Recent Advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration

Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.06.006 · Cited by 10,578 · see citing works

Flexible triboelectric generator

Fan, F.-R., Tian, Z.-Q., & Lin Wang, Z. (2012) · Nano Energy · doi:10.1016/j.nanoen.2012.01.004 · Cited by 6,705 · see citing works

The potential for artificial intelligence in healthcare

Davenport, T., & Kalakota, R. (2019) · Future Healthcare Journal · doi:10.7861/futurehosp.6-2-94 · Cited by 3,800 · see citing works

Machine learning and deep learning

Janiesch, C., Zschech, P., & Heinrich, K. (2021) · Electronic Markets · doi:10.1007/s12525-021-00475-2 · Cited by 2,959 · see citing works

Artificial intelligence in healthcare

Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817 · see citing works

Surface plasmon resonance for gas detection and biosensing

Liedberg, B., Nylander, C. I., & Lunström, I. (1983) · Sensors and Actuators · doi:10.1016/0250-6874(83)85036-7 · Cited by 2,545 · see citing works

A review of wearable sensors and systems with application in rehabilitation

Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-21 · Cited by 2,396 · see citing works

Machine learning and data mining methods in diabetes research

Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.12.005 · Cited by 1,445 · see citing works

Wearable sensors: modalities, challenges, and prospects

Heikenfeld, J., Jajack, A., Rogers, J., Gutruf, P., Tian, L., Pan, T., Li, R., Khine, M., Kim, J., Wang, J., et al. (2018) · Lab on a Chip · doi:10.1039/c7lc00914c · Cited by 1,434 · see citing works

Introduction to biosensors

Bhalla, N., Jolly, P., Formisano, N., & Estrela, P. (2016) · Essays in Biochemistry · doi:10.1042/ebc20150001 · Cited by 1,370 · see citing works

Glucose Biosensors: An Overview of Use in Clinical Practice

Yoo, E.-H., & Lee, S.-Y. (2010) · Sensors · doi:10.3390/s100504558 · Cited by 1,137 · see citing works

A governance model for the application of AI in health care

Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz192 · Cited by 979 · see citing works

Mapping the challenges of artificial intelligence in the public sector: Evidence from public healthcare

Sun, T. Q., & Medaglia, R. (2019) · Government Information Quarterly · doi:10.1016/j.giq.2018.09.008 · Cited by 948 · see citing works

Carbonized Silk Fabric for Ultrastretchable, Highly Sensitive, and Wearable Strain Sensors

Wang, C., Li, X., Gao, E., Jian, M., Xia, K., Wang, Q., Xu, Z., Ren, T., & Zhang, Y. (2016) · Advanced Materials · doi:10.1002/adma.201601572 · Cited by 916 · see citing works

Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0

Aceto, G., Persico, V., & Pescapè, A. (2020) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2020.100129 · Cited by 916 · see citing works

Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review

Xiao, C., Choi, E., & Sun, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy068 · Cited by 792 · see citing works

Recent Progress in Materials and Devices toward Printable and Flexible Sensors

Rim, Y. S., Bae, S., Chen, H., Marco, N. D., & Yang, Y. (2016) · Advanced Materials · doi:10.1002/adma.201505118 · Cited by 774 · see citing works

Multiplexed Point-of-Care Testing – xPOCT

Dincer, C., Bruch, R. C., Kling, A., Dittrich, P. S., & Urban, G. A. (2017) · Trends in biotechnology · doi:10.1016/j.tibtech.2017.03.013 · Cited by 508 · see citing works

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works

OmniPHR: a distributed architecture model to integrate personal health records

Roehrs, A., da Costa, C. A., & da Rosa Righi, R. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.05.012 · Cited by 428 · see citing works

Biosensors based on graphene oxide and its biomedical application

Lee, J., Kim, J., Kim, S., & Min, D. (2016) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2016.06.001 · Cited by 412 · see citing works

IoT and artificial intelligence implementations for remote healthcare monitoring systems: A survey

