Conference proceeding
Recent Temporal Pattern Mining for Septic Shock Early Prediction
2018 IEEE International Conference on Healthcare Informatics (ICHI), pp 229-240
Jun 2018
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Sepsis is a leading cause of in-hospital death over the world and septic shock, the most severe complication of sepsis, reaches a mortality rate as high as 50%. Early diagnosis and treatment can prevent most morbidity and mortality. In this work, Recent Temporal Patterns (RTPs) are used in conjunction with SVM classifier to build a robust yet interpretable model for early diagnosis of septic shock. This model is applied to two different prediction tasks: visit-level early diagnosis and event-level early prediction. For each setting, this model is compared against several strong baselines including atemporal method called Last-Value, six classic machine learning algorithms, and lastly, a state-of-the-art deep learning model: Long Short-Term Memory (LSTM). Our results suggest that RTP-based model can outperform all aforementioned baseline models for both diagnosis tasks. More importantly, the extracted interpretative RTPs can shed lights for the clinicians to discover progression behavior and latent patterns among septic shock patients.
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Details
- Title
- Recent Temporal Pattern Mining for Septic Shock Early Prediction
- Creators
- Farzaneh Khoshnevisan - North Carolina State UniversityJulie Ivy - North Carolina State UniversityMuge Capan - North Carolina State UniversityRyan Arnold - Drexel UniversityJeanne Huddleston - Drexel UniversityMin Chi - North Carolina State University
- Publication Details
- 2018 IEEE International Conference on Healthcare Informatics (ICHI), pp 229-240
- Conference
- 2018 IEEE International Conference on Healthcare Informatics (ICHI)
- Publisher
- IEEE
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000853207500026
- Scopus ID
- 2-s2.0-85051140346
- Other Identifier
- 991019173426004721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Health Care Sciences & Services
- Medical Informatics