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In the emergency department (ED), patients undergo triage and multiple laboratory tests before diagnosis.
Random forests
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The effect of laboratory testing on emergency department length of stay: a multihospital longitudinal study applying a cross-classified random-effect modeling approach
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Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
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Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index
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Proximal policy optimization algorithms, 2017
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Stop the Bottleneck: Improving Patient Throughput in the Emergency Department
DeAnda, R · 2018
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The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care
Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C., and Faisal, A. A · 2018
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Benchmarking deep learning models on large healthcare datasets
Purushotham, S., Meng, C., Che, Z., and Liu, Y · 2018
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
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Predicting 30-day mortality of patients with pneumonia in an emergency department setting using machine-learning models
Kang, S. Y., Cha, W. C., Yoo, J., Kim, T., Park, J. H., Yoon, H., Hwang, S. Y., Sim, M. S., Jo, I. J., and Shin, T. G · 2020
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MIMIC-Extract: a data extraction, preprocessing, and representation pipeline for MIMIC-III
Wang, S., McDermott, M. B. A., Chauhan, G., Ghassemi, M., Hughes, M. C., and Naumann, T · 2020
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Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., and Dormann, N · 2021
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Predicting progression to septic shock in the emergency department using an externally generalizable machine-learning algorithm
Wardi, G., Carlile, M., Holder, A., Shashikumar, S., Hayden, S. R., and Nemati, S · 2021
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Fast interpretable greedy-tree sums (FIGS)
Tan, Y. S., Singh, C., Nasseri, K., Agarwal, A., and Yu, B · 2022
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Benchmarking emergency department prediction models with machine learning and public electronic health records
Xie, F., Zhou, J., Lee, J. W., Tan, M., Li, S., Rajnthern, L. S., Chee, M. L., Chakraborty, B., Wong, A.-K. I., Dagan, A., et al · 2022
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A large language model for electronic health records
Yang, X., Chen, A., PourNejatian, N., Shin, H. C., Smith, K. E., Parisien, C., Compas, C., Martin, C., Costa, A. B., Flores, M. G., Zhang, Y., Magoc, T., Harle, C. A., Lipori, G., Mitchell, D. A., Hogan, W. R., Shenkman, E. A., Bian, J., and Wu, Y · 2022
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MDI+: A Flexible Random Forest-Based Feature Importance Framework, 2023
Agarwal, A., Kenney, A. M., Tan, Y. S., Tang, T. M., and Yu, B · 2023
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Pythia: A suite for analyzing large language models across training and scaling
Biderman, S., Schoelkopf, H., Anthony, Q. G., Bradley, H., O’Brien, K., Hallahan, E., Khan, M. A., Purohit, S., Prashanth, U. S., Raff, E., et al · 2023
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Clinical Relation Extraction Using Transformer-based Models, 2021
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Agarwal, A., Tan, Y. S., Ronen, O., Singh, C., and Yu, B · 2022
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Predictability and stability testing to assess clinical decision instrument performance for children after blunt torso trauma
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BioGPT: generative pre-trained transformer for biomedical text generation and mining
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Overcrowding in emergency department: Causes, consequences, and solutions-a narrative review
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Emergency department overcrowding: Understanding the factors to find corresponding solutions
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Tabllm: Few-shot classification of tabular data with large language models
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Language models are weak learners
Manikandan, H., Jiang, Y., and Kolter, J. Z · 2023
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Towards Expert-Level Medical Question Answering with Large Language Models, 2023
Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, K., Pfohl, S., Cole-Lewis, H., Neal, D., Schaekermann, M., Wang, A., Amin, M., Lachgar, S., Mansfield, P., Prakash, S., Green, B., Dominowska, E., y Arcas, B. A., Tomasev, N., Liu, Y., Wong, R., Semturs, C., Mahdavi, S. S., Barral, J., Webster, D., Corrado, G. S., Matias, Y., Azizi, S., Karthikesalingam, A., and Natarajan, V · 2023
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Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
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Deep Reinforcement Learning for Cost-Effective Medical Diagnosis
Yu, Z., Li, Y., Kim, J., Huang, K., Luo, Y., and Wang, M · 2023
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MedLM: Exploring Language Models for Medical Question Answering Systems, 2024
Yagnik, N., Jhaveri, J., Sharma, V., Pila, G., Ben, A., and Shang, J · 2024
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Multitask learning and benchmarking with clinical time series data
Harutyunyan, H., Khachatrian, H., Kale, D. C., Ver Steeg, G., and Galstyan, A · 2052
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