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Investigating diseases and chemicals in COVID-19 literature with text mining. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT DATA INSIGHTS 2021. [PMCID: PMC8126089 DOI: 10.1016/j.jjimei.2021.100016] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Given the rapidly unfolding nature of the COVID-19 pandemic, there is an urgent need to streamline the literature synthesis of the growing scientific research to elucidate targeted solutions. Traditional systematic literature review studies have restrictions, including analyzing a limited number of papers, having various biases, being time-consuming and labor-intensive, focusing on a few topics, and lack of data-driven tools. This research has collected 9298 papers representing COVID-19 research published through May 5, 2020. We used frequency analysis to find highly frequent manifestations and therapeutic chemicals, representing the importance of the two biomedical concepts. This study also applied topic modeling that provided 25 categories showing associations between the two overarching categories. This study is beneficial to researchers for obtaining a macro-level picture of literature, to educators for knowing the scope of literature, and to policymakers and funding agencies for creating scientific strategic plans regarding COVID-19.
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Contreras Pinochet LH, Amorim GDCB, Lucas Júnior D, Souza CAD. Consequential factors of Big Data's Analytics Capability: how firms use data in the competitive scenario. JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT 2021. [DOI: 10.1108/jeim-11-2020-0445] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
PurposeThe article's objective is to analyze the consequent factors of Big Data Analytics Capability for firms in the competitive scenario, using different analytical models.Design/methodology/approachThe research had a quantitative approach, using a survey of data from firms located in the state of São Paulo – Brazil. Structural Equation Modeling (SEM) was used to validate the model.FindingsThe results reveal that all hypotheses were accepted. Business value was the construct that had the most explanatory power in the model. It is necessary to invest more in analytical tools, as well as people trained in the analysis of these models, in addition to a change of mindset, which will dictate the bias of the firm's strategic decision-making. The Big Data analysis is evident from firms' growing investments, particularly those that operate in complex and fast-paced environments.Practical implicationsThe proposed theoretical model makes it possible to verify firms' analytical structure and whether they are better positioned to analyze customer data and information in real-time, generate insights and implement solutions to maintain and improve their market position.Originality/valueThe contribution of this article is to present a proposal to expand the research models in the literature that analyzed the direct and indirect relationship between “Big Data Analytics Capability” and “Product Innovation Performance”.
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Abstract
AbstractNowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future.
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De Croon R, Van Houdt L, Htun NN, Štiglic G, Vanden Abeele V, Verbert K. Health Recommender Systems: Systematic Review. J Med Internet Res 2021; 23:e18035. [PMID: 34185014 PMCID: PMC8278303 DOI: 10.2196/18035] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 05/20/2020] [Accepted: 05/24/2021] [Indexed: 01/30/2023] Open
Abstract
Background Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior. Objective We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations. Methods We conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review. Results Recommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used. Conclusions There is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.
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Affiliation(s)
- Robin De Croon
- Department of Computer Science, KU Leuven, Leuven, Belgium
| | - Leen Van Houdt
- Department of Computer Science, KU Leuven, Leuven, Belgium
| | - Nyi Nyi Htun
- Department of Computer Science, KU Leuven, Leuven, Belgium
| | - Gregor Štiglic
- Faculty of Health Sciences, University of Maribor, Maribor, Slovenia
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Tran TNT, Felfernig A, Trattner C, Holzinger A. Recommender systems in the healthcare domain: state-of-the-art and research issues. J Intell Inf Syst 2020. [DOI: 10.1007/s10844-020-00633-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
AbstractNowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future.
