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Showing 1 to 12 of 12 for “"MIMIC-III"”.
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Structural Robustness of Transformer Models for Clinical Text Summarization on MIMIC-III
… no formatting, temporal shuffling) on 1,000 MIMIC-III discharge summaries under de-identified and re-identified conditions, producing 40,000 summaries evaluated against Llama-3 70B silver-standard references. Results reveal widespread fragility: 42/60 perturbation tests and 18/20 …
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I. Molecular Probes for the Progesterone Receptor. II. Design and Synthesis of a Rigidly Constrained Peptidomimetic as a Potential Beta-Turn Mimic. III. Isolation and Characterization of Novel Ion Channel Modulators From Commercial Phenol Red Preparations
Chemical probes for steroid receptors have proven useful in providing molecular details about important hormone-receptor interactions. $16\alpha,17\alpha$- ((R)-1$\sp\prime$-(4-Azidophenyl)-ethylidenedioxy) pregn-4-ene-3,20-dione (12) was prepared in high specific activity tritium-labeled form (20 …
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On Critical Care Data and Machine Learning Loss Function Landscapes
… used to evaluate these models. Two databases, MIMIC III and Amsterdam UMC db, are compared. There are many possible calculations to perform, and initially time-series for single variables and pairs of variables are used as inputs to the neural network. From MIMIC III, Glasgow Coma Scale (GCS) …
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A system approach to augment clinical decision-making using machine learning
… rate prediction) from a particular source (the MIMIC III data base). The results will help define current limits on augmenting clinical decisions and establish direction for future work including more demanding experiments.
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Learning to Ask Like a Physician
… are generated by medical experts from 100+ MIMIC-III discharge summaries. We analyze this dataset to characterize the types of information sought by medical experts. We also train baseline models for trigger detection and question generation (QG), paired with unsupervised answer retrieval …
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Hyperparameters and neural architectures in differentially private deep learning
… main tradeoff in DP, for models trained on the MIMIC-III dataset. The analyzed hyperparameters are the noise multiplier, clipping bound, and batch size. The experiments examine neural architecture changes regarding the depth and width of the model, activation functions, and group normalization. …
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Understanding Clinical Pain Management and Patient Experiences of Pain from Electronic Health Records
… analyze data from two distinct populations, the MIMIC III ICU dataset and records from general medical services at Brigham and Women’s Hospital (BWH). This work is undertaken in collaboration with providers at BWH in Boston. To help quantify and standardize patients’ experiences of pain, we may …
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Addressing Deficiencies from Missing Data in Electronic Health Records
… namely, the masked value regression task, which mimics missing data situations at test time and optimizes a supervised regression loss in each learning episode. Additionally, an adversarial training procedure is employed to further improve the proposed system, similarly as the conditional …
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Personalized and Communication Cost Reduction Models in Federated Learning
… has been proved using the LSTM model on the MIMIC III dataset with series of medical records of different patients. Experiments and results show that the PFL model provides better results in terms of MSE, MAPE, and SMAPE evaluation metrics compared to the conventional federated learning …
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Applying Language Models To Patient Health Records: Acronym Expansion, Long Document Classification and Explainable Predictions
… achieving an average accuracy of 63\% on Mimic-III clinical notes. We dramatically reduced the cost for clinical annotations of these acronyms. The second contribution is the development of a novel deep learning architecture that leverages structured EHR data (e.g., demographics, billing …
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Risk assessment and mortality prediction in patients with venous thromboembolism using big data and machine learning.
… For that reason, a freely accessible database MIMIC-III has been used that contains a vast amount of various time-series healthcare data from thousands of patients, making it ideal for ML based forecasting. Since it provides information even after discharge from ICU, it gives an opportunity to …