Abstract is missing.
- Defining admissible rewards for high-confidence policy evaluation in batch reinforcement learningNiranjani Prasad, Barbara E. Engelhardt, Finale Doshi-Velez. 1-9 [doi]
- Variational learning of individual survival distributionsZidi Xiu, Chenyang Tao, Duke University, Ricardo Henao, Duke University. 10-18 [doi]
- Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelinesChirag Nagpal, Dennis Wei, Bhanukiran Vinzamuri, Monica Shekhar, Sara E. Berger, Subhro Das, Kush R. Varshney. 19-29 [doi]
- Adverse drug reaction discovery from electronic health records with deep neural networksWei Zhang, Zhaobin Kuang, Peggy L. Peissig, David Page. 30-39 [doi]
- CaliForest: calibrated random forest for health dataYubin Park, Joyce C. Ho. 40-50 [doi]
- BMM-Net: automatic segmentation of edema in optical coherence tomography based on boundary detection and multi-scale networkRuru Zhang, Jiawen He, Shenda Shi, Haihong E, Zhonghong Ou, Meina Song. 51-59 [doi]
- Survival cluster analysisPaidamoyo Chapfuwa, Chunyuan Li, Nikhil Mehta, Lawrence Carin, Ricardo Henao. 60-68 [doi]
- An adversarial approach for the robust classification of pneumonia from chest radiographsJoseph D. Janizek, Gabriel G. Erion, Alex J. DeGrave, Su-In Lee. 69-79 [doi]
- Explaining an increase in predicted risk for clinical alertsMichaela Hardt, Alvin Rajkomar, Gerardo Flores, Andrew M. Dai, Michael Howell, Greg Corrado, Claire Cui, Moritz Hardt. 80-89 [doi]
- Fast learning-based registration of sparse 3D clinical imagesKathleen M. Lewis, Natalia S. Rost, John V. Guttag, Adrian V. Dalca. 90-98 [doi]
- Multiple instance learning for predicting necrotizing enterocolitis in premature infants using microbiome dataThomas Hooven, Yun Chao Lin, Ansaf Salleb-Aouissi. 99-109 [doi]
- Hurtful words: quantifying biases in clinical contextual word embeddingsHaoran Zhang, Amy X. Lu, Mohamed Abdalla, Matthew B. A. McDermott, Marzyeh Ghassemi. 110-120 [doi]
- Disease state prediction from single-cell data using graph attention networksNeal G. Ravindra, Arijit Sehanobish, Jenna L. Pappalardo, David A. Hafler, David van Dijk. 121-130 [doi]
- Using SNOMED to automate clinical concept mappingShaun Gupta, Frederik Dieleman, Patrick Long, Orla M. Doyle, Nadejda Leavitt. 131-138 [doi]
- MMiDaS-AE: multi-modal missing data aware stacked autoencoder for biomedical abstract screeningEric W. Lee, Byron C. Wallace, Karla I. Galaviz, Joyce C. Ho. 139-150 [doi]
- Hidden stratification causes clinically meaningful failures in machine learning for medical imagingLuke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro, Christopher Ré. 151-159 [doi]
- Interactive hybrid approach to combine machine and human intelligence for personalized rehabilitation assessmentMin Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia. 160-169 [doi]
- Extracting medical entities from social mediaSanja Scepanovic, Enrique Martin-Lopez, Daniele Quercia, Khan Baykaner. 170-181 [doi]
- Population-aware hierarchical bayesian domain adaptation via multi-component invariant learningVishwali Mhasawade, Nabeel Abdur Rehman, Rumi Chunara. 182-192 [doi]
- TASTE: temporal and static tensor factorization for phenotyping electronic health recordsArdavan Afshar, Ioakeim Perros, Haesun Park, Christopher deFilippi, Xiaowei Yan, Walter F. Stewart, Joyce C. Ho, Jimeng Sun. 193-203 [doi]
- Analyzing the role of model uncertainty for electronic health recordsMichael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine A. Heller, Andrew M. Dai. 204-213 [doi]
- Deidentification of free-text medical records using pre-trained bidirectional transformersAlistair E. W. Johnson, Lucas Bulgarelli, Tom J. Pollard. 214-221 [doi]
- MIMIC-Extract: a data extraction, preprocessing, and representation pipeline for MIMIC-IIIShirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Marzyeh Ghassemi, Michael C. Hughes, Tristan Naumann. 222-235 [doi]