Mohammad Fakhri Bani Doumi

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Research

A 2-tuple fuzzy linguistic model for recommending health care services grounded on aspect-based sentiment analysis

Authors :Jesus Serrano-Guerrero, Mohammad Bani-Doumi, Francisco P. Romero, Jose A. Olivas

Abstract :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

Keywords : 2-tuple fuzzy model Multi-granular fuzzy linguistic modeling Recommender system Sentiment analysis Multicriteria decision making

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Selecting the Best Health Care Systems: An Approach Based on Opinion Mining and Simplified Neutrosophic Sets

Authors : Jesus Serrano-Guerrero, Mohammad Bani-Doumi, Francisco P. Romero, Jose A. Olivas

Abstract :Measuring what hospital offers the best services is very difficult, for that reason, the opinions from previous patients have become an essential tool for the new possible clients to decide which services they must select. Many online platforms deal with opinions to analyze their services/products, primarily, by means of aspect-based sentiment analysis techniques. These techniques are mainly based on the detection of features from services/products to model the feelings toward them. Most models primarily cope with positiveness, negativeness and neutrality; nevertheless, these do not reflect other situations in which there are positive and negative aspects, but the overall sentiment is not neutral, but indeterminate. To face this issue, simplified neutrosophic sets can be a useful tool. Therefore, this study presents a novel application of the simplified neutrosophic sets to hospital ranking. The application, first, detects the hospital features, second, models the feelings toward them using simplified neutrosophic sets, and finally, ranks the hospitals according to the patients’ preferences. It has been tested using opinions from a real website and compared against other fuzzy logic-based approaches. The achieved results outperform the ones obtained by other proposals, revealing that simplified neutrosophic sets can be an interesting solution to model feelings.

Keywords : Aspect-based sentiment analysis health care ranking simplified neutrosophic sets decision support systems SenticNet

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Understanding what patients think about hospitals: A deep learning approach for detecting emotions in patient opinions

Authors : Jesus Serrano-Guerrero, Mohammad Bani-Doumi, Francisco P. Romero, Jose A. Olivas

Abstract :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.

Keywords : Multiple emotion classification Patient feedback Multichannel Bidirectional gated recurrent unit Convolutional neural network

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A fuzzy aspect-based approach for recommending hospitals

Authors : Jesus Serrano-Guerrero, Mohammad Bani-Doumi, Francisco P. Romero, Jose A. Olivas

Abstract :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.

Keywords : hospital ranking, intuitionistic fuzzy sets, latentDirichlet allocation, opinion mining, recommender system

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An Algorithm for Ranking Hospitals based on Intuitionistic Fuzzy Sets and Sentiment Analysis

Authors : Jesus Serrano-Guerrero, Mohammad Bani-Doumi, Francisco P. Romero, Jose A. Olivas

Abstract :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

Keywords : Hospital Recommendation , Sentiment Analysis , Intuitionistic Fuzzy Sets , Multicriteria Decision Making , Latent Dirichlet Allocation

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