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pro vyhledávání: '"Fritz, Bradley"'
Autor:
Liu, Hao, Zhang, Muhan, Dong, Zehao, Kong, Lecheng, Chen, Yixin, Fritz, Bradley, Tao, Dacheng, King, Christopher
Rich Electronic Health Records (EHR), have created opportunities to improve clinical processes using machine learning methods. Prediction of the same patient events at different time horizons can have very different applications and interpretations;
Externí odkaz:
http://arxiv.org/abs/2303.02570
Learning to represent free text is a core task in many clinical machine learning (ML) applications, as clinical text contains observations and plans not otherwise available for inference. State-of-the-art methods use large language models developed w
Externí odkaz:
http://arxiv.org/abs/2301.11608
Although prediction models for delirium, a commonly occurring condition during general hospitalization or post-surgery, have not gained huge popularity, their algorithmic bias evaluation is crucial due to the existing association between social deter
Externí odkaz:
http://arxiv.org/abs/2211.04442
Autor:
Li, Dingwen, Xue, Bing, King, Christopher, Fritz, Bradley, Avidan, Michael, Abraham, Joanna, Lu, Chenyang
Major postoperative complications are devastating to surgical patients. Some of these complications are potentially preventable via early predictions based on intraoperative data. However, intraoperative data comprise long and fine-grained multivaria
Externí odkaz:
http://arxiv.org/abs/2210.04417
Autor:
Tripathi, Sandhya, Fritz, Bradley A., Abdelhack, Mohamed, Avidan, Michael S., Chen, Yixin, King, Christopher R.
An applied problem facing all areas of data science is harmonizing data sources. Joining data from multiple origins with unmapped and only partially overlapping features is a prerequisite to developing and testing robust, generalizable algorithms, es
Externí odkaz:
http://arxiv.org/abs/2207.03536
Autor:
Abdelhack, Mohamed, Zhang, Jiaming, Tripathi, Sandhya, Fritz, Bradley A, Felsky, Daniel, Avidan, Michael S, Chen, Yixin, King, Christopher R
Publikováno v:
Transactions on Machine Learning Research 2023
Data missingness and quality are common problems in machine learning, especially for high-stakes applications such as healthcare. Developers often train machine learning models on carefully curated datasets using only high quality data; however, this
Externí odkaz:
http://arxiv.org/abs/2107.08574
Autor:
Tripathi, Sandhya, Fritz, Bradley A., Abdelhack, Mohamed, Avidan, Michael S., Chen, Yixin, King, Christopher R.
Publikováno v:
In Journal of Biomedical Informatics March 2024 151
Ketamine for postoperative avoidance of depressive symptoms: the K-PASS feasibility randomised trial
Autor:
Fritz, Bradley A., Tellor Pennington, Bethany R., Dalton, Catherine, Horan, Christine, Palanca, Ben J.A., Schweiger, Julie A., Griffin, Logan, Tumwesige, Wilberforce, Willie, Jon T., Farber, Nuri B.
Publikováno v:
In BJA Open March 2024 9
Autor:
Tripathi, Sandhya, Fritz, Bradley A., Abdelhack, Mohamed, Avidan, Michael S., Chen, Yixin, King, Christopher R.
With the current ongoing debate about fairness, explainability and transparency of machine learning models, their application in high-impact clinical decision-making systems must be scrutinized. We consider a real-life example of risk estimation befo
Externí odkaz:
http://arxiv.org/abs/2011.02036
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