Alshamrani, M. I. (2021) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.06.005 · Cited by 395 · see citing works

Application of artificial intelligence in wearable devices: Opportunities and challenges

Nahavandi, D., Alizadehsani, R., Khosravi, A., & Acharya, U. R. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106541 · Cited by 392 · see citing works

Environmentally-friendly conductive cotton fabric as flexible strain sensor based on hot press reduced graphene oxide

Ren, J., Wang, C., Zhang, X. K., Carey, T., Chen, K., Yin, Y., & Torrisi, F. (2016) · Carbon · doi:10.1016/j.carbon.2016.10.045 · Cited by 365 · see citing works

Applications of Artificial Intelligence in Machine Learning: Review and Prospect

Das, S., Dey, A., Pal, A., & Roy, N. (2015) · International Journal of Computer Applications · doi:10.5120/20182-2402 · Cited by 360 · see citing works

Making machine learning robust against adversarial inputs

Goodfellow, I., McDaniel, P., & Papernot, N. (2018) · Communications of the ACM · doi:10.1145/3134599 · Cited by 358 · see citing works

Trends in IoT based solutions for health care: Moving AI to the edge

Greco, L., Percannella, G., Ritrovato, P., Tortorella, F., & Vento, M. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.05.016 · Cited by 318 · see citing works

Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System

Kiral-Kornek, I., Roy, S., Nurse, E. S., Mashford, B. S., Karoly, P. J., Carroll, T., Payne, D. E., Saha, S., Baldassano, S. N., O'Brien, T. J., et al. (2017) · EBioMedicine · doi:10.1016/j.ebiom.2017.11.032 · Cited by 303 · see citing works

Quantitating Physical Activity in COPD Using a Triaxial Accelerometer

Steele, B. G., Holt, L., Belza, B. L., Ferris, S. T., Lakshminaryan, S., & Büchner, D. M. (2000) · CHEST Journal · doi:10.1378/chest.117.5.1359 · Cited by 289 · see citing works

Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards

da Costa, C. A., Pasluosta, C. F., Eskofier, B. M., da Silva, D. B., & Righi, R. D. R. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.05.005 · Cited by 278 · see citing works

Recent developments in biosensors for healthcare and biomedical applications: A review

Mohankumar, P., Ajayan, J., Mohanraj, T., & Yasodharan, R. (2020) · Measurement · doi:10.1016/j.measurement.2020.108293 · Cited by 267 · see citing works

A Review of Piezoelectric and Magnetostrictive Biosensor Materials for Detection of COVID‐19 and Other Viruses

Narita, F., Wang, Z., Kurita, H., Li, Z., Shi, Y., Jia, Y., & Soutis, C. (2020) · Advanced Materials · doi:10.1002/adma.202005448 · Cited by 263 · see citing works

Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature

Triantafyllidis, A. K., & Tsanas, A. (2019) · Journal of Medical Internet Research · doi:10.2196/12286 · Cited by 243 · see citing works

Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

Cappon, G., Acciaroli, G., Vettoretti, M., Facchinetti, A., & Sparacino, G. (2017) · Electronics · doi:10.3390/electronics6030065 · Cited by 242 · see citing works

Vital Signs in Older Patients: Age-Related Changes

Chester, J. G., & Rudolph, J. L. (2010) · Journal of the American Medical Directors Association · doi:10.1016/j.jamda.2010.04.009 · Cited by 239 · see citing works

A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care

Alanazi, H. O., Abdullah, A. H., & Qureshi, K. N. (2017) · Journal of Medical Systems · doi:10.1007/s10916-017-0715-6 · Cited by 219 · see citing works

Machine Learning/Artificial Intelligence for Sensor Data Fusion–Opportunities and Challenges

Blasch, E., Pham, T., Chong, C.-Y., Koch, W., Leung, H., Braines, D., & Abdelzaher, T. (2021) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2020.3049030 · Cited by 207 · see citing works

