Papers with survival analysis
SMARTR: A Framework for Early Detection using Survival Analysis of Longitudinal Texts (2024.naacl-srw)
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| Challenge: | a paper aims to detect expensive insurance claims early using textual information from claims notes. |
| Approach: | They propose a model that leverages survival analysis concepts from claims notes to enhance a posteriori classification and early detection. |
| Outcome: | The proposed model improves classification and early detection without reducing performance . it is based on a privately held corpus of claim files from a Canadian insurer . |
The structure of online social networks modulates the rate of lexical change (2021.naacl-main)
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| Challenge: | lexical change is a prevalent process, as new words are added, thrive, and decline in day-to-day usage. |
| Approach: | They conduct a large-scale analysis of over 80k neologisms in 4420 online communities over a decade and found that the community’s network structure plays a significant role in lexical change. |
| Outcome: | The results show that the community’s network structure plays a significant role in lexical change. |
Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient Mortality (2021.naacl-main)
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| Challenge: | Current models for survival analysis are limited in scope and require a large amount of data and expert annotations for training. |
| Approach: | They propose to use BERT-based hidden layer representations of clinical texts as covariates for proportional hazards models to predict patient survival outcomes. |
| Outcome: | The proposed method outperforms the baseline model by 5.7% across C-index and time-dependent AUC. |
Leveraging Information Redundancy of Real-World Data through Distant Supervision (2024.lrec-main)
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| Challenge: | Existing methods for annotation of health care notes are promising but they are limited due to privacy regulations. |
| Approach: | They propose a text labeling method that leverages the redundancy of temporal information in a data lake to create a large programmatically annotated corpus and train transformer models using distant supervision. |
| Outcome: | The proposed method reduces expert annotation time, a scarce and expensive resource. |