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New Developments in AIoT Wearable Devices for Homecare of Patients with Cancer in Post COVID19 Era

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A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Survivorship and Quality of Life".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 6654

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Special Issue Editor

Dr. Shabbir Syed-Abdul

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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

Special Issue Information

Dear Colleagues,

Artificial Intelligence of Things (AIoT) is the combination of artificial intelligence and the Internet of Things (IoT) and has opened a wide range of opportunities for healthcare advancements. Wearable devices, non-contact sensors, and m-health technologies are some examples of the IoT which, when connected to the internet, can collect valuable medical data. These data can provide insights about the symptoms, the patterns, and variations, enable remote care and monitoring, and encourage participatory health care among patients. Research has focused on IoT devices and how they can be used to monitor health parameters and detect health conditions. Non-contact sensors are gaining popularity in clinical settings for monitoring the vital parameters of patients. Application of artificial intelligence (AI) algorithms on the data collected from IoT devices can help in prediction, early detection, and better management of diseases. These models can assist healthcare professionals in decision making and formulating better care plans for patients. Thus, artificial intelligence has a wide range of applications for healthcare data from IoT devices.

This SI will focus on the opportunities and implementation challenges of AIoT wearable devices for the homecare of patients with cancer.

Dr. Shabbir Syed-Abdul
Guest Editor

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. Cancers 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 2900 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 of Things
Artificial Intelligence
Internet of Things
healthcare advancements
wearable devices
medical data
cancer detection
cancer prediction
cancer management cancer care

Published Papers (3 papers)

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Research

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14 pages, 3803 KiB

Open AccessArticle

Predicting Long-Term Care Service Demands for Cancer Patients: A Machine Learning Approach

by Shuo-Chen Chien, Yu-Hung Chang, Chia-Ming Yen, Ying-Erh Chen, Chia-Chun Liu, Yu-Ping Hsiao, Ping-Yen Yang, Hong-Ming Lin, Xing-Hua Lu, I-Chien Wu, Chih-Cheng Hsu, Hung-Yi Chiou and Ren-Hua Chung

Cancers 2023, 15(18), 4598; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers15184598 - 16 Sep 2023

Cited by 2 | Viewed by 1182

Abstract

Background: Long-term care (LTC) service demands among cancer patients are significantly understudied, leading to gaps in healthcare resource allocation and policymaking. Objective: This study aimed to predict LTC service demands for cancer patients and identify the crucial factors. Methods: 3333 cases of cancers were included. We further developed two specialized prediction models: a Unified Prediction Model (UPM) and a Category-Specific Prediction Model (CSPM). The UPM offered generalized forecasts by treating all services as identical, while the CSPM built individual predictive models for each specific service type. Sensitivity analysis was also conducted to find optimal usage cutoff points for determining the usage and non-usage cases. Results: Service usage differences in lung, liver, brain, and pancreatic cancers were significant. For the UPM, the top 20 performance model cutoff points were adopted, such as through Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), and XGBoost (XGB), achieving an AUROC range of 0.707 to 0.728. The CSPM demonstrated performance with an AUROC ranging from 0.777 to 0.837 for the top five most frequently used services. The most critical predictive factors were the types of cancer, patients’ age and female caregivers, and specific health needs. Conclusion: The results of our study provide valuable information for healthcare decisions, resource allocation optimization, and personalized long-term care usage for cancer patients. Full article

(This article belongs to the Special Issue New Developments in AIoT Wearable Devices for Homecare of Patients with Cancer in Post COVID19 Era)

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27 pages, 8236 KiB

Open AccessArticle

Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information

by Yaoru Huang, Nidita Roy, Eshita Dhar, Umashankar Upadhyay, Muhammad Ashad Kabir, Mohy Uddin, Ching-Li Tseng and Shabbir Syed-Abdul

Cancers 2023, 15(8), 2232; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers15082232 - 10 Apr 2023

Cited by 2 | Viewed by 2262

Abstract

(1) Background: Predicting the survival of patients in end-of-life care is crucial, and evaluating their performance status is a key factor in determining their likelihood of survival. However, the current traditional methods for predicting survival are limited due to their subjective nature. Wearable technology that provides continuous patient monitoring is a more favorable approach for predicting survival outcomes among palliative care patients. (2) Aims and objectives: In this study, we aimed to explore the potential of using deep learning (DL) model approaches to predict the survival outcomes of end-stage cancer patients. Furthermore, we also aimed to compare the accuracy of our proposed activity monitoring and survival prediction model with traditional prognostic tools, such as the Karnofsky Performance Scale (KPS) and the Palliative Performance Index (PPI). (3) Method: This study recruited 78 patients from the Taipei Medical University Hospital’s palliative care unit, with 66 (39 male and 27 female) patients eventually being included in our DL model for predicting their survival outcomes. (4) Results: The KPS and PPI demonstrated an overall accuracy of 0.833 and 0.615, respectively. In comparison, the actigraphy data exhibited a higher accuracy at 0.893, while the accuracy of the wearable data combined with clinical information was even better, at 0.924. (5) Conclusion: Our study highlights the significance of incorporating clinical data alongside wearable sensors to predict prognosis. Our findings suggest that 48 h of data is sufficient for accurate predictions. The integration of wearable technology and the prediction model in palliative care has the potential to improve decision making for healthcare providers and can provide better support for patients and their families. The outcomes of this study can possibly contribute to the development of personalized and patient-centered end-of-life care plans in clinical practice. Full article

(This article belongs to the Special Issue New Developments in AIoT Wearable Devices for Homecare of Patients with Cancer in Post COVID19 Era)

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Review

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24 pages, 1098 KiB

Open AccessReview

A Scoping Review and a Taxonomy to Assess the Impact of Mobile Apps on Cancer Care Management

by Eshita Dhar, Adama Ns Bah, Irene Alice Chicchi Giglioli, Silvia Quer, Luis Fernandez-Luque, Francisco J. Núñez-Benjumea, Shwetambara Malwade, Mohy Uddin, Umashankar Upadhyay and Shabbir Syed-Abdul

Cancers 2023, 15(6), 1775; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers15061775 - 15 Mar 2023

Cited by 2 | Viewed by 2448

Abstract

Mobile Health (mHealth) has a great potential to enhance the self-management of cancer patients and survivors. Our study aimed to perform a scoping review to evaluate the impact and trends of mobile application-based interventions on adherence and their effects on health outcomes among the cancer population. In addition, we aimed to develop a taxonomy of mobile-app-based interventions to assist app developers and healthcare researchers in creating future mHealth cancer care solutions. Relevant articles were screened from the online databases PubMed, EMBASE, and Scopus, spanning the time period from 1 January 2016 to 31 December 2022. Of the 4135 articles initially identified, 55 were finally selected for the review. In the selected studies, breast cancer was the focus of 20 studies (36%), while mixed cancers were the subject of 23 studies (42%). The studies revealed that the usage rate of mHealth was over 80% in 41 of the 55 studies, with factors such as guided supervision, personalized suggestions, theoretical intervention foundations, and wearable technology enhancing adherence and efficacy. However, cancer progression, technical challenges, and unfamiliarity with devices were common factors that led to dropouts. We also proposed a taxonomy based on diverse theoretical foundations of mHealth interventions, delivery methods, psycho-educational programs, and social platforms. We suggest that future research should investigate, improve, and verify this taxonomy classification to enhance the design and efficacy of mHealth interventions. Full article

(This article belongs to the Special Issue New Developments in AIoT Wearable Devices for Homecare of Patients with Cancer in Post COVID19 Era)

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