{"list":"other","total":51,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1610.02391","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","created":"2016-10-07","cited_by_count":6733},{"id":"1409.0473","title":"Neural Machine Translation by Jointly Learning to Align and Translate","created":"2014-09-01","cited_by_count":4548},{"id":"1310.4546","title":"Distributed Representations of Words and Phrases and their Compositionality","created":"2013-10-16","cited_by_count":3474},{"id":"2105.01601","title":"MLP-Mixer: An all-MLP Architecture for Vision","created":"2021-05-04","cited_by_count":1446},{"id":"1802.07569","title":"Continual Lifelong Learning with Neural Networks: A Review","created":"2018-02-21","cited_by_count":1330},{"id":"1508.04025","title":"Effective Approaches to Attention-based Neural Machine Translation","created":"2015-08-17","cited_by_count":1026},{"id":"1606.03490","title":"The Mythos of Model Interpretability","created":"2016-06-10","cited_by_count":721},{"id":"1606.01865","title":"Recurrent Neural Networks for Multivariate Time Series with Missing Values","created":"2016-06-06","cited_by_count":554},{"id":"1312.6184","title":"Do Deep Nets Really Need to be Deep?","created":"2013-12-21","cited_by_count":474},{"id":"2103.11251","title":"Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges","created":"2021-03-20","cited_by_count":367},{"id":"1608.05745","title":"RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism","created":"2016-08-19","cited_by_count":349},{"id":"1703.07771","title":"Multitask learning and benchmarking with clinical time series data","created":"2017-03-22","cited_by_count":290},{"id":"1801.07860","title":"Scalable and accurate deep learning for electronic health records","created":"2018-01-24","cited_by_count":280},{"id":"1907.09538","title":"BEHRT: Transformer for Electronic Health Records","created":"2019-07-22","cited_by_count":263},{"id":"1511.05942","title":"Doctor AI: Predicting Clinical Events via Recurrent Neural Networks","created":"2015-11-18","cited_by_count":208},{"id":"1909.00590","title":"Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions","created":"2019-09-02","cited_by_count":172},{"id":"1706.03446","title":"Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis","created":"2017-06-12","cited_by_count":148},{"id":"1907.09294","title":"The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations","created":"2019-07-22","cited_by_count":97},{"id":"1502.04269","title":"Supersparse Linear Integer Models for Optimized Medical Scoring Systems","created":"2015-02-15","cited_by_count":76},{"id":"1810.09593","title":"MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare","created":"2018-10-22","cited_by_count":72},{"id":"1602.05568","title":"Multi-layer Representation Learning for Medical Concepts","created":"2016-02-17","cited_by_count":69},{"id":"1805.10724","title":"RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records","created":"2018-05-28","cited_by_count":61},{"id":"1607.07519","title":"Deepr: A Convolutional Net for Medical Records","created":"2016-07-26","cited_by_count":60},{"id":"2001.10054","title":"StageNet: Stage-Aware Neural Networks for Health Risk Prediction","created":"2020-01-24","cited_by_count":48},{"id":"1610.00168","title":"Learning Optimized Risk Scores","created":"2016-10-01","cited_by_count":47},{"id":"2010.02809","title":"Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review","created":"2020-10-06","cited_by_count":47},{"id":"2003.06516","title":"Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale","created":"2020-03-14","cited_by_count":39},{"id":"2006.08765","title":"COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching","created":"2020-06-15","cited_by_count":37},{"id":"1709.01648","title":"Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records","created":"2017-09-06","cited_by_count":36},{"id":"1905.03218","title":"MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records","created":"2019-05-08","cited_by_count":33},{"id":"1810.04793","title":"Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record","created":"2018-10-10","cited_by_count":30},{"id":"1802.00662","title":"Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning","created":"2018-02-02","cited_by_count":27},{"id":"1806.02878","title":"Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU","created":"2018-06-07","cited_by_count":22},{"id":"1905.08547","title":"Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk","created":"2019-05-21","cited_by_count":17},{"id":"1906.02956","title":"Early detection of sepsis utilizing deep learning on electronic health record event sequences","created":"2019-06-07","cited_by_count":15},{"id":"2003.12012","title":"TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications","created":"2020-03-24","cited_by_count":13},{"id":"1902.03376","title":"Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding","created":"2019-02-09","cited_by_count":12},{"id":"1911.05843","title":"TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records","created":"2019-11-13","cited_by_count":11},{"id":"1909.06886","title":"Temporal Self-Attention Network for Medical Concept Embedding","created":"2019-09-15","cited_by_count":9},{"id":"2006.15940","title":"Adversarial Multi-Source Transfer Learning in Healthcare: Application to Glucose Prediction for Diabetic People","created":"2020-06-29","cited_by_count":9},{"id":"2106.06957","title":"AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data","created":"2021-06-13","cited_by_count":7},{"id":"2107.06039","title":"AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data","created":"2021-07-13","cited_by_count":7},{"id":"1903.08652","title":"Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction","created":"2019-03-20","cited_by_count":5},{"id":"1811.02218","title":"CarePre: An Intelligent Clinical Decision Assistance System","created":"2018-11-06","cited_by_count":4},{"id":"2001.10065","title":"Medi-Care AI: Predicting Medications From Billing Codes via Robust Recurrent Neural Networks","created":"2019-11-14","cited_by_count":4},{"id":"2003.00655","title":"Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction","created":"2020-03-02","cited_by_count":4},{"id":"1910.06792","title":"Early Prediction of Sepsis From Clinical Datavia Heterogeneous Event Aggregation","created":"2019-10-14","cited_by_count":2},{"id":"1911.05698","title":"Predictive Multi-level Patient Representations from Electronic Health Records","created":"2019-11-12","cited_by_count":2},{"id":"1711.06516","title":"Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks","created":"2017-11-17","cited_by_count":1},{"id":"1802.00948","title":"Resset: A Recurrent Model for Sequence of Sets with Applications to Electronic Medical Records","created":"2018-02-03","cited_by_count":1}],"next_cursor":null}