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Abstract BackgroundHypertension is a prevalent cardiovascular disease with severe longer-term implications. Conventional management based on clinical guidelines does not facilitate personalized treatment that accounts for a richer set of patient characteristics. MethodsRecords from 1/1/2012 to 1/1/2020 at the Boston Medical Center were used, selecting patients with either a hypertension diagnosis or meeting diagnostic criteria (≥ 130 mmHg systolic or ≥ 90 mmHg diastolic, n = 42,752). Models were developed to recommend a class of antihypertensive medications for each patient based on their characteristics. Regression immunized against outliers was combined with a nearest neighbor approach to associate with each patient an affinity group of other patients. This group was then used to make predictions of future Systolic Blood Pressure (SBP) under each prescription type. For each patient, we leveraged these predictions to select the class of medication that minimized their future predicted SBP. ResultsThe proposed model, built with a distributionally robust learning procedure, leads to a reduction of 14.28 mmHg in SBP, on average. This reduction is 70.30% larger than the reduction achieved by the standard-of-care and 7.08% better than the corresponding reduction achieved by the 2nd best model which uses ordinary least squares regression. All derived models outperform following the previous prescription or the current ground truth prescription in the record. We randomly sampled and manually reviewed 350 patient records; 87.71% of these model-generated prescription recommendations passed a sanity check by clinicians. ConclusionOur data-driven approach for personalized hypertension treatment yielded significant improvement compared to the standard-of-care. The model implied potential benefits of computationally deprescribing and can support situations with clinical equipoise.
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Precision Dosing in Critical Care: Application of Machine Learning in Fluid Therapy
Fluid therapy is a common treatment for hypovolemic scenarios to restore the lost blood volume and stabilize acutely ill patients. Automating fluid therapy can lead to a reduction of delay in care, a decrement in dosing errors, and a reduction of cognitive load on clinicians responsible for patient care resulting in improved patient outcomes. However, this process is highly challenging due to the complexity of patient’s physiology and the variability of hemodynamic responses among patients. This work presents a novel machine learning approach based on reinforcement learning (RL) for automated fluid management, where the RL agent is designed to recommend subject-specific infusion dosages without having the knowledge of dose-response models and only by interacting with the environment (virtual subject generator). Compared to the state-of-the-art focusing on the entire population’s data, the proposed approach uses individual patient’s data to recommend patient-specific fluid dosage adjustment. Simulation results demonstrate that the proposed approach outperforms a proportional-integral-derivative (PID) and a rule-based fluid resuscitation controller previously reported for an animal study.
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- Award ID(s):
- 2138929
- PAR ID:
- 10417515
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-4103-4
- Subject(s) / Keyword(s):
- Machine learning reinforcement learning fluid management automated fluid therapy mean arterial pressure
- Format(s):
- Medium: X
- Location:
- Chicago, IL, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICDH60066.2023.00058
