Enhancing Disease Classification in Emergency Medical Scenarios Through Synthetic Data: A Comparative Study
2026 M. F. Kabir, M. M. I. Nayem, S. Tomforde
Agents and Artificial Intelligence — Lecture Notes in Computer Science (LNCS), Springer Nature Switzerland, Cham, pp. 286–308.
A comparative study of how synthetic data can improve disease-classification models for emergency medical scenarios, where real, labelled patient data is scarce and imbalanced.
Kabir, M. F., Nayem, M. M. I., & Tomforde, S. (2026). Enhancing disease classification in emergency medical scenarios through synthetic data: A comparative study. In Agents and Artificial Intelligence, Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 286–308.
Diagnosis and Treatment Support as an AI-Driven Assistant System for TeleNotary Emergency Care
2026 M. F. Kabir, K. Piliuk, S. Tomforde
Proceedings of the 18th International Conference on Agents and Artificial Intelligence (ICAART), INSTICC / SciTePress. Vol. 5, pp. 4651–4660. ISBN 978-989-758-796-2.
A deep multimodal architecture (time-series signals, tabular clinical records, and free-text notes) that produces probabilistic rankings of likely diagnoses, therapeutic interventions, and required physiological measurements for pre-hospital TeleNotArzt care. Evaluated with recall@k, macro/weighted AUC, and coverage-ratio metrics.
Kabir, M. F., Piliuk, K., & Tomforde, S. (2026). Diagnosis and treatment support as AI-driven assistant system for TeleNotary emergency care. In Proceedings of the 18th International Conference on Agents and Artificial Intelligence — Volume 5: ICAART, INSTICC, SciTePress, pp. 4651–4660.
Synthetic Data Generation for Emergency Medical Systems: A Systematic Comparison of Tabular GAN Extensions
2025 M. F. Kabir, M. M. I. Nayem, S. Tomforde
Proceedings of the 17th International Conference on Agents and Artificial Intelligence (ICAART), Volume 3, INSTICC / SciTePress, pp. 1199–1206. ISBN 978-989-758-737-5.
Introduces WLSTM-GAN — a single generator with two specialised LSTM networks and separate discriminators for continuous and categorical features — and systematically compares tabular GAN variants on three medical datasets, outperforming baselines on fidelity and privacy preservation for emergency medical data.
Kabir, M. F., Nayem, M. M. I., & Tomforde, S. (2025). Synthetic data generation for emergency medical systems: A systematic comparison of tabular GAN extensions. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence — Volume 3: ICAART, INSTICC, SciTePress, pp. 1199–1206.
A Deep Analysis for Medical Emergency Missing Value Imputation
2024 M. F. Kabir, S. Tomforde
Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART), Volume 3, INSTICC / SciTePress, pp. 1229–1236. ISBN 978-989-758-680-4.
Compares classical machine-learning imputation with denoising autoencoders and denoising LSTM autoencoders for numeric values missing completely at random in a real (non-public) telenotary emergency dataset with strong class imbalance and outliers. The denoising LSTM autoencoder performs best.
Kabir, M. F., & Tomforde, S. (2024). A deep analysis for medical emergency missing value imputation. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence — Volume 3: ICAART, INSTICC, SciTePress, pp. 1229–1236.
A Deep Learning Approach for the Classification of Highly Heterogeneous Data in the Context of Treatment of Medical Emergencies
2024 M. F. Kabir et al.
In Organic Computing (S. Tomforde & C. Krupitzer, Eds.), Chapter 10, kassel university press, pp. 121–135.
A multi-author chapter applying deep learning to highly heterogeneous emergency-care data, classifying diagnostic categories, medications, and physiological measurements to support faster TeleNotArzt decision-making.
Aust, T., Cui, H., Botache, D., Decke, J., Frost, E., Gerling, V., Heider, M., Henrichs, E., Hubert, A., Kabir, M. F., Kisselbach, T., Meitz, L., Pilar von Pilchau, W., Oguz Saltık, A., Schönberner, C., Spils, M., Steinberger, M., Kaul, N.-H., & Reichert, H. (2024). A deep learning approach for the classification of highly heterogeneous data in the context of treatment of medical emergencies. In S. Tomforde & C. Krupitzer (Eds.), Organic Computing, Chapter 10, kassel university press, pp. 121–135.