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

Tobore, I., Li, J., Yuhang, L., Al-Handarish, Y., Kandwal, A., Nie, Z., & Wang, L. (2019) · JMIR mHealth and uHealth · doi:10.2196/11966 · Cited by 180 · see citing works

Artificial intelligence in healthcare and medicine: promises, ethical challenges and governance

Guan, J. (2019) · Chinese Medical Sciences Journal · doi:10.24920/003611 · Cited by 170 · see citing works

ECG Signal Classification Using Various Machine Learning Techniques

Celin, S., & Vasanth, K. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1083-6 · Cited by 169 · see citing works

Artificial intelligence and machine learning in emergency medicine

Stewart, J. E., Sprivulis, P., & Dwivedi, G. (2018) · Emergency Medicine Australasia · doi:10.1111/1742-6723.13145 · Cited by 168 · see citing works

Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics

Shashikumar, S. P., Stanley, M. D., Sadiq, I., Li, Q., Holder, A., Clifford, G. D., & Nemati, S. (2017) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2017.08.013 · Cited by 167 · see citing works

Application of Internet of Things and artificial intelligence for smart fitness: A survey

Farrokhi, A., Farahbakhsh, R., Rezazadeh, J., & Minerva, R. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.107859 · Cited by 153 · see citing works

Healthcare 5.0: A Paradigm Shift in Digital Healthcare System Using Artificial Intelligence, IOT and 5G Communication

Mohanta, B., Das, P. R., & Patnaik, S. (2019) · International Conference on Applied Machine Learning (ICAML) · doi:10.1109/icaml48257.2019.00044 · Cited by 152 · see citing works

A deep learning based ensemble learning method for epileptic seizure prediction

Usman, S. M., Khalid, S., & Bashir, S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104710 · Cited by 152 · see citing works

Fully integrated flexible biosensor for wearable continuous glucose monitoring

Jin, X., Li, G., Xu, T., Su, L., Yan, D., & Zhang, X. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2021.113760 · Cited by 146 · see citing works

Enhanced stretchable graphene-based triboelectric nanogenerator via control of surface nanostructure

Chen, H., Xu, Y. F., Zhang, J., Wu, W., & Song, G. (2019) · Nano Energy · doi:10.1016/j.nanoen.2019.01.029 · Cited by 145 · see citing works

Automatic Classification of Tremor Severity in Parkinson’s Disease Using a Wearable Device

Jeon, H., Lee, W., Park, H.-Y., Lee, H. X., Kim, S., Kim, H., Jeon, B., & Park, K. J. (2017) · Sensors · doi:10.3390/s17092067 · Cited by 144 · see citing works

Correlates of Physical Activity in Chronic Obstructive Pulmonary Disease

Belza, B. L., Steele, B. G., Hunziker, J., Lakshminaryan, S., Holt, L., & Büchner, D. M. (2001) · Nursing Research · doi:10.1097/00006199-200107000-00003 · Cited by 129 · see citing works

Wearable Skin Sensors and Their Challenges: A Review of Transdermal, Optical, and Mechanical Sensors

Ahmad Tarar, A., Mohammad, U., & K. Srivastava, S. (2020) · Biosensors · doi:10.3390/bios10060056 · Cited by 119 · see citing works

Conductive Cotton Fabrics for Motion Sensing and Heating Applications

Yang, M., Pan, J., Xu, A., Luo, L., Cheng, D., Cai, G., Wang, J., Tang, B., & Wang, X. (2018) · Polymers · doi:10.3390/polym10060568 · Cited by 117 · see citing works

A Highly Aligned Nanowire‐Based Strain Sensor for Ultrasensitive Monitoring of Subtle Human Motion

Tang, N., Zhou, C. J., Qu, D., Fang, Y., Zheng, Y., Hu, W., Jin, K., Wu, W., Duan, X., & Haick, H. (2020) · Small · doi:10.1002/smll.202001363 · Cited by 114 · see citing works

Regulatory alternatives for AI

Clarke, R. (2019) · Computer law & security review · doi:10.1016/j.clsr.2019.04.008 · Cited by 104 · see citing works