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Holzinger A, Carrington A, Müller H. Measuring the Quality of Explanations: The System Causability Scale (SCS): Comparing Human and Machine Explanations. KUNSTLICHE INTELLIGENZ 2020; 34:193-198. [PMID: 32549653 PMCID: PMC7271052 DOI: 10.1007/s13218-020-00636-z] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 01/09/2020] [Indexed: 12/19/2022]
Abstract
Recent success in Artificial Intelligence (AI) and Machine Learning (ML) allow problem solving automatically without any human intervention. Autonomous approaches can be very convenient. However, in certain domains, e.g., in the medical domain, it is necessary to enable a domain expert to understand, why an algorithm came up with a certain result. Consequently, the field of Explainable AI (xAI) rapidly gained interest worldwide in various domains, particularly in medicine. Explainable AI studies transparency and traceability of opaque AI/ML and there are already a huge variety of methods. For example with layer-wise relevance propagation relevant parts of inputs to, and representations in, a neural network which caused a result, can be highlighted. This is a first important step to ensure that end users, e.g., medical professionals, assume responsibility for decision making with AI/ML and of interest to professionals and regulators. Interactive ML adds the component of human expertise to AI/ML processes by enabling them to re-enact and retrace AI/ML results, e.g. let them check it for plausibility. This requires new human–AI interfaces for explainable AI. In order to build effective and efficient interactive human–AI interfaces we have to deal with the question of how to evaluate the quality of explanations given by an explainable AI system. In this paper we introduce our System Causability Scale to measure the quality of explanations. It is based on our notion of Causability (Holzinger et al. in Wiley Interdiscip Rev Data Min Knowl Discov 9(4), 2019) combined with concepts adapted from a widely-accepted usability scale.
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Affiliation(s)
- Andreas Holzinger
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria.,xAI-Lab, Alberta Machine Intelligence Institute, Edmonton, Canada
| | - André Carrington
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Heimo Müller
- Diagnostic and Research Institute of Pathology, Medical University Graz, Graz, Austria
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Afolabi AO, Toivanen P. Harmonization and Categorization of Metrics and Criteria for Evaluation of Recommender Systems in Healthcare From Dual Perspectives. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2020. [DOI: 10.4018/ijehmc.2020010105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Researchers' choice of metrics and criteria in evaluating recommender systems depends on what the researcher feels is popular among other researchers, or sometimes based on the objective of the research. There is no harmonized set of criteria and metrics that can be referenced when evaluating recommender systems in healthcare. In this article, a set of metrics and criteria are harmonized and categorized as a guide for evaluating recommender systems. By means of an online survey, the opinions of forty-four experienced researchers and other stakeholders from eight countries and four continents were sought on the relevance of identified metrics and criteria. Analysis of the results show speed and timeliness are at the top. Topping the list of criteria is the provision of information that will guide users to useful decisions. The result is presented from two logical perspectives. Four categories are then identified as a useful guide for evaluating recommender systems.
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Affiliation(s)
| | - Pekka Toivanen
- University of Eastern Finland, School of Computing, Kuopio, Finland
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Erdeniz SP, Menychtas A, Maglogiannis I, Felfernig A, Tran TNT. Recommender systems for IoT enabled quantified-self applications. EVOLVING SYSTEMS 2019. [DOI: 10.1007/s12530-019-09302-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
AbstractAs an emerging trend in big data science, applications based onthe Quantified-Self (QS)engage individuals in the self-tracking of any kind of biological, physical, behavioral, or environmental information as individuals or groups. There are new needs and opportunities for recommender systems to develop new models/approaches to support QS application users.Recommender systemscan help to more easily identify relevant artifacts for users and thus improve user experiences. Currently recommender systems are widely and effectively used in the e-commerce domain (e.g., online music services, online bookstores). Next-generation QS applications could include more recommender tools for assisting the users of QS systems based on their personal self-tracking data streams from wearable electronics, biosensors, mobile phones, genomic data, and cloud-based services. In this paper, we propose three new recommendation approaches for QS applications:Virtual Coach,Virtual Nurse, andVirtual Sleep Regulatorwhich help QS users to improve their health conditions.Virtual Coachworks like a real fitness coach to recommend personalized work-out plans whereasVirtual Nurseconsiders the medical history and health targets of a user to recommend a suitable physical activity plan.Virtual Sleep Regulatoris specifically designed for insomnia (a kind of sleep disorder) patients to improve their sleep quality with the help of recommended physical activity and sleep plans. We explain how these proposed recommender technologies can be applied on the basis of the collected QS data to create qualitative recommendations for user needs. We present example recommendation results ofVirtual Sleep Regulatoron the basis of the dataset from a real world QS application.
