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Role and Challenges of Healthcare Cognitive Computing: From Extraction to Data Analysis Techniques

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 35203

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Dr. Antonella Carbonaro

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Guest Editor

Department of Computer Science and Engineering, University of Bologna, 40136 Bologna, Italy
Interests: customization and content-based information processing for data and knowledge representation; semantic web technologies; personalized environments; heterogeneous data integration from IoT devices
Special Issues, Collections and Topics in MDPI journals

Dr. Gianluca Moro

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Guest Editor

Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
Interests: data mining; text mining; agents & peer-to-peer systems; sensor networks; multi-dimensional indexing

Special Issue Information

Dear Colleagues,

A main transformation that characterizes the era in which we live concerns the high availability of data (especially thanks to the pervasiveness of social media), which is most of the time unstructured, not labeled and expressed in natural language. One of the most investigated areas in this sense is medicine and health, wherein researchers are often called on to put into play cutting-edge analytical techniques, often trying to manage the semantic aspects of the data considered. Cognitive computing systems process enormous amounts of data in order to answer specific queries and make customized intelligent analyses, potentially improving the quality of patient care.

So far, quantitative techniques (such as statistical models, machine learning and deep learning) and qualitative/symbolic techniques (related to the world of the Semantic Web, ontologies and knowledge graphs) have given good results, but the growing complexity of such applications in healthcare has led many experts to assert that the future demands a fusion of these solutions.

This Special Issue, entitled "Role and Challenges of Healthcare Cognitive Computing: From Information Extraction to Analytics", aims to explore the scientific-technological frontiers that characterize the solving of the above-mentioned problems. It seeks original, previously unpublished papers empirically addressing key issues and challenges related to the methods, implementation, results and evaluation of novel approaches based on the use of Cognitive Computing in healthcare.

Prof. Antonella Carbonaro
Dr. Gianluca Moro
Guest Editors

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Keywords

Cognitive Computing
Natural Language Processing
Semantic Web
Neural-Symbolic Learning
Wearable Sensors for Medical Applications

Published Papers (10 papers)

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Research

19 pages, 437 KiB

Open AccessArticle

Supporting Smart Home Scenarios Using OWL and SWRL Rules

by Roberto Reda, Antonella Carbonaro, Victor de Boer, Ronald Siebes, Roderick van der Weerdt, Barry Nouwt and Laura Daniele

Sensors 2022, 22(11), 4131; https://0-doi-org.brum.beds.ac.uk/10.3390/s22114131 - 29 May 2022

Cited by 6 | Viewed by 2226

Abstract

Despite the pervasiveness of IoT domotic devices in the home automation landscape, their potential is still quite under-exploited due to the high heterogeneity and the scarce expressivity of the most commonly adopted scenario programming paradigms. The aim of this study is to show that Semantic Web technologies constitute a viable solution to tackle not only the interoperability issues, but also the overall programming complexity of modern IoT home automation scenarios. For this purpose, we developed a knowledge-based home automation system in which scenarios are the result of logical inferences over the IoT sensors data combined with formalised knowledge. In particular, we describe how the SWRL language can be employed to overcome the limitations of the well-known trigger-action paradigm. Through various experiments in three distinct scenarios, we demonstrated the feasibility of the proposed approach and its applicability in a standardised and validated context such as SAREF Full article

(This article belongs to the Special Issue Role and Challenges of Healthcare Cognitive Computing: From Extraction to Data Analysis Techniques)

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22 pages, 2550 KiB

Open AccessArticle

Human Being Detection from UWB NLOS Signals: Accuracy and Generality of Advanced Machine Learning Models

by Gianluca Moro, Federico Di Luca, Davide Dardari and Giacomo Frisoni

Sensors 2022, 22(4), 1656; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041656 - 20 Feb 2022

Cited by 7 | Viewed by 3731

Abstract

This paper studies the problem of detecting human beings in non-line-of-sight (NLOS) conditions using an ultra-wideband radar. We perform an extensive measurement campaign in realistic environments, considering different body orientations, the obstacles’ materials, and radar–obstacle distances. We examine two main scenarios according to the radar position: (i) placed on top of a mobile cart; (ii) handheld at different heights. We empirically analyze and compare several input representations and machine learning (ML) methods—supervised and unsupervised, symbolic and non-symbolic—according to both their accuracy in detecting NLOS human beings and their adaptability to unseen cases. Our study proves the effectiveness and flexibility of modern ML techniques, avoiding environment-specific configurations and benefiting from knowledge transference. Unlike traditional TLC approaches, ML allows for generalization, overcoming limits due to unknown or only partially known observation models and insufficient labeled data, which usually occur in emergencies or in the presence of time/cost constraints. Full article

