MedGen: A Python Natural Language Processing Toolkit for Medical Text Processing

This study introduces MedGen, a comprehensive natural language processing (NLP) toolkit designed for medical text processing. MedGen is tailored for biomedical researchers and healthcare professionals with an easy-to-use, all-in-one solution that requires minimal programming expertise. It includes (1) Generative Functions: For the first time, MedGen includes four advanced generative functions: question answering, text summarization, text simplification, and machine translation; (2) Basic NLP Functions: MedGen integrates 12 essential NLP functions such as word tokenization and sentence segmentation; and (3) Query and Search Capabilities: MedGen provides user-friendly query and search functions on text corpora. We fine-tuned 32 domain-specific language models, evaluated them thoroughly on 24 established benchmarks and conducted manual reviews with clinicians. Additionally, we expanded our toolkit by introducing query and search functions, while also standardizing and integrating functions from third-party libraries. The toolkit, its models, and associated data are publicly available via https://github.com/Yale-LILY/MedGen.

Multi-modal, multi-task, multi-attention (M3) deep learning detection of reticular pseudodrusen: towards automated and accessible classification of age-related macular degeneration

Objective Reticular pseudodrusen (RPD), a key feature of age-related macular degeneration (AMD), are poorly detected by human experts on standard color fundus photography (CFP) and typically require advanced imaging modalities such as fundus autofluorescence (FAF). The objective was to develop and evaluate the performance of a novel 'M3' deep learning framework on RPD detection. Materials and Methods A deep learning framework M3 was developed to detect RPD presence accurately using CFP alone, FAF alone, or both, employing >8000 CFP-FAF image pairs obtained prospectively (Age-Related Eye Disease Study 2). The M3 framework includes multi-modal (detection from single or multiple image modalities), multi-task (training different tasks simultaneously to improve generalizability), and multi-attention (improving ensembled feature representation) operation. Performance on RPD detection was compared with state-of-the-art deep learning models and 13 ophthalmologists; performance on detection of two other AMD features (geographic atrophy and pigmentary abnormalities) was also evaluated. Results For RPD detection, M3 achieved area under receiver operating characteristic (AUROC) 0.832, 0.931, and 0.933 for CFP alone, FAF alone, and both, respectively. M3 performance on CFP was very substantially superior to human retinal specialists (median F1-score 0.644 versus 0.350). External validation (on Rotterdam Study, Netherlands) demonstrated high accuracy on CFP alone (AUROC 0.965). The M3 framework also accurately detected geographic atrophy and pigmentary abnormalities (AUROC 0.909 and 0.912, respectively), demonstrating its generalizability. Conclusion This study demonstrates the successful development, robust evaluation, and external validation of a novel deep learning framework that enables accessible, accurate, and automated AMD diagnosis and prognosis.