Forecasting Treatment Response with Deep Pharmacokinetic Encoders

• URL: http://arxiv.org/abs/2309.13135v7
• Date: Sat, 02 Nov 2024 22:34:52 GMT
• Title: Forecasting Treatment Response with Deep Pharmacokinetic Encoders
• Authors: Willa Potosnak, Cristian Challu, Kin Gutierrez Olivares, Keith Dufendach, Artur Dubrawski,
• Abstract summary: We propose a novel hybrid global-local architecture and a PK encoder that informs deep learning models of patient-specific treatment effects. We showcase the efficacy of our approach in achieving significant accuracy gains for a blood glucose forecasting task.
• Score: 14.900236106367167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Forecasting healthcare time series data is vital for early detection of adverse outcomes and patient monitoring. However, forecasting is challenging in practice due to variable medication administration and unique pharmacokinetic (PK) properties for each patient. To address these challenges, we propose a novel hybrid global-local architecture and a PK encoder that informs deep learning models of patient-specific treatment effects. We showcase the efficacy of our approach in achieving significant accuracy gains for a blood glucose forecasting task using both realistically simulated and real-world data. Our hybrid global-local architecture improves over patient-specific models by 15.8% on average. Additionally, our PK encoder surpasses baselines by up to 16.4% on simulated data and 4.9% on real-world data for individual patients during critical events of severely high and low glucose levels.

Related papers

• Zero-shot generation of synthetic neurosurgical data with large language models [0.7373617024876725]
  This study aims to evaluate the capability of zero-shot generation of synthetic neurosurgical data with a large language model (LLM), GPT-4o. Data synthesized with GPT-4o can effectively augment clinical data with small sample sizes, and train ML models for prediction of neurosurgical outcomes.
  arXiv  Detail & Related papers  (2025-02-13T18:21:15Z)
• Pre-Ictal Seizure Prediction Using Personalized Deep Learning [0.0]
  Approximately 23 million or 30% of epilepsy patients worldwide suffer from drug-resistant epilepsy (DRE) The unpredictability of seizure occurrences, which causes safety issues as well as social concerns, restrict the lifestyles of DRE patients. The goal of this research was to employ improved technologies and methods to predict seizures up to two hours before onset.
  arXiv  Detail & Related papers  (2024-10-07T21:04:41Z)
• 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)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching [49.78442796596806]
  We propose an innovative privacy-aware data augmentation approach for patient-trial matching (LLM-PTM) Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%.
  arXiv  Detail & Related papers  (2023-03-24T03:14:00Z)
• Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Values [9.418011774179794]
  This paper proposes a novel end-to-end approach to modeling EHR patient journey data with Integrated Convolutional and Recurrent Neural Networks. Our model can capture both long- and short-term temporal patterns within each patient journey and effectively handle the high degree of missingness in EHR data without any imputation data generation.
  arXiv  Detail & Related papers  (2022-11-11T07:36:18Z)
• A Meta-GNN approach to personalized seizure detection and classification [53.906130332172324]
  We propose a personalized seizure detection and classification framework that quickly adapts to a specific patient from limited seizure samples. We train a Meta-GNN based classifier that learns a global model from a set of training patients. We show that our method outperforms the baselines by reaching 82.7% on accuracy and 82.08% on F1 score after only 20 iterations on new unseen patients.
  arXiv  Detail & Related papers  (2022-11-01T14:12:58Z)
• A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care [15.64030213048907]
  We propose two clinical prediction tasks, Outcome-specific length-of-stay prediction and Early mortality prediction for COVID-19 patients in intensive care units. The two tasks are adapted from the naive length-of-stay and mortality prediction tasks to accommodate the clinical practice for COVID-19 patients. We propose fair, detailed, open-source data-preprocessing pipelines and evaluate 17 state-of-the-art predictive models on two tasks.
  arXiv  Detail & Related papers  (2022-09-16T09:09:15Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data [56.53715632642495]
  Clinical trials face uncertain outcomes due to issues with efficacy, safety, or problems with patient recruitment. In this paper, we propose Hierarchical INteraction Network (HINT) for more general, clinical trial outcome predictions.
  arXiv  Detail & Related papers  (2021-02-08T15:09:07Z)
• Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Network [9.287068570192057]
  We propose a robust in-time predictor for in-hospital COVID-19 patient's probability of requiring mechanical ventilation. A challenge in the risk prediction for COVID-19 patients lies in the great variability and irregular sampling of patient's vitals and labs observed in the clinical setting. We frame the prediction task into a multi-objective learning framework, and the risk scores at all time points are optimized altogether.
  arXiv  Detail & Related papers  (2021-02-01T20:35:22Z)
• Longitudinal modeling of MS patient trajectories improves predictions of disability progression [2.117653457384462]
  This work addresses the task of optimally extracting information from longitudinal patient data in the real-world setting. We show that with machine learning methods suited for patient trajectories modeling, we can predict disability progression of patients in a two-year horizon. Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction.
  arXiv  Detail & Related papers  (2020-11-09T20:48:00Z)
• DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data [68.29870617697532]
  We propose Deep Recurrent Inverse TreatmEnt weighting (DeepRite) for time-varying confounding in longitudinal data. DeepRite is shown to recover the ground truth from synthetic data, and estimate unbiased treatment effects from real data.
  arXiv  Detail & Related papers  (2020-10-28T15:05:08Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)

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.