Abstract Quality management techniques such as the quality function deployment model can help hospitals assess and improve the quality of their services by integrating the voice of customers. The different quality parameters of this model are usually determined and assessed by experts; nonetheless, obtaining such experts is not always easy or inexpensive. Moreover, in this method, patient opinions are not usually considered directly, although they are the real users of the services and those who can best assess those services. Nevertheless, these opinions are easily accessible today, owing to the development of medical social networks where patients directly convey their opinions about the different services and features of a hospital. Therefore, it is feasible to replace expert knowledge with the information provided by these opinions. Based on this idea, this study proposes a novel fuzzy recommendation model based on the quality function deployment method to rank hospitals depending on patient opinions and preferences regarding hospital services. This model integrates a topic modeling strategy for determining hospital requirements, customer needs, and the relationship between them as well as a sentiment analysis algorithm for assessing customer satisfaction regarding hospital services. To demonstrate the usefulness of the proposed method, several experiments were conducted using patient reviews from real hospitals, and the method was compared against other recommendation models. The results prove that this approach represents a step toward more personalized and effective health care system selection considering patient preferences and opinions.

Evaluating the quality of health care systems usually entails the examination of objective variables (waiting time, patients per doctor, etc.). Nonetheless, other subjective variables can be used thanks to new Internet tools. Many online medical services let users convey their opinions on the offered services. That information is an interesting tool to measure the quality of these services. It describes sentiments about different features using a wide range of adjectives, adverbs and nouns, many times, completely different for each feature. Therefore, it is interesting to assess every feature individually using different scales. This study presents an application of a multi-granular fuzzy linguistic model to represent the opinions about the different features of health care systems with the aim of recommending hospitals according to the user preferences. To test this approach, the opinions from real hospitals have been assessed using different user preferences. The obtained results outperform other state-of-the-art approaches.

INTRODUCTION: Most hospital assessment systems are based on the study of objective statistical variables as well as patient opinions on their experiences with respect to the services offered by each hospital. Nevertheless, studies have indicated that most of these assessment systems fail to detect patient emotions when they are assessing their stays in a hospital. This information is vital to understanding most of the patient reviews, which are very complex and convey several emotions per review. Therefore, this study aimed to address the problem of detecting multiple emotions from patient reviews. METHODS: First, a large set of patient opinions was collected from a website that allowed patients to publish their experiences when visiting hospitals. Second, each opinion was labeled with the corresponding conveyed emotions. Third, a deep learning architecture based on a bidirectional gated recurrent unit with a multichannel convolutional neural network layer was proposed to detect multiple emotions from these reviews. Finally, the hyperparameters of this architecture were fine-tuned and different pretrained word embedding models were configured to test its performance. RESULTS: The results confirmed that our proposed method outperformed other deep learning and machine learning-based algorithms and achieved an average accuracy of 95.82%. Furthermore, the experiments show that clinical-domain word embedding slightly outperforms other general-domain word embeddings, although general-domain embeddings are larger in terms of dimensions. CONCLUSIONS: The combination of the gated recurrent unit and the multichannel convolutional neural network is able to exploit both semantic and syntactic characteristics of patient opinions. The findings of this study identify research gaps related to areas such as opinion-based hospital recommendations, thereby providing future research directions.

The process to assess a hospital performance usually needs the interaction of a lot of experts and patients and is very costly and time-consuming. Nevertheless, the availability of patient opinions on the Internet offers a great opportunity to develop systems that evaluate hospitals based on user feedback. The content of these opinions is very challenging, including information about the hospital services but also stories about their own patients, their families, and personal feelings or beliefs before or after leaving a hospital. Therefore, the task of recommending hospitals according to the quality of their services becomes really complicated. This study describes an application for ranking hospitals based on the user preferences about the different offered services as well as the opinions about them. First, it semiautomatically classifies all predefined hospital aspects, calculates the sentiment orientation, and represents their associated polarity by intuitionistic fuzzy sets. Second, by means of the user preferences towards the different aspects, an aggregation operator, and a multicriteria decision-making algorithm, all hospitals are ranked. To assess this methodology, a large set of reviews about hospitals have been collected. Further, considering all patient ratings about the different hospitals, an algorithm for ranking them is proposed, which develops baselines for comparison. In addition, an interval-valued Pythagorean fuzzy approach has been also implemented to compare the obtained results. These results confirm the soundness of the proposal.

Understanding opinions about products offered by big online providers, for instance, TripAdvisor, is relatively easy because the features assessed by the user about hotels are well-known (food, room, desk, sleep quality, etc.). Nonetheless, in the health domain, many times the user provides free-text reviews which are not clearly focused on a few specific features. The present study proposes a methodology for recommending hospitals according to textual opinions which describe the quality of the offered services. First, it detects hospital aspects, which represent the hospital services, by a Latent Dirichlet Allocation-based approach following the criteria of a quality model called SERVQUAL. The polarity of those aspects is computed and modelled by intuitionistic fuzzy sets. Depending on the user preferences or his/her attitude, the aspects are aggregated to finally rank the alternative hospitals following a Multicriteria Decision Making algorithm (PROMETHEE II). The methodology has been tested using a large collection of free-text reviews on hospitals, which contain information about their offered services, obtaining interesting results.

We have witnessed a flourish of review websites where users can buy many products/services and share their opinions about them. Most of those opinions may be broken down into different sub-opinions on the different aspects describing said products/services. This fact makes more complicated the task of computing the overall polarity about the product/service studied.We are presenting a fuzzy aggregation mechanism to compute the overall sentiment conveyed in a opinion/review taking into account the individual ratings for the different aspects commented by the opinion holder. This proposal has been tested using real data from Yelp dataset obtaining promising results.

