Article... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics2019
Feature Exploration and Causal Inference on Mortality of Epilepsy Patients Using Insurance Claims Data.
Article in ... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Proposing Causal Sequence of Death by Neural Machine Translation in Public Health Informatics.IEEE journal of biomedical and health informatics · 2022Article
- Regularization of Deep Neural Networks for EEG Seizure Detection to Mitigate Overfitting.Proceedings : Annual International Computer Software and Applications Conference. COMPSAC · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Approximately 0.5-1% of the global population is afflicted with epilepsy, a neurological disorder characterized by repeated seizures. Sudden Unexpected Death in Epilepsy (SUDEP) is a poorly understood complication that claims the lives of nearly 1-in-1000 epilepsy patients every year. This paper aims to explore diagnosis codes, demographic and payment features on mortality of epilepsy patients. We design a mortality prediction model with diagnosis codes and non-diagnosis features extracted from US commercial insurance claims data. We present classification accuracy of 0.91 and 0.85 by using different feature vectors. After analyzing the aforementioned features in prediction model, we extend the work to causal inference between modified diagnosis codes and selected non-diagnosis features. The uplift test of causal inference using three algorithms indicates that a patient is more likely to survive if upgrading from a low-coverage healthcare plan into a high-coverage plan.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.