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Spänig S, Emberger-Klein A, Sowa JP, Canbay A, Menrad K, Heider D. The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes. Artif Intell Med 2019; 100:101706. [PMID: 31607340 DOI: 10.1016/j.artmed.2019.101706] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 06/27/2019] [Accepted: 08/18/2019] [Indexed: 11/16/2022]
Abstract
Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. However, these systems are widely used, e.g., in diabetes or cancer prediction. In the current study, we developed an AI that is able to interact with a patient (virtual doctor) by using a speech recognition and speech synthesis system and thus can autonomously interact with the patient, which is particularly important for, e.g., rural areas, where the availability of primary medical care is strongly limited by low population densities. As a proof-of-concept, the system is able to predict type 2 diabetes mellitus (T2DM) based on non-invasive sensors and deep neural networks. Moreover, the system provides an easy-to-interpret probability estimation for T2DM for a given patient. Besides the development of the AI, we further analyzed the acceptance of young people for AI in healthcare to estimate the impact of such a system in the future.
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Affiliation(s)
- Sebastian Spänig
- Department of Bioinformatics, Faculty of Mathematics and Computer Science, Philipps-University of Marburg, Hans-Meerwein-Str. 6, 35037 Marburg, Germany
| | - Agnes Emberger-Klein
- Chair of Marketing and Management of Biogenic Resources, Weihenstephan-Triesdorf University of Applied Sciences/TUM Campus Straubing for Biotechnology and Sustainability, Petersgasse 18, 94315 Straubing, Germany
| | - Jan-Peter Sowa
- Department of Gastroenterology, Hepatology and Infectious Diseases, Otto-von-Guericke University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany
| | - Ali Canbay
- Department of Gastroenterology, Hepatology and Infectious Diseases, Otto-von-Guericke University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany
| | - Klaus Menrad
- Chair of Marketing and Management of Biogenic Resources, Weihenstephan-Triesdorf University of Applied Sciences/TUM Campus Straubing for Biotechnology and Sustainability, Petersgasse 18, 94315 Straubing, Germany
| | - Dominik Heider
- Department of Bioinformatics, Faculty of Mathematics and Computer Science, Philipps-University of Marburg, Hans-Meerwein-Str. 6, 35037 Marburg, Germany.
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Pan Y, He F, Yu H, Li H. Learning adaptive trust strength with user roles of truster and trustee for trust-aware recommender systems. APPL INTELL 2019. [DOI: 10.1007/s10489-019-01542-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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12
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Huang M, Han H, Wang H, Li L, Zhang Y, Bhatti UA. A Clinical Decision Support Framework for Heterogeneous Data Sources. IEEE J Biomed Health Inform 2018; 22:1824-1833. [PMID: 29994279 DOI: 10.1109/jbhi.2018.2846626] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
To keep pace with the developments in medical informatics, health medical data is being collected continually. But, owing to the diversity of its categories and sources, medical data has become so complicated in many hospitals that it now needs a clinical decision support (CDS) system for its management. To effectively utilize the accumulating health data, we propose a CDS framework that can integrate heterogeneous health data from different sources such as laboratory test results, basic information of patients, and health records into a consolidated representation of features of all patients. Using the electronic health medical data so created, multilabel classification was employed to recommend a list of diseases and thus assist physicians in diagnosing or treating their patients' health issues more efficiently. Once the physician diagnoses the disease of a patient, the next step is to consider the likely complications of that disease, which can lead to more diseases. Previous studies reveal that correlations do exist among some diseases. Considering these correlations, a k-nearest neighbors algorithm is improved for multilabel learning by using correlations among labels (CML-kNN). The CML- kNN algorithm first exploits the dependence between every two labels to update the origin label matrix and then performs multilabel learning to estimate the probabilities of labels by using the integrated features. Finally, it recommends the top N diseases to the physicians. Experimental results on real health medical data establish the effectiveness and practicability of the proposed CDS framework.
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Hors-Fraile S, Schneider F, Fernandez-Luque L, Luna-Perejon F, Civit A, Spachos D, Bamidis P, de Vries H. Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol. BMC Public Health 2018; 18:698. [PMID: 29871595 PMCID: PMC5989385 DOI: 10.1186/s12889-018-5612-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Accepted: 05/25/2018] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Smoking is one of the most avoidable health risk factors, and yet the quitting success rates are low. The usage of tailored health messages to support quitting has been proved to increase quitting success rates. Technology can provide convenient means to deliver tailored health messages. Health recommender systems are information-filtering algorithms that can choose the most relevant health-related items-for instance, motivational messages aimed at smoking cessation-for each user based on his or her profile. The goals of this study are to analyze the perceived quality of an mHealth recommender system aimed at smoking cessation, and to assess the level of engagement with the messages delivered to users via this medium. METHODS Patients participating in a smoking cessation program will be provided with a mobile app to receive tailored motivational health messages selected by a health recommender system, based on their profile retrieved from an electronic health record as the initial knowledge source. Patients' feedback on the messages and their interactions with the app will be analyzed and evaluated following an observational prospective methodology to a) assess the perceived quality of the mobile-based health recommender system and the messages, using the precision and time-to-read metrics and an 18-item questionnaire delivered to all patients who complete the program, and b) measure patient engagement with the mobile-based health recommender system using aggregated data analytic metrics like session frequency and, to determine the individual-level engagement, the rate of read messages for each user. This paper details the implementation and evaluation protocol that will be followed. DISCUSSION This study will explore whether a health recommender system algorithm integrated with an electronic health record can predict which tailored motivational health messages patients would prefer and consider to be of a good quality, encouraging them to engage with the system. The outcomes of this study will help future researchers design better tailored motivational message-sending recommender systems for smoking cessation to increase patient engagement, reduce attrition, and, as a result, increase the rates of smoking cessation. TRIAL REGISTRATION The trial was registered at clinicaltrials.org under the ClinicalTrials.gov identifier NCT03206619 on July 2nd 2017. Retrospectively registered.
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Affiliation(s)
- Santiago Hors-Fraile
- Department of Architecture and Computer Technology, Universidad de Sevilla, ETSII, Avenida Reina Mercedes S/N, 41012 Seville, Spain
- Department of Health Promotion, School for Public Health and Primary Care (Caphri), Maastricht University, P. Debyeplein 1, 6229 HA Maastricht, The Netherlands
| | - Francine Schneider
- Department of Health Promotion, School for Public Health and Primary Care (Caphri), Maastricht University, P. Debyeplein 1, 6229 HA Maastricht, The Netherlands
| | - Luis Fernandez-Luque
- Qatar Computing Research Institute, Hamad bin Khalifa University, Education City, Doha, Qatar
- Salumedia Tecnologías, Avenida República Argentina 24, Edificio Torre de los Remedios, Planta 5, Módulo A, Seville, Spain
| | - Francisco Luna-Perejon
- Department of Architecture and Computer Technology, Universidad de Sevilla, ETSII, Avenida Reina Mercedes S/N, 41012 Seville, Spain
| | - Anton Civit
- Department of Architecture and Computer Technology, Universidad de Sevilla, ETSII, Avenida Reina Mercedes S/N, 41012 Seville, Spain
| | - Dimitris Spachos
- Medical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Panagiotis Bamidis
- Medical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Hein de Vries
- Department of Health Promotion, School for Public Health and Primary Care (Caphri), Maastricht University, P. Debyeplein 1, 6229 HA Maastricht, The Netherlands
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Feitosa D, Dermeval D, Ávila T, Bittencourt II, Lóscio BF, Isotani S. A systematic review on the use of best practices for publishing linked data. ONLINE INFORMATION REVIEW 2018. [DOI: 10.1108/oir-11-2016-0322] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
Data providers have been increasingly publishing content as linked data (LD) on the Web. This process includes guidelines (i.e. good practices) to publish, share, and connect data on the Web. Several people in different areas, for instance, sciences, medicine, governments and so on, use these practices to publish data. The LD community has been proposing many practices to aid the publication of data on the Web. However, discovering these practices is a costly and time-consuming task, considering the practices that are produced by the literature. Moreover, the community still lacks a comprehensive understanding of how these practices are used for publishing LD. Thus, the purpose of this paper is to investigate and better understand how best practices support the publication of LD as well as identifying to what extent they have been applied to this field.
Design/methodology/approach
The authors conducted a systematic literature review to identify the primary studies that propose best practices to address the publication of LD, following a predefined review protocol. The authors then identified the motivations for recommending best practices for publishing LD and looked for evidence of the benefits of using such practices. The authors also examined the data formats and areas addressed by the studies as well as the institutions that have been publishing LD.
Findings
In summary, the main findings of this work are: there is empirical evidence of the benefits of using best practices for publishing LD, especially for defining standard practices, integrability and uniformity of LD; most of the studies used RDF as data format; there are many areas interested in dissemination data in a connected way; and there is a great variety of institutions that have published data on the Web.
Originality/value
The results presented in this systematic review can be very useful to the semantic web and LD community, since it gathers pieces of evidence from the primary studies included in the review, forming a body of knowledge regarding the use best practices for publishing LD pointing out interesting opportunities for future research.
