Language models can improve event prediction by few-shot abductive reasoning
Large language models have shown astonishing performance on a wide range of reasoning
tasks. In this paper, we investigate whether they could reason about real-world events and …
tasks. In this paper, we investigate whether they could reason about real-world events and …
Using recurrent neural network models for early detection of heart failure onset
E Choi, A Schuetz, WF Stewart… - Journal of the American …, 2017 - academic.oup.com
Objective: We explored whether use of deep learning to model temporal relations among
events in electronic health records (EHRs) would improve model performance in predicting …
events in electronic health records (EHRs) would improve model performance in predicting …
The neural hawkes process: A neurally self-modulating multivariate point process
Many events occur in the world. Some event types are stochastically excited or inhibited—in
the sense of having their probabilities elevated or decreased—by patterns in the sequence …
the sense of having their probabilities elevated or decreased—by patterns in the sequence …
Doctor ai: Predicting clinical events via recurrent neural networks
E Choi, MT Bahadori, A Schuetz… - Machine learning for …, 2016 - proceedings.mlr.press
Leveraging large historical data in electronic health record (EHR), we developed Doctor AI,
a generic predictive model that covers observed medical conditions and medication uses …
a generic predictive model that covers observed medical conditions and medication uses …
Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks
Predicting the future health information of patients from the historical Electronic Health
Records (EHR) is a core research task in the development of personalized healthcare …
Records (EHR) is a core research task in the development of personalized healthcare …
Self-attentive Hawkes process
Capturing the occurrence dynamics is crucial to predicting which type of events will happen
next and when. A common method to do this is through Hawkes processes. To enhance …
next and when. A common method to do this is through Hawkes processes. To enhance …
Retracted article: LSTM model for prediction of heart failure in big data
G Maragatham, S Devi - Journal of medical systems, 2019 - Springer
The combination of big data and deep learning is a world-shattering technology that can
make a great impact on any industry if used in a proper way. With the availability of large …
make a great impact on any industry if used in a proper way. With the availability of large …
Neural survival recommender
The ability to predict future user activity is invaluable when it comes to content
recommendation and personalization. For instance, knowing when users will return to an …
recommendation and personalization. For instance, knowing when users will return to an …
Predicting the risk of heart failure with EHR sequential data modeling
Electronic health records (EHRs) contain patient diagnostic records, physician records, and
records of hospital departments. For heart failure, we can obtain mass unstructured data …
records of hospital departments. For heart failure, we can obtain mass unstructured data …
Multi-disease prediction using LSTM recurrent neural networks
L Men, N Ilk, X Tang, Y Liu - Expert Systems with Applications, 2021 - Elsevier
Prediction of future clinical events (eg, disease diagnoses) is an important machine learning
task in healthcare informatics research. In this work, we propose a deep learning approach …
task in healthcare informatics research. In this work, we propose a deep learning approach …