(This article belongs to the Special Issue Role and Challenges of Healthcare Cognitive Computing: From Extraction to Data Analysis Techniques)

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34 pages, 11920 KiB

Open AccessArticle

Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature

by Giacomo Frisoni, Gianluca Moro, Giulio Carlassare and Antonella Carbonaro

Sensors 2022, 22(1), 3; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010003 - 21 Dec 2021

Cited by 12 | Viewed by 4429

Abstract

The automatic extraction of biomedical events from the scientific literature has drawn keen interest in the last several years, recognizing complex and semantically rich graphical interactions otherwise buried in texts. However, very few works revolve around learning embeddings or similarity metrics for event graphs. This gap leaves biological relations unlinked and prevents the application of machine learning techniques to promote discoveries. Taking advantage of recent deep graph kernel solutions and pre-trained language models, we propose Deep Divergence Event Graph Kernels (DDEGK), an unsupervised inductive method to map events into low-dimensional vectors, preserving their structural and semantic similarities. Unlike most other systems, DDEGK operates at a graph level and does not require task-specific labels, feature engineering, or known correspondences between nodes. To this end, our solution compares events against a small set of anchor ones, trains cross-graph attention networks for drawing pairwise alignments (bolstering interpretability), and employs transformer-based models to encode continuous attributes. Extensive experiments have been done on nine biomedical datasets. We show that our learned event representations can be effectively employed in tasks such as graph classification, clustering, and visualization, also facilitating downstream semantic textual similarity. Empirical results demonstrate that DDEGK significantly outperforms other state-of-the-art methods. Full article

(This article belongs to the Special Issue Role and Challenges of Healthcare Cognitive Computing: From Extraction to Data Analysis Techniques)

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20 pages, 474 KiB

Open AccessArticle

Efficient Self-Supervised Metric Information Retrieval: A Bibliography Based Method Applied to COVID Literature

by Gianluca Moro and Lorenzo Valgimigli

Sensors 2021, 21(19), 6430; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196430 - 26 Sep 2021

Cited by 22 | Viewed by 2719

Abstract

The literature on coronaviruses counts more than 300,000 publications. Finding relevant papers concerning arbitrary queries is essential to discovery helpful knowledge. Current best information retrieval (IR) use deep learning approaches and need supervised training sets with labeled data, namely to know a priori the queries and their corresponding relevant papers. Creating such labeled datasets is time-expensive and requires prominent experts’ efforts, resources insufficiently available under a pandemic time pressure. We present a new self-supervised solution, called SUBLIMER, that does not require labels to learn to search on corpora of scientific papers for most relevant against arbitrary queries. SUBLIMER is a novel efficient IR engine trained on the unsupervised COVID-19 Open Research Dataset (CORD19), using deep metric learning. The core point of our self-supervised approach is that it uses no labels, but exploits the bibliography citations from papers to create a latent space where their spatial proximity is a metric of semantic similarity; for this reason, it can also be applied to other domains of papers corpora. SUBLIMER, despite is self-supervised, outperforms the Precision@5 (P@5) and Bpref of the state-of-the-art competitors on CORD19, which, differently from our approach, require both labeled datasets and a number of trainable parameters that is an order of magnitude higher than our. Full article

(This article belongs to the Special Issue Role and Challenges of Healthcare Cognitive Computing: From Extraction to Data Analysis Techniques)

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20 pages, 318 KiB

Open AccessArticle

A Machine Learning Approach as an Aid for Early COVID-19 Detection

by Roberto Martinez-Velazquez, Diana P. Tobón V., Alejandro Sanchez, Abdulmotaleb El Saddik and Emil Petriu

Sensors 2021, 21(12), 4202; https://0-doi-org.brum.beds.ac.uk/10.3390/s21124202 - 18 Jun 2021

