ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2018
A Deep Deterministic Policy Gradient Approach to Medication Dosing and Surveillance in the ICU.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 41 citations in OpenAlex.
- Nephrocast-V: A Deep Learning Model for the Prediction of Vancomycin Trough Concentration Using Electronic Health Record Data.Pharmacotherapy · 2026Article
- Article
- A Primer on Reinforcement Learning in Medicine for Clinicians.NPJ digital medicine · 2024Review
- PrescDRL: deep reinforcement learning for herbal prescription planning in treatment of chronic diseases.Chinese medicine · 2024Article
- Deep Attention Q-Network for Personalized Treatment Recommendation.Proceedings ... ICDM workshops. IEEE International Conference on Data Mining · 2023Article
- Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach.Interactive journal of medical research · 2022Article
- A Clinical Prediction Model to Predict Heparin Treatment Outcomes and Provide Dosage Recommendations: Development and Validation Study.Journal of medical Internet research · 2021Article
- Patient-Specific Sedation Management via Deep Reinforcement Learning.Frontiers in digital health · 2021Article
- Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review.Journal of medical Internet research · 2020Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Medication dosing in a critical care environment is a complex task that involves close monitoring of relevant physiologic and laboratory biomarkers and corresponding sequential adjustment of the prescribed dose. Misdosing of medications with narrow therapeutic windows (such as intravenous [IV] heparin) can result in preventable adverse events, decrease quality of care and increase cost. Therefore, a robust recommendation system can help clinicians by providing individualized dosing suggestions or corrections to existing protocols. We present a clinician-in-the-loop framework for adjusting IV heparin dose using deep reinforcement learning (RL). Our main objectives were to learn a new IV heparin dosing policy based on the multi-dimensional features of patients, and evaluate the effectiveness of the learned policy in the presence of other confounding factors that may contribute to heparin-related side effects. The data used in the experiments included 2598 intensive care patients from the publicly available MIMIC database and 2310 patients from the Emory University clinical data warehouse. Experimental results suggested that the distance from RL policy had a statistically significant association with anticoagulant complications $(p< 0.05)$, after adjusting for the effects of confounding factors.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.