of some pre-processing techniques, the TF-IDF technique, the volume of the training set and ensembles techniques in the accuracy of some supervised techniquesMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation