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Digital Health and Smart Sensors for Better Management of Cancer and Chronic Diseases

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

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 21994

Special Issue Editors

Dr. Shabbir Syed-Abdul

E-Mail Website
Guest Editor
Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan
Interests: wearable devices; IoT in health; big data analytics; data visualization; long-term care; cancer management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Panagiotis Bamidis

E-Mail Website
Guest Editor
Medical Physics and Digital Innovation Lab, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: digital health; mHealth and management of chronic diseases and cancer; virtual reality; medical education technology; co-creation; living labs
Special Issues, Collections and Topics in MDPI journals
Dr. Luis Fernandez Luque

E-Mail Website
Guest Editor
Qatar Computing Research Institute, Doha, Qatar
Interests: social media; digital health; wearable sensors; mHealth
Dr. Vicente Traver

E-Mail Website
Guest Editor
SABIEN- ITACA, Universitat Politècnica de València, València, Spain
Interests: E-health; telemedicine; mhealth; social media; ICT, health promotion
Special Issues, Collections and Topics in MDPI journals
Dr. Anton Gradisek

E-Mail Website
Guest Editor
Jozef Stefan Institute, Ljubljana, Slovenia
Interests: solid-state physics; NMR relaxometry; molecular dynamics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The last decade was prominently marked by the proliferation of “smart wearables”. This new decade is likely to see a broad adoption and application of wearable technologies in healthcare. Wearable devices are already used for the continuous monitoring of patients, better management of chronic diseases, prevention of emergency situations, improvement of care quality, and also reduction of health care costs.

In this Special Issue, we aim to focus on exploring how the use of smart sensors can improve management of chronic diseases and assist cancer survivor patients (e.g., through the collection of patient-reported outcome measurement—PREMs). It has been previously demonstrated that the approaches of telemedicine have favorable outcomes for patients with diseases such as diabetes [1], chronic heart failure [2], or chronic kidney disease [3], chronic obstructive pulmonary disease, cancer, and others [4,5]. Several technologies have been positively accepted both by patients and the doctors [3,4]. While many telemonitoring approaches rely solely on phone contact between patient and doctor, the incorporation of wearable sensor data in medical diagnostics and monitoring is gaining importance. Sensors may serve either a specific function, such as glucose monitors, or have more general applicability, such as sensors for monitoring heart rate, quality of sleep or physical activity. Sensors come in a variety of shapes and sizes; they can be dedicated wearables, smart patches, or one may instead rely on the integrated sensors in a smartphone. Wearables are able to generate vast amounts of data, which, coupled with big data analytic approaches, can be used for research, leading to further improvements in healthcare.

We anticipate that this issue will develop new insights into healthcare and research applications of wearable devices and in turn aid researchers and physicians and enhance the use of the devices among patients for a better management of cancer and chronic diseases.

Dr. Shabbir Syed Abdul
Prof. Panagiotis Bamidis
Dr. Luis Fernandez Luque
Dr. Vicente Traver
Dr. Anton Gradisek
Guest Editors

References

  1. Lee, Shaun Wen Huey, et al. "Comparative effectiveness of telemedicine strategies on type 2 diabetes management: a systematic review and network meta-analysis." Scientific reports 7.1 (2017): 12680.
  2. Bashi, Nazli, et al. "Remote monitoring of patients with heart failure: an overview of systematic reviews." Journal of medical Internet research 19.1 (2017): e18.
  3. Garcia, Marcos Antonio Martinez, et al. "Telemonitoring system for patients with chronic kidney disease undergoing peritoneal dialysis: Usability assessment based on a case study." PloS one 13.11 (2018): e0206600.
  4. Walker, Rachael C., et al. "Patient expectations and experiences of remote monitoring for chronic diseases: systematic review and thematic synthesis of qualitative studies." International journal of medical informatics 124 (2019): 78-85.
  5. Hanlon, Peter, et al. "Telehealth interventions to support self-management of long-term conditions: a systematic metareview of diabetes, heart failure, asthma, chronic obstructive pulmonary disease, and cancer." Journal of medical Internet research 19.5 (2017): e172.

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial intelligence
  • Digital health
  • Smart sensors
  • Physical activity
  • Sleep behavior
  • Cancer
  • Diabetes miletus
  • Chronic disease

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Published Papers (4 papers)

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Research

19 pages, 420 KiB  
Article
Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data
by Zun Shen, Qingfeng Wu, Zhi Wang, Guoyi Chen and Bin Lin
Sensors 2021, 21(11), 3663; https://doi.org/10.3390/s21113663 - 25 May 2021
Cited by 20 | Viewed by 3201
Abstract
(1) Background: Diabetic retinopathy, one of the most serious complications of diabetes, is the primary cause of blindness in developed countries. Therefore, the prediction of diabetic retinopathy has a positive impact on its early detection and treatment. The prediction of diabetic retinopathy based [...] Read more.
(1) Background: Diabetic retinopathy, one of the most serious complications of diabetes, is the primary cause of blindness in developed countries. Therefore, the prediction of diabetic retinopathy has a positive impact on its early detection and treatment. The prediction of diabetic retinopathy based on high-dimensional and small-sample-structured datasets (such as biochemical data and physical data) was the problem to be solved in this study. (2) Methods: This study proposed the XGB-Stacking model with the foundation of XGBoost and stacking. First, a wrapped feature selection algorithm, XGBIBS (Improved Backward Search Based on XGBoost), was used to reduce data feature redundancy and improve the effect of a single ensemble learning classifier. Second, in view of the slight limitation of a single classifier, a stacking model fusion method, Sel-Stacking (Select-Stacking), which keeps Label-Proba as the input matrix of meta-classifier and determines the optimal combination of learners by a global search, was used in the XGB-Stacking model. (3) Results: XGBIBS greatly improved the prediction accuracy and the feature reduction rate of a single classifier. Compared to a single classifier, the accuracy of the Sel-Stacking model was improved to varying degrees. Experiments proved that the prediction model of XGB-Stacking based on the XGBIBS algorithm and the Sel-Stacking method made effective predictions on diabetes retinopathy. (4) Conclusion: The XGB-Stacking prediction model of diabetic retinopathy based on biochemical and physical data had outstanding performance. This is highly significant to improve the screening efficiency of diabetes retinopathy and reduce the cost of diagnosis. Full article
(This article belongs to the Special Issue Digital Health and Smart Sensors for Better Management of Cancer and Chronic Diseases)
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25 pages, 1340 KiB  
Article
Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining
by Zoe Valero-Ramon, Carlos Fernandez-Llatas, Bernardo Valdivieso and Vicente Traver
Sensors 2020, 20(18), 5330; https://doi.org/10.3390/s20185330 - 17 Sep 2020
Cited by 11 | Viewed by 3101
Abstract
Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist [...] Read more.
Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health professionals in chronic disease management incorporating data generated through smart sensors in a more precise and personalised manner. However, current understanding of risk models relies on static snapshots of health variables or measures, rather than ongoing and dynamic feedback loops of behaviour, considering changes and different states of patients and diseases. The rationale of this work is to introduce a new method for discovering dynamic risk models for chronic diseases, based on patients’ dynamic behaviour provided by health sensors, using Process Mining techniques. Results show the viability of this method, three dynamic models have been discovered for the chronic diseases hypertension, obesity, and diabetes, based on the dynamic behaviour of metabolic risk factors associated. This information would support health professionals to translate a one-fits-all current approach to treatments and care, to a personalised medicine strategy, that fits treatments built on patients’ unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors. Full article
(This article belongs to the Special Issue Digital Health and Smart Sensors for Better Management of Cancer and Chronic Diseases)
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27 pages, 6399 KiB  
Article
OBINTER: A Holistic Approach to Catalyse the Self-Management of Chronic Obesity
by Roberto Álvarez, Jordi Torres, Garazi Artola, Gorka Epelde, Sara Arranz and Gerard Marrugat
Sensors 2020, 20(18), 5060; https://doi.org/10.3390/s20185060 - 6 Sep 2020
Cited by 6 | Viewed by 4122
Abstract
Obesity is a preventable chronic condition that, in 2016, affected more than 1.9 billion people globally. Several factors have been identified that have a positive impact on long-term weight loss programs such as personalized recommendations, adherence strategies, weight and diet follow-up or physical [...] Read more.
Obesity is a preventable chronic condition that, in 2016, affected more than 1.9 billion people globally. Several factors have been identified that have a positive impact on long-term weight loss programs such as personalized recommendations, adherence strategies, weight and diet follow-up or physical activity tracking. Recently, various applications have been developed which help patients to self-manage their condition. These apps implement either one or some of these identified factors; however, there is not a single application that combines all of them following a holistic approach. In this context, we developed the OBINTER platform, which assists patients during the weight loss process by targeting user engagement during the longer term. The solution includes a mobile application which allows users to fill out dietetic questionnaires, receive dietetic and nutraceutical plans, track the evolution of their weight and adherence to the diet, as well as track their physical activity via a wearable device. Furthermore, an adherence strategy has been developed as a tool to foster the app usage during the whole weight loss process. In this paper, we present how the OBINTER approach gathers all of these features as well as the positive results of a usability testing study performed to assess the performance and usability of the OBINTER platform. Full article
(This article belongs to the Special Issue Digital Health and Smart Sensors for Better Management of Cancer and Chronic Diseases)
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22 pages, 1466 KiB  
Article
Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods
by Muhammad Fazal Ijaz, Muhammad Attique and Youngdoo Son
Sensors 2020, 20(10), 2809; https://doi.org/10.3390/s20102809 - 15 May 2020
Cited by 196 | Viewed by 9760
Abstract
Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction [...] Read more.
Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction of cervical cancer using risk factors as inputs. The CCPM first removes outliers by using outlier detection methods such as density-based spatial clustering of applications with noise (DBSCAN) and isolation forest (iForest) and by increasing the number of cases in the dataset in a balanced way, for example, through synthetic minority over-sampling technique (SMOTE) and SMOTE with Tomek link (SMOTETomek). Finally, it employs random forest (RF) as a classifier. Thus, CCPM lies on four scenarios: (1) DBSCAN + SMOTETomek + RF, (2) DBSCAN + SMOTE+ RF, (3) iForest + SMOTETomek + RF, and (4) iForest + SMOTE + RF. A dataset of 858 potential patients was used to validate the performance of the proposed method. We found that combinations of iForest with SMOTE and iForest with SMOTETomek provided better performances than those of DBSCAN with SMOTE and DBSCAN with SMOTETomek. We also observed that RF performed the best among several popular machine learning classifiers. Furthermore, the proposed CCPM showed better accuracy than previously proposed methods for forecasting cervical cancer. In addition, a mobile application that can collect cervical cancer risk factors data and provides results from CCPM is developed for instant and proper action at the initial stage of cervical cancer. Full article
(This article belongs to the Special Issue Digital Health and Smart Sensors for Better Management of Cancer and Chronic Diseases)
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