Wearable Assistive Tactile Communication Interface Based on Integrated Touch Sensors and Actuators

Ozioko, O., Karipoth, P., Hersh, M. A., & Dahiya, R. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.2986222 · Cited by 104 · see citing works

Facile Fabrication of High-Performance Pen Ink-Decorated Textile Strain Sensors for Human Motion Detection

Yang, S., Li, C., Chen, X., Zhao, Y., Zhang, H., Wen, N., Fan, Z., & Pan, L. (2020) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b22534 · Cited by 99 · see citing works

Core-sheath nanofiber yarn for textile pressure sensor with high pressure sensitivity and spatial tactile acuity

Qi, K., Wang, H., You, X., Tao, X., Li, M., Zhou, Y., Zhang, Y., He, J., Shao, W., & Cui, S. (2019) · Journal of Colloid and Interface Science · doi:10.1016/j.jcis.2019.11.059 · Cited by 98 · see citing works

Advances in Medical Wearable Biosensors: Design, Fabrication and Materials Strategies in Healthcare Monitoring

Pillai, S., Upadhyay, A., Sayson, D., Nguyen, B. H., & Tran, S. D. (2021) · Molecules · doi:10.3390/molecules27010165 · Cited by 95 · see citing works

Review on “Blockchain technology based medical healthcare system with privacy issues”

Saha, A., Amin, R., Kunal, S., Vollala, S., & Dwivedi, S. K. (2019) · Security and Privacy · doi:10.1002/spy2.83 · Cited by 92 · see citing works

Deep-learning for seizure forecasting in canines with epilepsy

Nejedlý, P., Křemen, V., Sladky, V., Nasseri, M., Guragain, H., Klimeš, P., Cimbálník, J., Varatharajah, Y., Brinkmann, B. H., & Worrell, G. A. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab172d · Cited by 91 · see citing works

Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

Kumar, G., Basri, S. B., Imam, A. A., Khowaja, S. A., Capretz, L. F., & Balogun, A. O. (2021) · Applied Sciences · doi:10.3390/app11178275 · Cited by 91 · see citing works

Lean thinking in healthcare - Findings from a systematic literature network and bibliometric analysis.

Akmal, A., Greatbanks, R., & Foote, J. L. (2020) · Health Policy · doi:10.1016/j.healthpol.2020.04.008 · Cited by 73 · see citing works

Personalized Risk Scoring for Critical Care Prognosis Using Mixtures of Gaussian Processes

Alaa, A. M., Yoon, J., Hu, S. B., & van der Schaar, M. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2698602 · Cited by 70 · see citing works

All‐3D‐Printed, Flexible, and Hybrid Wearable Bioelectronic Tactile Sensors Using Biocompatible Nanocomposites for Health Monitoring

Qian, Y. H., Najafikhoshnoo, S., Das, P., Noh, S., Hoang, E., Kim, T., & Esfandyarpour, R. (2021) · Advanced Materials Technologies · doi:10.1002/admt.202101034 · Cited by 69 · see citing works

Advanced biomaterials for biosensor and theranostics

Liu, H., Ge, J., Ma, E., & Yang, L. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-813477-1.00010-4 · Cited by 66 · see citing works

Non-invasive wearable seizure detection using long–short-term memory networks with transfer learning

Nasseri, M., Pal Attia, T., Joseph, B., Gregg, N. M., Nurse, E. S., Viana, P. F., Schulze-Bonhage, A., Dümpelmann, M., Worrell, G., Freestone, D. R., et al. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abef8a · Cited by 66 · see citing works

A Mobile Sensing Approach to Stress Detection and Memory Activation for Public Bus Drivers

Rodrigues, J. G. P., Kaiseler, M., Aguiar, A., Cunha, J. P. S., & Barros, J. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2445314 · Cited by 65 · see citing works

Belief rule-based inference for predicting trauma outcome

Kong, G., Xu, D., Yang, J., Yin, X., Wang, T., Jiang, B., & Hu, Y. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.12.002 · Cited by 59 · see citing works

