Earticle

초록

영어

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

목차

Abstract
1. Introduction
1.1 Related Work
2. Experiments
2.1 Data
2.2 Machine Learning Classifiers
2.3 Experimental Setup 
3. Results
4. Discussion
5. Conclusion
Acknowledgement
References

키워드

저자

• Gabriel D. M. Manalu [ Undergraduate student, Department of Computer Science and Electrical Engineering, Handong Global University, Korea ]
• Mulomba Mukendi Christian [ Doctoral student, Department of Advanced Convergence, Handong Global University, Korea ] Corresponding Author
• Songhee You [ Assistant professor, School of Spatial Environment System Engineering, Handong Global University, Korea ]
• Hyebong Choi [ Associate professor, School of Global Entrepreneurship and ICT, Handong Global University, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380