Within the framework of the Intelligent Data Suite (IDS) that is being developed by the company Prometeus Global Solutions, there is a Sentiment Analysis and Opinion Mining module focused on detecting `dangerous' (to the tool user company) messages on Social Media. This can be useful for sending `early warnings' to alert tool user company analysts to take preventive measures against potentially harmful messages. In this paper, a brief description of IDS features, regarding tweets filtering and classification is firstly presented. Affective Norms for English Words (ANEW) provides a set of normative emotional ratings for a large number of words in English and three emotions (valence, arousal and dominance) measures for each term. It is used as a basis for describing a fuzzy model containing five categories for representing the opinion of a microblogging text (very negative, negative, neutral, positive and very positive). The proposal is implemented and tested on the IDS framework.

Most of the well-known recommender systems are based on a proprietary database and a finite and well-known set of elements to recommend. This article presents a methodology that allows us to make recommendations on broad domains where we do not have our own database of recommendable items. This methodology proposes the definition of the characteristic aspects of the domain treated and the extraction of information based on online content tools. These tools will provide the system with subjective and objective information that will make it possible to characterize the recommended elements and thus, adapt the recommendations to the user's preferences.

Decision support systems, embedded in modern telemedicine applications, are a tool to improve the skills of general practitioners and patients in decision-making in medicine. Nowadays, one of the more challenging problems in this context is how to diagnose those diseases, whose early clinical signs are often subtle, and many of their common signs and symptoms are similar to other. These “fuzzy diseases,” even they can have distinctive features, are not diagnosable through a concrete clinical test or symptom, and, thus, they are difficult to recognize, especially in their initial phases when they might be mistaken for other similar ones. Then, the diagnosis of a fuzzy disease set is based on the exclusion of symptoms and tests results, due to the similarity between them. In the present article, it is proposed the development of a Clinical Decision Support System framework to diagnose a set of fuzzy diseases, concretely applied to Fibromyalgia and associated syndromes. For this purpose, in this paper a reasoning method that uses theories about conceptual categorization from the psychology, pattern recognition, and Zadeh's prototypes has been designed. Through the use of this model, satisfactory results in the evaluation of patients were obtained.

There are several types of business documents, ranging from brief accounting documents to complex legal agreements. Companies extensively use such documents to communicate, transact business and analyse their productivity. This results in the generation of a large number of documents daily, and small- and medium-sized enterprises are easily overwhelmed by this situation. Given this background, companies require software solutions which provide all of the features required by users for optimal document management, as well as optimising management processes and automating the extraction of relevant information from the documents. Open-source software provides these organizations with low-cost, high-quality software which incorporates an array of advanced features that extend beyond only storage solutions. In this study, we test several computational-intelligence open-source software tools in order to enhance the information-retrieval capabilities in small business document-management systems. We implement a prototype to test these Natural Language Processing (NLP) tools and Machine-Learning techniques in a business environment, with the aim of choosing the best alternative for each process.

The Learning Object Repository is an important element in the management, publishing, location and retrieval of Learning Objects. However in this system, the task of finding Learning Objects that conform to user's needs and requirements still presents problems related to easiness, simplicity and usefulness in selecting and recovering them. Thus, this paper proposes a metric to determine the similarity among Learning Objects based on information represented in their metadata. The proposed method is based on a fuzzy approach to determine the semantic similarity of XML structures used to represent the Learning Object metadata. The metric has been implemented into a Learning Object Management System, as part of the Searching component. This component offers several searching mechanisms based on Semantic Query, Learning Objects Patterns and Query by Examples. The first results confirm the effectiveness of the approach in identifying similar Learning Objects, an important task in managing these educational resource types.

Category-based information filtering is ground on the representation of user preferences according to a set of categories of similar items. The use of type 1 fuzzy sets provides a good method to represent categories when only one static interpretation of them is considered. This representation is not enough when documents do not have the same meaning for two different users because there are some degrees of subjectivity. On the other hand, type 2 fuzzy sets have been successfully applied to manage uncertainty more effectively than type-1 fuzzy sets in several environments. This paper presents a method to manage efficiently uncertainties in the filtering process in environments where there is a constant flow of new information (news, e-mail, etc.) and multiple users are involved. The proposed solution is based on the extension of the categories-based filtering method using interval type 2 fuzzy sets for representing each category and the user preferences. Experimental results, that illustrate the feasibility of this approach, are provided.

The goal of this paper is to present the architecture of a metasearch engine called Castalia, still under development, which includes several underlying Q&A systems. Usually metasearch engines manage typical search engines like Google or Yahoo, but in this case the encapsulation of Q&A systems proposes new challenges that can be modeled by fuzzy logic apart from the other existing challenges such as the fuzzy modeling of temporal or causal questions.

Existing search engines and question-answering (QA) systems have made possible processing large volumes of textual information. Current work on QA has mainly focused on answering two basic types of questions: factoid and definition questions. However, the capability to synthesize an answer to a query by drawing on bodies of information which reside in various parts of the knowledge base is not among the capabilities of those systems. In this paper, a system oriented to infer query answers from a collection of propositions expressed in natural language is introduced. By means of a specific example, it is outlined how the system proceeds to face those situations. This approach is based on the use of formal constraining relations modeling copulative and comparative sentences. Combining those propositions with others contained in different knowledge bases and applying deduction rules, the desired answer could be obtained.

In this poster, a tool named BUCEFALO is presented. This tool is specially designed to improve the information retrieval tasks in web-based Personal Health Records (PHR). This tool implements semantic and multilingual query expansion techniques and information filtering algorithms in order to help users find the most valuable information about a specific clinical case. The filtering model is based on fuzzy prototypes based filtering, data quality measures, user profiles and healthcare ontologies. The first experimental results illustrate the feasibility of this tool.