Patient-specific learning in real time for adaptive monitoring in critical care

Zhang, Y., & Peter Szolovits, B. (2008) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2008.03.011 · Cited by 58 · see citing works

Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department

Azeez, D., Ali, M. A. M., Gan, K. B., & Saiboon, I. M. (2013) · SpringerPlus · doi:10.1186/2193-1801-2-416 · Cited by 56 · see citing works

A facile way of fabricating a flexible and conductive cotton fabric

Liu, S., Hu, M., & Yang, J. (2016) · Journal of Materials Chemistry C · doi:10.1039/c5tc03679h · Cited by 52 · see citing works

Nanobiosensors and Nanobioanalyses

Vestergaard, M. C. M., Kerman, K., Hsing, I., & Tamiya, E. (2015) · Springer eBooks · doi:10.1007/978-4-431-55190-4 · Cited by 51 · see citing works

A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data

Muneer, A., Taib, S. M., Fati, S. M., Balogun, A. O., & Aziz, I. A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.021113 · Cited by 49 · see citing works

Classifications of Sustainable Factors in Blockchain Adoption: A Literature Review and Bibliometric Analysis

AL-Ashmori, A. M., Basri, S. B., Dominic, P. D. D., Capretz, L. F., Muneer, A., Balogun, A. O., Gilal, A. R., & Ali, R. F. (2022) · Sustainability · doi:10.3390/su14095176 · Cited by 49 · see citing works

Highly stretchable potentiometric ion sensor based on surface strain redistributed fiber for sweat monitoring

Wang, S., Bai, Y., Yang, X., Liu, L., Li, L., Lu, Q., Li, T., & Zhang, T. (2020) · Talanta · doi:10.1016/j.talanta.2020.120869 · Cited by 47 · see citing works

Automating and improving cardiovascular disease prediction using Machine learning and EMR data features from a regional healthcare system

Li, Q., Câmpan, A., Ren, A., & Eid, W. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104786 · Cited by 45 · see citing works

Artificial Intelligence (AI) and Internet of Things (IoT) in Healthcare: Opportunities and Challenges

Thakare, V., Khire, G., & Kumbhar, M. (2022) · ECS Transactions · doi:10.1149/10701.7941ecst · Cited by 44 · see citing works

Ultrasensitive and highly repeatable pen ink decorated cuprammonium rayon (cupra) fabrics for multifunctional sensors

Bi, S., Hou, L., Zhao, H., Zhu, L. Z., & Lu, Y. (2018) · Journal of Materials Chemistry A · doi:10.1039/c8ta04809f · Cited by 40 · see citing works

Semi-supervised learning to improve generalizability of risk prediction models

Chi, S., Li, X., Tian, Y., Li, J., Kong, X., Ding, K., Weng, C., & Li, J. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103117 · Cited by 38 · see citing works

Software Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Ranking Strategy

Balogun, A. O., Basri, S. B., Capretz, L. F., Mahamad, S., Imam, A. A., Almomani, M. A., Adeyemo, V. E., Alazzawi, A. K., Bajeh, A. O., & Kumar, G. (2021) · Symmetry · doi:10.3390/sym13112166 · Cited by 35 · see citing works

Design of Cost-effective Wearable Sensors with integrated Health Monitoring System

Nachiar, C. C., Ambika, N., Moulika, R., & Poovendran, R. (2020) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac49090.2020.9243462 · Cited by 32 · see citing works

Cloud-based IoMT framework for cardiovascular disease prediction and diagnosis in personalized E-health care

Adewole, K. S., Akintola, A. G., Jimoh, R. G., Mabayoje, M. A., Jimoh, M. K., Usman-Hamza, F. E., Balogun, A. O., Sangaiah, A. K., & Ameen, A. O. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821187-8.00005-8 · Cited by 32 · see citing works

Predicting mortality over different time horizons: which data elements are needed?

