An AI-Driven IoT Wearable Framework for Real-Time Health Monitoring and Early Risk Prediction in Lymphoma Patients Using Hybrid CNN-LSTM
International Journal of Medical Toxicology and Forensic Medicine,
Vol. 16 (2026),
1 January 2026
,
Page 1-7
https://doi.org/10.22037/ijmtfm.v16.53262
Abstract
Background: Lymphoma is a complex form of hematological cancer, and its early symptoms of disease progression need continuous monitoring for prompt treatment. The report proposes using the Internet of Things (IoT) for real-time patient monitoring, featuring wearable sensors, Deep Learning (DL) models, and an intelligent alert system to enhance predictive assessment of patient health.
Methods: These smart wearable devices continuously measure physiological parameters, including heart rate, oxygen saturation (SpO₂), temperature, and electrocardiogram (ECG) parameters. A hybrid CNN-LSTM model is employed for physiological time-series analysis, while a ResNet-50 model enhanced with a Spatial Attention Mechanism (SAM) is used for histopathological classification of CLL, FL, and MCL. The reported performance of these models is evaluated separately according to their respective tasks. In addition, a weighted physiological risk score is used to identify abnormal physiological states from continuously monitored HR, SpO₂, temperature, and ECG measurements. An alert is generated when the risk score exceeds the predefined threshold. The framework is intended as an AI-assisted monitoring and decision-support system rather than an autonomous clinical diagnostic system.
Results: The experimental results demonstrated that the proposed CNN-LSTM model achieved an accuracy of 96.1%, a precision of 94.7%, a recall of 95.5%, an F1-score of 95.1%, and an inference time of 7ms for the physiological time-series analysis. Additionally, ResNet-50+SAM had a remarkably low inference time of 6 ms and stable five-fold cross-validation results (mean accuracy: 97.50 ± 0.14%).
Conclusion: This proposed framework offers numerous benefits to patients by enabling early detection of important health events and facilitating communication with healthcare professionals. This research demonstrates that artificial intelligence and IoT can transform real-time health monitoring, providing physicians with valuable information for personalized, ongoing care management of lymphoma patients.
- Lymphoma Monitoring, IoT-based Healthcare, CNN-LSTM, ResNet-50 with SAM, Risk Assessment, AI-driven Alerts, Wearable Sensors
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References
[1] Morinaga S, Han Q, Mizuta K, Kang BM, Yamamoto N, Hayashi K, Kimura H, et al. Complete response (CR) in a previously-progressing chronic lymphocytic leukemia (CLL) patient treated with methionine restriction in combination with first-line chemotherapy. Cancer Diagn Progn. 2025;5(1):21. [DOI: 10.21873/cdp.10407]
[2] Izutsu K, Fukuhara N. JSH practical guidelines for hematological malignancies, 2023: II. Lymphoma-1. Follicular lymphoma (FL). Int J Hematol. 2025:1-9. [DOI: 10.1007/s12185-025-03922-4]
[3] Phillips TJ, Carlo-Stella C, Morschhauser F, Bachy E, Crump M, Trněný M, Bartlett NL, et al. Glofitamab in relapsed/refractory mantle cell lymphoma: results from a phase I/II study. J Clin Oncol. 2025;43(3):318-28. [DOI: 10.1200/JCO.23.02470]
[4] Wang M, Jurczak W, Trneny M, Belada D, Wrobel T, Ghosh N, Keating MM, et al. Ibrutinib plus venetoclax in relapsed or refractory mantle cell lymphoma (SYMPATICO): a multicentre, randomised, double-blind, placebo-controlled, phase 3 study. Lancet Oncol. 2025;26(2):200-13. [DOI: 10.1016/S1470-2045(24)00682-X]
