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22,957 results on '"AHMED, I."'

4. Hybrid Student-Teacher Large Language Model Refinement for Cancer Toxicity Symptom Extraction

Author

Khanmohammadi, Reza, Ghanem, Ahmed I., Verdecchia, Kyle, Hall, Ryan, Elshaikh, Mohamed, Movsas, Benjamin, Bagher-Ebadian, Hassan, Luo, Bing, Chetty, Indrin J., Alhanai, Tuka, Thind, Kundan, and Ghassemi, Mohammad M.

Subjects
Computer Science - Computation and Language, Computer Science - Information Retrieval
Abstract

Large Language Models (LLMs) offer significant potential for clinical symptom extraction, but their deployment in healthcare settings is constrained by privacy concerns, computational limitations, and operational costs. This study investigates the optimization of compact LLMs for cancer toxicity symptom extraction using a novel iterative refinement approach. We employ a student-teacher architecture, utilizing Zephyr-7b-beta and Phi3-mini-128 as student models and GPT-4o as the teacher, to dynamically select between prompt refinement, Retrieval-Augmented Generation (RAG), and fine-tuning strategies. Our experiments on 294 clinical notes covering 12 post-radiotherapy toxicity symptoms demonstrate the effectiveness of this approach. The RAG method proved most efficient, improving average accuracy scores from 0.32 to 0.73 for Zephyr-7b-beta and from 0.40 to 0.87 for Phi3-mini-128 during refinement. In the test set, both models showed an approximate 0.20 increase in accuracy across symptoms. Notably, this improvement was achieved at a cost 45 times lower than GPT-4o for Zephyr and 79 times lower for Phi-3. These results highlight the potential of iterative refinement techniques in enhancing the capabilities of compact LLMs for clinical applications, offering a balance between performance, cost-effectiveness, and privacy preservation in healthcare settings.

Published
2024

10. Unraveling the impact of miRNAs on gouty arthritis: diagnostic significance and therapeutic opportunities

Author

Abdel Mageed, Sherif S., Elimam, Hanan, Elesawy, Ahmed E., Abulsoud, Ahmed I., Raouf, Ahmed Amr, Tabaa, Manar Mohammed El, Mohammed, Osama A., Zaki, Mohamed Bakr, Abd-Elmawla, Mai A., El-Dakroury, Walaa A., Mangoura, Safwat Abdelhady, Elrebehy, Mahmoud A., Elballal, Mohammed S., Mohamed, Aya A., Ashraf, Alaa, Abdel-Reheim, Mustafa Ahmed, Eleragi, Ali M. S., Abdellatif, Hussein, and Doghish, Ahmed S.

Published
2024
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20. Diabetic Neuropathy: A Guide to Pain Management

Author

Zhang, Emily X., Yazdi, Cyrus, Islam, Rahib K., Anwar, Ahmed I., Alvares-Amado, Alana, Townsend, Horace, Allen, Kaitlyn E., Plakotaris, Elena, Hirsch, Jon D., Rieger, Ross G., Allampalli, Varsha, Hasoon, Jamal, Islam, Kazi N., Shekoohi, Sahar, Kaye, Alan D., and Robinson, Christopher L.

Published
2024
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25. Iterative Prompt Refinement for Radiation Oncology Symptom Extraction Using Teacher-Student Large Language Models

Author

Khanmohammadi, Reza, Ghanem, Ahmed I, Verdecchia, Kyle, Hall, Ryan, Elshaikh, Mohamed, Movsas, Benjamin, Bagher-Ebadian, Hassan, Chetty, Indrin, Ghassemi, Mohammad M., and Thind, Kundan

