Machine learning methods in predicting chemotherapy-induced neutropenia in oncology patients using clinical data

Holborow, Alexander; Coupe, Bryony; Davies, Mark; Zhou, Shang‐Ming

doi:10.7861/clinmedicine.19-3s-s89

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…12 Within oncology, ML is increasingly being applied to longitudinal electronic health data to estimate the risk of undesirable events, such as acute care after initiating systemic therapy, trajectories of cytopenias during chemotherapy, and response to therapy. [13][14][15][16] ML predictions of prognosis outperform clinicians 17 and prompts from prognostic ML systems can increase the frequency of serious illness conversations. 18,19 To our knowledge, no study to date has evaluated whether ML could improve the allocation of PC resources.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning to Allocate Palliative Care Consultations During Cancer Treatment

He,

Moffat,

Podolsky

et al. 2024

JCO

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PURPOSE For patients with advanced cancer, early consultations with palliative care (PC) specialists reduce costs, improve quality of life, and prolong survival. However, capacity limitations prevent all patients from receiving PC shortly after diagnosis. We evaluated whether a prognostic machine learning system could promote early PC, given existing capacity. METHODS Using population-level administrative data in Ontario, Canada, we assembled a cohort of patients with incurable cancer who received palliative-intent systemic therapy between July 1, 2014, and December 30, 2019. We developed a machine learning system that predicted death within 1 year of each treatment using demographics, cancer characteristics, treatments, symptoms, laboratory values, and history of acute care admissions. We trained the system in patients who started treatment before July 1, 2017, and evaluated the potential impact of the system on PC in subsequent patients. RESULTS Among 560,210 treatments received by 54,628 patients, death occurred within 1 year of 45.2% of treatments. The machine learning system recommended the same number of PC consultations observed with usual care at the 60.0% 1-year risk of death, with a first-alarm positive predictive value of 69.7% and an outcome-level sensitivity of 74.9%. Compared with usual care, system-guided care could increase early PC by 8.5% overall (95% CI, 7.5 to 9.5; P < .001) and by 15.3% (95% CI, 13.9 to 16.6; P < .001) among patients who live 6 months beyond their first treatment, without requiring more PC consultations in total or substantially increasing PC among patients with a prognosis exceeding 2 years. CONCLUSION Prognostic machine learning systems could increase early PC despite existing resource constraints. These results demonstrate an urgent need to deploy and evaluate prognostic systems in real-time clinical practice to increase access to early PC.

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Section: Introductionmentioning

confidence: 99%

Machine Learning to Allocate Palliative Care Consultations During Cancer Treatment

He,

Moffat,

Podolsky

et al. 2024

JCO

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“…Most existing models rely on logistic regression [2][3][4][5][6] or ML methods that lack interpretability. 7,8 Our proposed model differs from existing studies in several ways: we assess the risk of SN-FN at the initiation of any chemotherapy cycle, not only the patient's first cycle; we consider a broad set of cancers and drugs, rather than focusing on targeted populations or treatment regimens; and we restrict our data set to discrete EMR fields, allowing for direct integration into oncology workflows without manual data manipulation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Neutropenic Events in Chemotherapy Patients: A Machine Learning Approach

Wiberg

Montanaro

et al. 2021

JCO Clinical Cancer Informatics

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PURPOSE Severe and febrile neutropenia present serious hazards to patients with cancer undergoing chemotherapy. We seek to develop a machine learning–based neutropenia prediction model that can be used to assess risk at the initiation of a chemotherapy cycle. MATERIALS AND METHODS We leverage rich electronic medical records (EMRs) data from a large health care system and apply machine learning methods to predict severe and febrile neutropenic events. We outline the data curation process and challenges posed by EMRs data. We explore a range of algorithms with an emphasis on model interpretability and ease of use in a clinical setting. RESULTS Our final proposed model demonstrates an out-of-sample area under the receiver operating characteristic curve of 0.865 (95% CI, 0.830 to 0.891) in the prediction of neutropenic events on the basis of only 20 clinical features. The model validates known risk factors and offers insight into potential novel clinical indicators and treatment characteristics that elevate risk. It relies on factors that are directly extractable from EMRs, provided a tool can be easily integrated into existing workflows. A cost-based analysis provides insight into optimal risk thresholds and offers a framework for tailoring algorithms to individual hospital needs. CONCLUSION A better understanding of neutropenic risk on an individual level enables a more informed approach to patient monitoring and treatment decisions.

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Machine learning methods in predicting chemotherapy-induced neutropenia in oncology patients using clinical data

Cited by 2 publications

References 3 publications

Machine Learning to Allocate Palliative Care Consultations During Cancer Treatment

Machine Learning to Allocate Palliative Care Consultations During Cancer Treatment

Prediction of Neutropenic Events in Chemotherapy Patients: A Machine Learning Approach

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