Enhancing Acute Kidney Injury Prediction through Integration of Drug Features in Intensive Care Units

• URL: http://arxiv.org/abs/2401.04368v1
• Date: Tue, 9 Jan 2024 05:42:32 GMT
• Title: Enhancing Acute Kidney Injury Prediction through Integration of Drug Features in Intensive Care Units
• Authors: Gabriel D. M. Manalu, Mulomba Mukendi Christian, Songhee You, Hyebong Choi
• Abstract summary: The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs has yet to be explored in the critical care setting. This study proposes a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

Related papers

• XAI for In-hospital Mortality Prediction via Multimodal ICU Data [57.73357047856416]
  We propose an efficient, explainable AI solution for predicting in-hospital mortality via multimodal ICU data. We employ multimodal learning in our framework, which can receive heterogeneous inputs from clinical data and make decisions. Our framework can be easily transferred to other clinical tasks, which facilitates the discovery of crucial factors in healthcare research.
  arXiv  Detail & Related papers  (2023-12-29T14:28:04Z)
• Multiscale Topology in Interactomic Network: From Transcriptome to Antiaddiction Drug Repurposing [0.3683202928838613]
  The escalating drug addiction crisis in the United States underscores the urgent need for innovative therapeutic strategies. This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment.
  arXiv  Detail & Related papers  (2023-12-03T04:01:38Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction [15.06049250330114]
  The Intensive Care Unit (ICU) is one of the most important parts of a hospital, which admits critically ill patients and provides continuous monitoring and treatment. Various patient outcome prediction methods have been attempted to assist healthcare professionals in clinical decision-making.
  arXiv  Detail & Related papers  (2023-08-24T05:26:56Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• On the Importance of Clinical Notes in Multi-modal Learning for EHR Data [0.0]
  Previous research has shown that jointly using clinical notes with electronic health record data improved predictive performance for patient monitoring. We first confirm that performance significantly improves over state-of-the-art EHR data models when combining EHR data and clinical notes. We then provide an analysis showing improvements arise almost exclusively from a subset of notes containing broader context on patient state rather than clinician notes.
  arXiv  Detail & Related papers  (2022-12-06T15:18:57Z)
• Prediction of drug effectiveness in rheumatoid arthritis patients based on machine learning algorithms [2.5759046095742453]
  Rheumatoid arthritis (RA) is an autoimmune condition caused when patients' immune system mistakenly targets their own tissue. Machine learning (ML) has the potential to identify patterns in patient electronic health records to forecast the best clinical treatment to improve patient outcomes. This study introduced a Drug Response Prediction (TNF) framework with two main goals: 1) design a data processing pipeline to extract information from clinical data, and then preprocess it for functional use, and 2) predict RA patient's responses to drugs and evaluate classification models' performance.
  arXiv  Detail & Related papers  (2022-10-14T15:15:37Z)
• SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction [127.43571146741984]
  Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery. wet experiments remain the most reliable method, but they are time-consuming and resource-intensive. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. We present the SSM-DTA framework, which incorporates three simple yet highly effective strategies.
  arXiv  Detail & Related papers  (2022-06-20T14:53:25Z)
• Literature-Augmented Clinical Outcome Prediction [10.46990394710927]
  We introduce techniques to help bridge this gap between EBM and AI-based clinical models. We propose a novel system that automatically retrieves patient-specific literature based on intensive care (ICU) patient information. Our model is able to substantially boost predictive accuracy on three challenging tasks in comparison to strong recent baselines.
  arXiv  Detail & Related papers  (2021-11-16T11:19:02Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspective [61.70045118068213]
  We build a dataset by collecting symptoms of PD patients, and their prescription drug provided by neurologists. Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug. For the new coming patients, we could recommend (predict) suitable prescription drug on their observed symptoms by our prescription model.
  arXiv  Detail & Related papers  (2020-07-31T14:34:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.