Goldstein, B. A., Pencina, M. J., Montez‐Rath, M. E., & Winkelmayer­, W. C. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw057 · Cited by 31 · see citing works

Highly sensitive and wide-dynamic-range side-polished fiber-optic taste sensor

Khalilian, A., Khan, M. R. R., & Kang, S. (2017) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2017.04.088 · Cited by 30 · see citing works

Design of Health Care Monitoring System Based on Internet of Thing (IOT)

Abdulameer, T. H., İbrahim, A. A., & Mohammed, A. H. (2020) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit50672.2020.9254291 · Cited by 28 · see citing works

A review on prediction and diagnosis of heart failure

Gnaneswar, B., & Jebarani, M. R. E. (2017) · International Conference on Innovations in Information, Embedded and Communication Systems · doi:10.1109/iciiecs.2017.8276033 · Cited by 26 · see citing works

A Novel Rank Aggregation‐Based Hybrid Multifilter Wrapper Feature Selection Method in Software Defect Prediction

Balogun, A. O., Basri, S. B., Mahamad, S., Capretz, L. F., Imam, A. A., Almomani, M. A., Adeyemo, V. E., & Kumar, G. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/5069016 · Cited by 26 · see citing works

Telemonitoring and experimentation in telemedicine for the improvement of healthcare pathways (ETAPES program). Sustainability beyond 2021: What type of organisational model and funding should be used?

Douarin, Y.-M. L., Traversino, Y., Graciet, A., Josseran, A., Bili, A.-B., Blaise, L., Châtellier, G., Coulonjou, H., Delval, C., Detournay, B., et al. (2019) · Therapies · doi:10.1016/j.therap.2019.12.009 · Cited by 25 · see citing works

Resource Use and Economic Implications of Remote Monitoring With Subcutaneous Cardiac Rhythm Monitors

Afzal, M. R., Nadkarni, A., Niemet, L., Houmsse, M. M., Devgun, J. K., Koppert, T. L., Ferguson, K., Mease, J., Okabe, T., Houmsse, M., et al. (2021) · JACC. Clinical electrophysiology · doi:10.1016/j.jacep.2020.10.014 · Cited by 21 · see citing works

Wearable alcohol monitoring device with auto-calibration ability for high chemical specificity

Umasankar, Y., Jalal, A. H., Gonzalez, P. J., Chowdhury, M., Alfonso, A. A., & Bhansali, S. (2016) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2016.7516287 · Cited by 19 · see citing works

Designing of a prototype of e-health monitoring system

Biswas, S., & Misra, S. (2015) · International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) · doi:10.1109/icrcicn.2015.7434248 · Cited by 18 · see citing works

An overview of artificial intelligence and big data analytics for smart healthcare: requirements, applications, and challenges

Chui, K. T., Lytras, M. D., Visvizi, A., & Sarirete, A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822060-3.00015-2 · Cited by 17 · see citing works

Correction: Automatic Classification of Tremor Severity in Parkinson’s Disease Using a Wearable Device. Sensors 2017, 17, 2067

Jeon, H., Lee, W., Park, H.-Y., Lee, H. X., Kim, S., Kim, H., Jeon, B., & Park, K. J. (2017) · Sensors · doi:10.3390/s18010033 · Cited by 14 · see citing works

Cardiorespiratory instability in monitored step-down unit patients: using cluster analysis to identify patterns of change

Bose, E. L., Clermont, G., Chen, L., Dubrawski, A. W., Ren, D., Hoffman, L. A., Pinsky, M. R., & Hravnak, M. (2017) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-017-0001-7 · Cited by 14 · see citing works

Estimating patient's health state using latent structure inferred from clinical time series and text

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Low-Code Machine Learning for Predicting Differentiated Thyroid Cancer Reoccurrence

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Application of Artificial Intelligence and Machine Learning in Polymer-Based Sensors

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Integrating Artificial Intelligence with Wearable Sensors for Advanced Health Monitoring and Diagnosis

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Artificial Intelligence in Remote Healthcare

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Emergence and Functionality of Artificial Intelligence (AI) and Machine Learning (ML) in Smart Wearables

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