Subjects
Computer Science - Computation and Language
Abstract

This study introduces a novel teacher-student architecture utilizing Large Language Models (LLMs) to improve prostate cancer radiotherapy symptom extraction from clinical notes. Mixtral, the student model, initially extracts symptoms, followed by GPT-4, the teacher model, which refines prompts based on Mixtral's performance. This iterative process involved 294 single symptom clinical notes across 12 symptoms, with up to 16 rounds of refinement per epoch. Results showed significant improvements in extracting symptoms from both single and multi-symptom notes. For 59 single symptom notes, accuracy increased from 0.51 to 0.71, precision from 0.52 to 0.82, recall from 0.52 to 0.72, and F1 score from 0.49 to 0.73. In 375 multi-symptom notes, accuracy rose from 0.24 to 0.43, precision from 0.6 to 0.76, recall from 0.24 to 0.43, and F1 score from 0.20 to 0.44. These results demonstrate the effectiveness of advanced prompt engineering in LLMs for radiation oncology use.

Published
2024

28. Eco-friendly Strategies for Biological Synthesis of Green Nanoparticles with Promising Applications

Author

El-Sayyad, Gharieb S., Elfadil, Dounia, Mosleh, Mahmoud A., Hasanien, Yasmeen A., Mostafa, Abdo, Abdelkader, Rofaida S., Refaey, Neveen, Elkafoury, Eman M., Eshaq, Ghada, Abdelrahman, Ehab A., Malash, Mohamed N., Rizk, Samar H., Gobara, Mohamed, Nada, Hanady G., Hashem, Amr H., Attia, Mohamed S., Noreddin, Ayman M., Abdel Maksoud, M. I. A., Ghobashy, Mohamed Mohamady, Basher, Dina E., Magdy, Ragaa, Elkhatib, Walid F., and El-Batal, Ahmed I.

Published
2024
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32. Unraveling the miRNA Puzzle in Atherosclerosis: Revolutionizing Diagnosis, Prognosis, and Therapeutic Approaches

Author

Abulsoud, Ahmed I., Elshaer, Shereen Saeid, Rizk, Nehal I., Khaled, Reem, Abdelfatah, Amr M., Aboelyazed, Ahmed M., Waseem, Aly M., Bashier, Doha, Mohammed, Osama A., Elballal, Mohammed S., Mageed, Sherif S. Abdel, Elrebehy, Mahmoud A., Zaki, Mohamed Bakr, Elesawy, Ahmed E., El-Dakroury, Walaa A., Abdel-Reheim, Mustafa Ahmed, Saber, Sameh, and Doghish, Ahmed S.

Published
2024
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41. An Introduction to Natural Language Processing Techniques and Framework for Clinical Implementation in Radiation Oncology

Author

Khanmohammadi, Reza, Ghassemi, Mohammad M., Verdecchia, Kyle, Ghanem, Ahmed I., Bing, Luo, Chetty, Indrin J., Bagher-Ebadian, Hassan, Siddiqui, Farzan, Elshaikh, Mohamed, Movsas, Benjamin, and Thind, Kundan

Subjects
Computer Science - Computation and Language
Abstract

Natural Language Processing (NLP) is a key technique for developing Medical Artificial Intelligence (AI) systems that leverage Electronic Health Record (EHR) data to build diagnostic and prognostic models. NLP enables the conversion of unstructured clinical text into structured data that can be fed into AI algorithms. The emergence of the transformer architecture and large language models (LLMs) has led to remarkable advances in NLP for various healthcare tasks, such as entity recognition, relation extraction, sentence similarity, text summarization, and question answering. In this article, we review the major technical innovations that underpin modern NLP models and present state-of-the-art NLP applications that employ LLMs in radiation oncology research. However, these LLMs are prone to many errors such as hallucinations, biases, and ethical violations, which necessitate rigorous evaluation and validation before clinical deployment. As such, we propose a comprehensive framework for assessing the NLP models based on their purpose and clinical fit, technical performance, bias and trust, legal and ethical implications, and quality assurance, prior to implementation in clinical radiation oncology. Our article aims to provide guidance and insights for researchers and clinicians who are interested in developing and using NLP models in clinical radiation oncology.

Published
2023

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