[5] Soni P, Nagalli MM. Enhancing neonatal resuscitation outcomes: bridging theory and practice. Eur J Pediatr. 2025;184(4):1-13. [DOI: 10.1007/s00431-025-06087-8]
[6] Khan HA, XueQing G, Naeem MA, Siddique ZB. Automated labeling and prognostic prediction of nasopharyngeal carcinoma based on fuzzy c-mean and integrated CNN-LSTM model. Signal, Image and Video Processing. 2026 Jul;20(8):450. [DOI: 10.1007/s11760-026-05516-x]
[7] Thummar S, Bangoria D, Bhimani A, Thakor K, Chauhan A, Patel V, Rathod K, et al. A review on leukemia cancer detection and classification: integrating classical approaches to advanced AI techniques. AIP Conf Proc. 2025;3255(1). [DOI: 10.1063/5.0254153]
[8] Krajnc D, Spielvogel CP, Ecsedi B, Ritter Z, Alizadeh H, Hacker M, Papp L. Clinician-driven automated data preprocessing in nuclear medicine AI environments. Eur J Nucl Med Mol Imaging. 2025:1-11. [DOI: 10.1007/s00259-025-07183-5]
[9] Fu Y, Huang Z, Deng X, Xu L, Liu Y, Zhang M, Liu J, et al. Artificial intelligence in lymphoma histopathology: systematic review. J Med Internet Res. 2025;27:e62851. [DOI: 10.2196/62851]
[10] Gurnani K, D K. A survey of deep learning methods for leukemia detection and classification. In: 2026 International Conference on Emerging Trends in Mobile Computing and Sustainable Informatics (ICEMCSI). Bengaluru, India: IEEE; 2026. p. 1-5. [DOI: 10.1109/ICEMCSI67638.2026.11602860]
[11] Mandal S, Daivajna V. Machine learning based system for automatic detection of leukemia cancer cell. In: 2019 IEEE 16th India Council International Conference (INDICON). IEEE; 2019. p. 1-4. [DOI: 10.1109/INDICON47234.2019.9029034]
[12] Zolfaghari M, Sajedi H. A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells. Multimedia Tools Appl. 2022;81(5):6723-53. [DOI: 10.1007/s11042-022-12108-7]
[13] Kim D, Ahmed SS, Amjad A, Won K, Xian X. Integrating artificial intelligence with wearable sensors for advanced health monitoring and diagnosis. Biosensors. 2026;16. [DOI: 10.3390/bios16060344]
[14] Salem A, Teama M, Kassem HA, Vakilzadehian N, Ali AMA, Venugopal D, Khalifa AM. Artificial intelligence in hematologic malignancies: opportunities, challenges, and clinical integration. Cureus. 2026;18. [DOI: 10.7759/cureus.100950]
[15] Yang C, Chen Y, Zhu L, Wang L, Lin Q. A deep learning MRI-based signature may provide risk-stratification strategies for nasopharyngeal carcinoma. European Archives of Oto-Rhino-Laryngology. 2023;280(11):5039-47. [DOI: 10.1007/s00405-023-08084-9]
[16] Jacobsen M, Gholamipoor R, Dembek TA, Rottmann P, Verket M, Brandts J, Jäger P, et al. Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of hematologic malignancies. NPJ Digit Med. 2023;6(1). [DOI: 10.1038/s41746-023-00847-2]
[17] Ji L, Mao R, Wu J, Ge C, Xiao F, Xu X, Xie L, Gu X. Deep convolutional neural network for nasopharyngeal carcinoma discrimination on MRI by comparison of hierarchical and simple layered convolutional neural networks. Diagnostics. 2022;12(10):2478. [DOI: 10.3390/diagnostics12102478]
[18] Li Z, Zhao J, Huang L, Chen X, Wei JS, Tian W. Artificial intelligence reshaping the paradigm of hematologic malignancy diagnosis and treatment: from static assessment to dynamic precision management. Ann Hematol. 2026;105. [DOI: 10.1007/s00277-026-06996-0]
[19] PhysioNet. MIMIC-III Waveform Database. PhysioNet; Available from: PhysioNet MIMIC-III Waveform Database. [Link]
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