Name
Title | ||
|---|---|---|
Semantic-based approach for predicting venous thromboembolism using Kohonen self organized map neural network |
Abstract | ||
|---|---|---|
This paper presents a novel semantic-based approach to analyze clinical documentation in order to predict the first occurrence of symptomless unprovoked venous thrombo-embolism (VTE) in patients. The goal of this work is the attempt to save lives in cases of symptomless unprovoked VTEs that could be fatal from first occurrence. Using the Unified Medical Language System (UMLS) and MetaMap API, our semantic approach extracts hidden factors from unstructured text that might be very critical in the diagnosis or identification of potential risks of having a VTE. We use Kohonen self-organizing map to predict a patient's potential to develop VTE based on the similarity between the identified hidden risk factors in the clinical notes section from their medical records and the clinical notes used in the learning phase. Our system achieved 80% accuracy with 50% precision in prediction. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/BHI.2016.7455846 | 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) |
Keywords | Field | DocType |
semantic-based approach,venous thromboembolism,Kohonen self-organized map neural network,clinical documentation,Unified Medical Language System,MetaMap API,hidden factor extraction,unstructured text,diagnosis,identified hidden risk factors,clinical notes section,medical records,learning phase | Ontology (information science),Computer science,Feature extraction,Self-organizing map,Medical record,Artificial intelligence,Artificial neural network,Documentation,Unified Medical Language System,Machine learning,Semantics | Conference |
Citations | PageRank | References |
2 | 0.40 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Susan Sabra | 1 | 2 | 0.40 |
| Khalid Mahmood | 2 | 270 | 39.43 |
| Mazen Alobaidi | 3 | 20 | 3.96 |