Cited by 16 | Viewed by 3149

Abstract

The novel coronavirus SARS-CoV-2 that causes the disease COVID-19 has forced us to go into our homes and limit our physical interactions with others. Economies around the world have come to a halt, with non-essential businesses being forced to close in order to prevent further propagation of the virus. Developing countries are having more difficulties due to their lack of access to diagnostic resources. In this study, we present an approach for detecting COVID-19 infections exclusively on the basis of self-reported symptoms. Such an approach is of great interest because it is relatively inexpensive and easy to deploy at either an individual or population scale. Our best model delivers a sensitivity score of 0.752, a specificity score of 0.609, and an area under the curve for the receiver operating characteristic of 0.728. These are promising results that justify continuing research efforts towards a machine learning test for detecting COVID-19. Full article

(This article belongs to the Special Issue Role and Challenges of Healthcare Cognitive Computing: From Extraction to Data Analysis Techniques)

27 pages, 876 KiB

Open AccessArticle

Automatic Correction of Real-Word Errors in Spanish Clinical Texts

by Daniel Bravo-Candel, Jésica López-Hernández, José Antonio García-Díaz, Fernando Molina-Molina and Francisco García-Sánchez

Sensors 2021, 21(9), 2893; https://0-doi-org.brum.beds.ac.uk/10.3390/s21092893 - 21 Apr 2021

Cited by 13 | Viewed by 2941

Abstract

Real-word errors are characterized by being actual terms in the dictionary. By providing context, real-word errors are detected. Traditional methods to detect and correct such errors are mostly based on counting the frequency of short word sequences in a corpus. Then, the probability of a word being a real-word error is computed. On the other hand, state-of-the-art approaches make use of deep learning models to learn context by extracting semantic features from text. In this work, a deep learning model were implemented for correcting real-word errors in clinical text. Specifically, a Seq2seq Neural Machine Translation Model mapped erroneous sentences to correct them. For that, different types of error were generated in correct sentences by using rules. Different Seq2seq models were trained and evaluated on two corpora: the Wikicorpus and a collection of three clinical datasets. The medicine corpus was much smaller than the Wikicorpus due to privacy issues when dealing with patient information. Moreover, GloVe and Word2Vec pretrained word embeddings were used to study their performance. Despite the medicine corpus being much smaller than the Wikicorpus, Seq2seq models trained on the medicine corpus performed better than those models trained on the Wikicorpus. Nevertheless, a larger amount of clinical text is required to improve the results. Full article

(This article belongs to the Special Issue Role and Challenges of Healthcare Cognitive Computing: From Extraction to Data Analysis Techniques)

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15 pages, 1196 KiB

Open AccessArticle

Predictive Capacity of COVID-19 Test Positivity Rate

by Livio Fenga and Mauro Gaspari

Sensors 2021, 21(7), 2435; https://0-doi-org.brum.beds.ac.uk/10.3390/s21072435 - 01 Apr 2021

Cited by 11 | Viewed by 3208

Abstract

COVID-19 infections can spread silently, due to the simultaneous presence of significant numbers of both critical and asymptomatic to mild cases. While, for the former reliable data are available (in the form of number of hospitalization and/or beds in intensive care units), this is not the case of the latter. Hence, analytical tools designed to generate reliable forecast and future scenarios, should be implemented to help decision-makers to plan ahead (e.g., medical structures and equipment). Previous work of one of the authors shows that an alternative formulation of the Test Positivity Rate (TPR), i.e., the proportion of the number of persons tested positive in a given day, exhibits a strong correlation with the number of patients admitted in hospitals and intensive care units. In this paper, we investigate the lagged correlation structure between the newly defined TPR and the hospitalized people time series, exploiting a rigorous statistical model, the Seasonal Auto Regressive Moving Average (SARIMA). The rigorous analytical framework chosen, i.e., the stochastic processes theory, allowed for a reliable forecasting about 12 days ahead of those quantities. The proposed approach would also allow decision-makers to forecast the number of beds in hospitals and intensive care units needed 12 days ahead. The obtained results show that a standardized TPR index is a valuable metric to monitor the growth of the COVID-19 epidemic. The index can be computed on daily basis and it is probably one of the best forecasting tools available today for predicting hospital and intensive care units overload, being an optimal compromise between simplicity of calculation and accuracy. Full article

(This article belongs to the Special Issue Role and Challenges of Healthcare Cognitive Computing: From Extraction to Data Analysis Techniques)

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13 pages, 550 KiB

Open AccessArticle

The Prediction of Body Mass Index from Negative Affectivity through Machine Learning: A Confirmatory Study

by Giovanni Delnevo, Giacomo Mancini, Marco Roccetti, Paola Salomoni, Elena Trombini and Federica Andrei

