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Large language models (LLMs) have emerged as promising tools for assisting in medical tasks, yet processing Electronic Health Records (EHRs) presents unique challenges due to their longitudinal nature.
Rouge: A package for automatic evaluation of summaries
Lin, C.-Y · 2004
Earlier work this paper cites.
Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Banerjee, S. and Lavie, A · 2005
Earlier work this paper cites.
chrf: character n-gram f-score for automatic mt evaluation
Popović, M · 2015
Earlier work this paper cites.
Bertscore: Evaluating text generation with bert
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., and Artzi, Y · 2019
Earlier work this paper cites.
Learning from longitudinal data in electronic health record and genetic data to improve cardiovascular event prediction
Zhao, J., Feng, Q., Wu, P., Lupu, R. A., Wilke, R. A., Wells, Q. S., Denny, J. C., and Wei, W.-Q · 2019
Earlier work this paper cites.
Using electronic health records in longitudinal studies: estimating patient attrition
Huguet, N., Kaufmann, J., O’Malley, J., Angier, H., Hoopes, M., DeVoe, J. E., and Marino, M · 2020
Earlier work this paper cites.
Analyzing patient trajectories with artificial intelligence
Allam, A., Feuerriegel, S., Rebhan, M., and Krauthammer, M · 2021
Earlier work this paper cites.
Condensed trajectory of the temporal correlation of diseases and mortality extracted from over 300,000 patients in hospitals
Paik, H. and Kim, J · 2021
Earlier work this paper cites.
LoRA: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
Earlier work this paper cites.
Temporal patient trajectories: long stories in short admissions
Scheller, V. K · 2022
Earlier work this paper cites.
Prediction models using artificial intelligence and longitudinal data from electronic health records: a systematic methodological review
Carrasco-Ribelles, L. A., Llanes-Jurado, J., Gallego-Moll, C., Cabrera-Bean, M., Monteagudo-Zaragoza, M., Violán, C., and Zabaleta-del Olmo, E · 2023
Earlier work this paper cites.
Meditron-70b: Scaling medical pretraining for large language models, 2023
Chen, Z., Cano, A. H., Romanou, A., Bonnet, A., Matoba, K., Salvi, F., Pagliardini, M., Fan, S., Köpf, A., Mohtashami, A., Sallinen, A., Sakhaeirad, A., Swamy, V., Krawczuk, I., Bayazit, D., Marmet, A., Montariol, S., Hartley, M.-A., Jaggi, M., and Bosselut, A · 2023
Earlier work this paper cites.
Medalpaca–an open-source collection of medical conversational ai models and training data
Han, T., Adams, L. C., Papaioannou, J.-M., Grundmann, P., Oberhauser, T., Löser, A., Truhn, D., and Bressem, K. K · 2023
Earlier work this paper cites.
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al · 2023
Earlier work this paper cites.
Evaluating large language models on medical evidence summarization
Tang, L., Sun, Z., Idnay, B., Nestor, J. G., Soroush, A., Elias, P. A., Xu, Z., Ding, Y., Durrett, G., Rousseau, J. F., et al · 2023
Cited alongside, same era.
Synthesize high-dimensional longitudinal electronic health records via hierarchical autoregressive language model
Theodorou, B., Xiao, C., and Sun, J · 2023
Cited alongside, same era.
Self-instruct: Aligning language models with self-generated instructions
Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., and Hajishirzi, H · 2023
Cited alongside, same era.
Testing and evaluation of health care applications of large language models: a systematic review
Bedi, S., Liu, Y., Orr-Ewing, L., Dash, D., Koyejo, S., Callahan, A., Fries, J. A., Wornow, M., Swaminathan, A., Lehmann, L. S., et al · 2024
Cited alongside, same era.
MedPaLM 2 for Healthcare: Model Reference
Cloud, G · 2024
Cited alongside, same era.
Babilong: Testing the limits of llms with long context reasoning-in-a-haystack
Kuratov, Y., Bulatov, A., Anokhin, P., Rodkin, I., Sorokin, D., Sorokin, A., and Burtsev, M · 2024
Later among the works it cites.
Long-context llms struggle with long in-context learning
Li, T., Zhang, G., Do, Q. D., Yue, X., and Chen, W · 2024
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Llama-3.1-8b-instruct
Llama, M · 2024
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Large language models in biomedicine and health: current research landscape and future directions
Lu, Z., Peng, Y., Cohen, T., Ghassemi, M., Weng, C., and Tian, S · 2024
Later among the works it cites.