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Shaikh FA, Kolowitz BJ, Awan O, Aerts HJ, von Reden A, Halabi S, Mohiuddin SA, Malik S, Shrestha RB, Deible C. Technical Challenges in the Clinical Application of Radiomics. JCO Clin Cancer Inform 2017; 1:1-8. [DOI: 10.1200/cci.17.00004] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Radiomics is a quantitative approach to medical image analysis targeted at deciphering the morphologic and functional features of a lesion. Radiomic methods can be applied across various malignant conditions to identify tumor phenotype characteristics in the images that correlate with their likelihood of survival, as well as their association with the underlying biology. Identifying this set of characteristic features, called tumor signature, holds tremendous value in predicting the behavior and progression of cancer, which in turn has the potential to predict its response to various therapeutic options. We discuss the technical challenges encountered in the application of radiomics, in terms of methodology, workflow integration, and user experience, that need to be addressed to harness its true potential.
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Affiliation(s)
- Faiq A. Shaikh
- Faiq A. Shaikh, Brian J. Kolowitz, Anna von Reden, Rasu B. Shrestha, and Christopher Deible, University of Pittsburgh Medical Center Enterprises, Pittsburgh; Omer Awan, Temple University, Philadelphia, PA; Hugo J. Aerts, Dana-Farber Cancer Institute, Boston, MA; Safwan Halabi, Stanford University, Stanford, CA; Sohaib A. Mohiuddin, University of Miami, Miami, FL; and Sana Malik, University of Chicago, Chicago, IL
| | - Brian J. Kolowitz
- Faiq A. Shaikh, Brian J. Kolowitz, Anna von Reden, Rasu B. Shrestha, and Christopher Deible, University of Pittsburgh Medical Center Enterprises, Pittsburgh; Omer Awan, Temple University, Philadelphia, PA; Hugo J. Aerts, Dana-Farber Cancer Institute, Boston, MA; Safwan Halabi, Stanford University, Stanford, CA; Sohaib A. Mohiuddin, University of Miami, Miami, FL; and Sana Malik, University of Chicago, Chicago, IL
| | - Omer Awan
- Faiq A. Shaikh, Brian J. Kolowitz, Anna von Reden, Rasu B. Shrestha, and Christopher Deible, University of Pittsburgh Medical Center Enterprises, Pittsburgh; Omer Awan, Temple University, Philadelphia, PA; Hugo J. Aerts, Dana-Farber Cancer Institute, Boston, MA; Safwan Halabi, Stanford University, Stanford, CA; Sohaib A. Mohiuddin, University of Miami, Miami, FL; and Sana Malik, University of Chicago, Chicago, IL
| | - Hugo J. Aerts
- Faiq A. Shaikh, Brian J. Kolowitz, Anna von Reden, Rasu B. Shrestha, and Christopher Deible, University of Pittsburgh Medical Center Enterprises, Pittsburgh; Omer Awan, Temple University, Philadelphia, PA; Hugo J. Aerts, Dana-Farber Cancer Institute, Boston, MA; Safwan Halabi, Stanford University, Stanford, CA; Sohaib A. Mohiuddin, University of Miami, Miami, FL; and Sana Malik, University of Chicago, Chicago, IL
| | - Anna von Reden
- Faiq A. Shaikh, Brian J. Kolowitz, Anna von Reden, Rasu B. Shrestha, and Christopher Deible, University of Pittsburgh Medical Center Enterprises, Pittsburgh; Omer Awan, Temple University, Philadelphia, PA; Hugo J. Aerts, Dana-Farber Cancer Institute, Boston, MA; Safwan Halabi, Stanford University, Stanford, CA; Sohaib A. Mohiuddin, University of Miami, Miami, FL; and Sana Malik, University of Chicago, Chicago, IL
| | - Safwan Halabi
- Faiq A. Shaikh, Brian J. Kolowitz, Anna von Reden, Rasu B. Shrestha, and Christopher Deible, University of Pittsburgh Medical Center Enterprises, Pittsburgh; Omer Awan, Temple University, Philadelphia, PA; Hugo J. Aerts, Dana-Farber Cancer Institute, Boston, MA; Safwan Halabi, Stanford University, Stanford, CA; Sohaib A. Mohiuddin, University of Miami, Miami, FL; and Sana Malik, University of Chicago, Chicago, IL
| | - Sohaib A. Mohiuddin
- Faiq A. Shaikh, Brian J. Kolowitz, Anna von Reden, Rasu B. Shrestha, and Christopher Deible, University of Pittsburgh Medical Center Enterprises, Pittsburgh; Omer Awan, Temple University, Philadelphia, PA; Hugo J. Aerts, Dana-Farber Cancer Institute, Boston, MA; Safwan Halabi, Stanford University, Stanford, CA; Sohaib A. Mohiuddin, University of Miami, Miami, FL; and Sana Malik, University of Chicago, Chicago, IL