Sensors 2021, 21(7), 2361; https://0-doi-org.brum.beds.ac.uk/10.3390/s21072361 - 29 Mar 2021

Cited by 11 | Viewed by 3538

Abstract

This study investigates on the relationship between affect-related psychological variables and Body Mass Index (BMI). We have utilized a novel method based on machine learning (ML) algorithms that forecast unobserved BMI values based on psychological variables, like depression, as predictors. We have employed various machine learning algorithms, including gradient boosting and random forest, with psychological variables relative to 221 subjects to predict both the BMI values and the BMI status (normal, overweight, and obese) of those subjects. We have found that the psychological variables in use allow one to predict both the BMI values (with a mean absolute error of 5.27–5.50) and the BMI status with an accuracy of over 80% (metric: F1-score). Further, our study has also confirmed the particular efficacy of psychological variables of negative type, such as depression for example, compared to positive ones, to achieve excellent predictive BMI values. Full article

(This article belongs to the Special Issue Role and Challenges of Healthcare Cognitive Computing: From Extraction to Data Analysis Techniques)

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22 pages, 3258 KiB

Open AccessArticle

A Cross-Regional Analysis of the COVID-19 Spread during the 2020 Italian Vacation Period: Results from Three Computational Models Are Compared

by Luca Casini and Marco Roccetti

Sensors 2020, 20(24), 7319; https://0-doi-org.brum.beds.ac.uk/10.3390/s20247319 - 19 Dec 2020

Cited by 18 | Viewed by 3221

Abstract

On 21 February 2020, a violent COVID-19 outbreak, which was initially concentrated in Lombardy before infecting some surrounding regions exploded in Italy. Shortly after, on 9 March, the Italian Government imposed severe restrictions on its citizens, including a ban on traveling to other parts of the country. No travel, no virus spread. Many regions, such as those in southern Italy, were spared. Then, in June 2020, under pressure for the economy to reopen, many lockdown measures were relaxed, including the ban on interregional travel. As a result, the virus traveled for hundreds of kilometers, from north to south, with the effect that areas without infections, receiving visitors from infected areas, became infected. This resulted in a sharp increase in the number of infected people; i.e., the daily count of new positive cases, when comparing measurements from the beginning of July to those from at the middle of September, rose significantly in almost all the Italian regions. Upon confirmation of the effect of Italian domestic tourism on the virus spread, three computational models of increasing complexity (linear, negative binomial regression, and cognitive) have been compared in this study, with the aim of identifying the one that better correlates the relationship between Italian tourist flows during the summer of 2020 and the resurgence of COVID-19 cases across the country. Results show that the cognitive model has more potential than the others, yet has relevant limitations. The models should be considered as a relevant starting point for the study of this phenomenon, even if there is still room to further develop them up to a point where they become able to capture all the various and complex spread patterns of this disease. Full article

(This article belongs to the Special Issue Role and Challenges of Healthcare Cognitive Computing: From Extraction to Data Analysis Techniques)

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14 pages, 628 KiB

Open AccessArticle

Accuracy of Mobile Applications versus Wearable Devices in Long-Term Step Measurements

by Filippo Piccinini, Giovanni Martinelli and Antonella Carbonaro

Sensors 2020, 20(21), 6293; https://0-doi-org.brum.beds.ac.uk/10.3390/s20216293 - 05 Nov 2020

Cited by 17 | Viewed by 3829

Abstract

Fitness sensors and health systems are paving the way toward improving the quality of medical care by exploiting the benefits of new technology. For example, the great amount of patient-generated health data available today gives new opportunities to measure life parameters in real time and create a revolution in communication for professionals and patients. In this work, we concentrated on the basic parameter typically measured by fitness applications and devices—the number of steps taken daily. In particular, the main goal of this study was to compare the accuracy and precision of smartphone applications versus those of wearable devices to give users an idea about what can be expected regarding the relative difference in measurements achieved using different system typologies. In particular, the data obtained showed a difference of approximately 30%, proving that smartphone applications provide inaccurate measurements in long-term analysis, while wearable devices are precise and accurate. Accordingly, we challenge the reliability of previous studies reporting data collected with phone-based applications, and besides discussing the current limitations, we support the use of wearable devices for mHealth. Full article

(This article belongs to the Special Issue Role and Challenges of Healthcare Cognitive Computing: From Extraction to Data Analysis Techniques)

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Role and Challenges of Healthcare Cognitive Computing: From Extraction to Data Analysis Techniques

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