Reasoning with large language models for medical question answering
Lucas, M. M., Yang, J., Pomeroy, J. K., and Yang, C. C · 2024
Later among the works it cites.
Towards building multilingual language model for medicine, 2024
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Biomedical visual instruction tuning with clinician preference alignment
Cui, H., Mao, L., Liang, X., Zhang, J., Ren, H., Li, Q., Li, X., and Yang, C · 2024
Cited alongside, same era.
Alpacafarm: A simulation framework for methods that learn from human feedback
Dubois, Y., Li, C. X., Taori, R., Zhang, T., Gulrajani, I., Ba, J., Guestrin, C., Liang, P. S., and Hashimoto, T. B · 2024
Cited alongside, same era.
Test of time: A benchmark for evaluating llms on temporal reasoning
Fatemi, B., Kazemi, M., Tsitsulin, A., Malkan, K., Yim, J., Palowitch, J., Seo, S., Halcrow, J., and Perozzi, B · 2024
Cited alongside, same era.
Medalign: A clinician-generated dataset for instruction following with electronic medical records
Fleming, S. L., Lozano, A., Haberkorn, W. J., Jindal, J. A., Reis, E., Thapa, R., Blankemeier, L., Genkins, J. Z., Steinberg, E., Nayak, A., et al · 2024
Cited alongside, same era.
Scaling synthetic data creation with 1,000,000,000 personas
Ge, T., Chan, X., Wang, X., Yu, D., Mi, H., and Yu, D · 2024
Cited alongside, same era.
Influence of a large language model on diagnostic reasoning: A randomized clinical vignette study
Goh, E., Gallo, R., Hom, J., Strong, E., Weng, Y., Kerman, H., Cool, J., Kanjee, Z., Parsons, A. S., Ahuja, N., et al · 2024
Cited alongside, same era.
Evaluating and mitigating limitations of large language models in clinical decision making
Hager, P., Jungmann, F., Bhagat, K., Hubrecht, I., Knauer, M., Vielhauer, J., Holland, R., Braren, R., Makowski, M., Kaisis, G., et al · 2024
Cited alongside, same era.
Qiu, P., Wu, C., Zhang, X., Lin, W., Wang, H., Zhang, Y., Wang, Y., and Xie, W · 2024
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Exploring the effectiveness of instruction tuning in biomedical language processing
Rohanian, O., Nouriborji, M., Kouchaki, S., Nooralahzadeh, F., Clifton, L., and Clifton, D. A · 2024
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Evaluation framework of large language models in medical documentation: Development and usability study
Seo, J., Choi, D., Kim, T., Cha, W. C., Kim, M., Yoo, H., Oh, N., Yi, Y., Lee, K. H., and Choi, E · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Team, G., Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al · 2024
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Bioinstruct: instruction tuning of large language models for biomedical natural language processing
Tran, H., Yang, Z., Yao, Z., and Yu, H · 2024
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Adapted large language models can outperform medical experts in clinical text summarization
Van Veen, D., Van Uden, C., Blankemeier, L., Delbrouck, J.-B., Aali, A., Bluethgen, C., Pareek, A., Polacin, M., Reis, E. P., Seehofnerová, A., et al · 2024
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Tram: Benchmarking temporal reasoning for large language models
Wang, Y. and Zhao, Y · 2024
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Context clues: Evaluating long context models for clinical prediction tasks on ehrs
Wornow, M., Bedi, S., Hernandez, M. A. F., Steinberg, E., Fries, J. A., Ré, C., Koyejo, S., and Shah, N. H · 2024
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Zhu, Y., Wang, Z., Gao, J., Tong, Y., An, J., Liao, W., Harrison, E. M., Ma, L., and Pan, C · 2024
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Qwen2.5: A party of foundation models, 2025
Qwen, :, Yang, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu, B., Li, C., Liu, D., Huang, F., Wei, H., Lin, H., Yang, J., Tu, J., Zhang, J., Yang, J., Yang, J., Zhou, J., Lin, J., Dang, K., Lu, K., Bao, K., Yang, K., Yu, L., Li, M., Xue, M., Zhang, P., Zhu, Q., Men, R., Lin, R., Li, T., Tang, T., Xia, T., Ren, X., Ren, X., Fan, Y., Su, Y., Zhang, Y., Wan, Y., Liu, Y., Cui, Z., Zhang, Z., and Qiu, Z · 2025
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