arXiv subjects

Mojdeh Rahmanian

Publications and source records attributed to Mojdeh Rahmanian.

3 recordsLinked to original sources

arXiv2026

Large Language Models for Software Engineering Diagrams: A Systematic Review of UML and ER modelling

Large language models (LLMs) are increasingly applied to diagram-based software and data modelling. Among various modelling notations, UML and entity-relationship (ER) diagrams are the most widely adopted for software modelling and data modelling, respectively. Recent literature has investigated various applications of LLMs in diagram modelling; however, their effectiveness and limitations have not been extensively discussed. This systematic literature review analyses 64 studies published between 2023 and 2025, examining diagram coverage, modelling tasks, technical approaches, evaluation practices, and limitations. Our findings reveal significant concentration patterns and gaps. UML-based software modelling strongly dominates, with class diagrams receiving the most attention whilst behavioural diagrams and data modelling remain underrepresented. Diagram construction from natural language is the primary focus, with limited work on transformation, quality assurance, and consistency checking. GPT-based models are heavily prevalent, raising concerns about reproducibility and vendor dependence. Evaluation practices are heterogeneous, employing diverse metrics and custom datasets with limited benchmark reuse and inconsistent reporting of robustness and statistical significance. Common limitations include semantic inaccuracies, hallucinated diagram elements, sensitivity to prompt formulation, and reproducibility constraints. This survey provides the first systematic synthesis of LLM-based diagram modelling research, highlighting needs for standardised benchmarks, stronger evaluation protocols, broader diagram coverage, and techniques for improving semantic reliability and multi-view consistency.

cs.SE↗

arXiv2024

Towards Efficient Patient Recruitment for Clinical Trials: Application of a Prompt-Based Learning Model

Objective: Clinical trials are essential for advancing pharmaceutical interventions, but they face a bottleneck in selecting eligible participants. Although leveraging electronic health records (EHR) for recruitment has gained popularity, the complex nature of unstructured medical texts presents challenges in efficiently identifying participants. Natural Language Processing (NLP) techniques have emerged as a solution with a recent focus on transformer models. In this study, we aimed to evaluate the performance of a prompt-based large language model for the cohort selection task from unstructured medical notes collected in the EHR. Methods: To process the medical records, we selected the most related sentences of the records to the eligibility criteria needed for the trial. The SNOMED CT concepts related to each eligibility criterion were collected. Medical records were also annotated with MedCAT based on the SNOMED CT ontology. Annotated sentences including concepts matched with the criteria-relevant terms were extracted. A prompt-based large language model (Generative Pre-trained Transformer (GPT) in this study) was then used with the extracted sentences as the training set. To assess its effectiveness, we evaluated the model's performance using the dataset from the 2018 n2c2 challenge, which aimed to classify medical records of 311 patients based on 13 eligibility criteria through NLP techniques. Results: Our proposed model showed the overall micro and macro F measures of 0.9061 and 0.8060 which were among the highest scores achieved by the experiments performed with this dataset. Conclusion: The application of a prompt-based large language model in this study to classify patients based on eligibility criteria received promising scores. Besides, we proposed a method of extractive summarization with the aid of SNOMED CT ontology that can be also applied to other medical texts.

cs.CL↗

arXiv2022

Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique

Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further analysis is difficult. In these circumstances, natural language processing (NLP) techniques can facilitate automatic information extraction and transformation of free-text formats to structured data. In recent years, deep learning (DL)-based models have been adapted for NLP experiments with promising results. Despite the significant potential of DL models based on artificial neural networks (ANN) and convolutional neural networks (CNN), the models face some limitations to implement in clinical practice. Transformers, another new DL architecture, have been increasingly applied to improve the process. Therefore, in this study, we propose a transformer-based fine-grained named entity recognition (NER) architecture for clinical information extraction. We collected 88 abdominopelvic sonography reports in free-text formats and annotated them based on our developed information schema. The text-to-text transfer transformer model (T5) and Scifive, a pre-trained domain-specific adaptation of the T5 model, were applied for fine-tuning to extract entities and relations and transform the input into a structured format. Our transformer-based model in this study outperformed previously applied approaches such as ANN and CNN models based on ROUGE-1, ROUGE-2, ROUGE-L, and BLEU scores of 0.816, 0.668, 0.528, and 0.743, respectively, while providing an interpretable structured report.

cs.CL↗