| | - Sana Malik
- Faiq A. Shaikh, Brian J. Kolowitz, Anna von Reden, Rasu B. Shrestha, and Christopher Deible, University of Pittsburgh Medical Center Enterprises, Pittsburgh; Omer Awan, Temple University, Philadelphia, PA; Hugo J. Aerts, Dana-Farber Cancer Institute, Boston, MA; Safwan Halabi, Stanford University, Stanford, CA; Sohaib A. Mohiuddin, University of Miami, Miami, FL; and Sana Malik, University of Chicago, Chicago, IL
| | - Rasu B. Shrestha
- Faiq A. Shaikh, Brian J. Kolowitz, Anna von Reden, Rasu B. Shrestha, and Christopher Deible, University of Pittsburgh Medical Center Enterprises, Pittsburgh; Omer Awan, Temple University, Philadelphia, PA; Hugo J. Aerts, Dana-Farber Cancer Institute, Boston, MA; Safwan Halabi, Stanford University, Stanford, CA; Sohaib A. Mohiuddin, University of Miami, Miami, FL; and Sana Malik, University of Chicago, Chicago, IL
| | - Christopher Deible
- Faiq A. Shaikh, Brian J. Kolowitz, Anna von Reden, Rasu B. Shrestha, and Christopher Deible, University of Pittsburgh Medical Center Enterprises, Pittsburgh; Omer Awan, Temple University, Philadelphia, PA; Hugo J. Aerts, Dana-Farber Cancer Institute, Boston, MA; Safwan Halabi, Stanford University, Stanford, CA; Sohaib A. Mohiuddin, University of Miami, Miami, FL; and Sana Malik, University of Chicago, Chicago, IL
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Sanchez Bocanegra CL, Sevillano Ramos JL, Rizo C, Civit A, Fernandez-Luque L. HealthRecSys: A semantic content-based recommender system to complement health videos. BMC Med Inform Decis Mak 2017; 17:63. [PMID: 28506225 PMCID: PMC5433022 DOI: 10.1186/s12911-017-0431-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Accepted: 03/24/2017] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND The Internet, and its popularity, continues to grow at an unprecedented pace. Watching videos online is very popular; it is estimated that 500 h of video are uploaded onto YouTube, a video-sharing service, every minute and that, by 2019, video formats will comprise more than 80% of Internet traffic. Health-related videos are very popular on YouTube, but their quality is always a matter of concern. One approach to enhancing the quality of online videos is to provide additional educational health content, such as websites, to support health consumers. This study investigates the feasibility of building a content-based recommender system that links health consumers to reputable health educational websites from MedlinePlus for a given health video from YouTube. METHODS The dataset for this study includes a collection of health-related videos and their available metadata. Semantic technologies (such as SNOMED-CT and Bio-ontology) were used to recommend health websites from MedlinePlus. A total of 26 healths professionals participated in evaluating 253 recommended links for a total of 53 videos about general health, hypertension, or diabetes. The relevance of the recommended health websites from MedlinePlus to the videos was measured using information retrieval metrics such as the normalized discounted cumulative gain and precision at K. RESULTS The majority of websites recommended by our system for health videos were relevant, based on ratings by health professionals. The normalized discounted cumulative gain was between 46% and 90% for the different topics. CONCLUSIONS Our study demonstrates the feasibility of using a semantic content-based recommender system to enrich YouTube health videos. Evaluation with end-users, in addition to healthcare professionals, will be required to identify the acceptance of these recommendations in a nonsimulated information-seeking context.
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Affiliation(s)
| | | | | | - Anton Civit
- Department of Architecture and Computer Technology Universidad de Sevilla, Seville, Spain
| | - Luis Fernandez-Luque
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Qatar Foundation, PO Box 5825, Doha, Qatar.
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