ArticleCPT: pharmacometrics & systems pharmacology2023
Clinical decision support for chemotherapy-induced neutropenia using a hybrid pharmacodynamic/machine learning model.
Article in CPT: pharmacometrics & systems pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning methods for predicting adverse drug events: A systematic review.British journal of clinical pharmacology · 2026Pooled it
- Development of an Artificial Intelligence Web Application for Predicting Chemotherapy-Induced Neutropenia in Patients With Non-Small Cell Lung Cancer: A Prospective Study.Cancer medicine · 2026Article
- Clinical Model-Informed Precision Dosing Consult Service for Accelerating Personalized Medication in Pediatric Patients.Clinical pharmacology and therapeutics · 2026Review
- Model-informed precision dosing of carboplatin in cancer patients by leveraging myelosuppression data from electronic health records.British journal of clinical pharmacology · 2026Article
- Development and validation of a machine learning model for individualised meropenem dosing in CRRT patients.Scientific reports · 2026Article
- Enhancing Severe Neutropenia Prediction: PKPD-Informed Labeling for Machine Learning Models Trained on Real-World Data.Clinical pharmacology and therapeutics · 2026Article
- Prediction of neutrophil nadir and recovery following paediatric haematopoietic cell transplantation with busulfan conditioning.British journal of clinical pharmacology · 2026Article
- Rising Role of Artificial Intelligence in Clinical Pharmacometrics and Model-Informed Precision Dosing in Pediatrics.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2026Article
- Hybrid Population Pharmacokinetic-Machine Learning Modeling to Predict Infliximab Pharmacokinetics in Pediatric and Young Adult Patients with Crohn's Disease.Clinical pharmacokinetics · 2025Article
- Hybrid Population PK-Machine Learning Modeling to Predict Infliximab Pharmacokinetics in Pediatric and Young Adult Patients with Crohn's Disease.bioRxiv : the preprint server for biology · 2025Article
- Article
- Applying AI to Structured Real-World Data for Pharmacovigilance Purposes: Scoping Review.Journal of medical Internet research · 2024Article
- Prediction of vancomycin plasma concentration in elderly patients based on multi-algorithm mining combined with population pharmacokinetics.Scientific reports · 2024Article
- Evaluating Use of Artificial Intelligence for Drug Exposure and Effect Prediction.Kidney international reports · 2024Article
- Clinical decision support for chemotherapy-induced neutropenia using a hybrid pharmacodynamic/machine learning model.CPT: pharmacometrics & systems pharmacology · 2023Article
- Role of pharmacometrics and systems pharmacology in facilitating efficient dose optimization in oncology.CPT: pharmacometrics & systems pharmacology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Consensus guidelines recommend use of granulocyte colony stimulating factor in patients deemed at risk of chemotherapy-induced neutropenia, however, these risk models are limited in the factors they consider and miss some cases of neutropenia. Clinical decision making could be supported using models that better tailor their predictions to the individual patient using the wealth of data available in electronic health records (EHRs). Here, we present a hybrid pharmacokinetic/pharmacodynamic (PKPD)/machine learning (ML) approach that uses predictions and individual Bayesian parameter estimates from a PKPD model to enrich an ML model built on her data. We demonstrate this approach using models developed on a large real-world data set of 9121 patients treated for lymphoma, breast, or thoracic cancer. We also investigate the benefits of augmenting the training data using synthetic data simulated with the PKPD model. We find that PKPD-enrichment of ML models improves prediction of grade 3-4 neutropenia, as measured by higher precision (61%) and recall (39%) compared to PKPD model predictions (47%, 33%) or base ML model predictions (51%, 31%). PKPD augmentation of ML models showed minor improvements in recall (44%) but not precision (56%), and data augmentation required careful tuning to control overfitting its predictions to the PKPD model. PKPD enrichment of ML shows promise for leveraging both the physiology-informed predictions of PKPD and the ability of ML to learn predictor-outcome relationships from large data sets to predict patient response to drugs in a clinical precision dosing context.
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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.