Diverse patient trajectories during cytotoxic chemotherapy: Capturing longitudinal patient‐reported outcomes

Azad, Amee D.; Yilmaz, M.; Bozkurt, Selen; Brooks, James D.; Blayney, Douglas W.; Hernandez‐Boussard, Tina

doi:10.1002/cam4.4124

Cited by 8 publications

(2 citation statements)

References 48 publications

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“…Clustering and federated learning have also been combined to predict patient outcomes accurately, in a manner that limits data sharing between healthcare institutions and protects data privacy 36 . Finally, clustering has been used side-by-side with other statistical techniques, such as grouping oncology patient health trajectories after identifying statistically significant covariates for health decline 37 . Comparing a combined prediction and clustering approach to other approaches using only one or the other may be an area for future work.…”

Section: Discussionmentioning

confidence: 99%

Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients

Cowley

Robinette

Matelsky

et al. 2023

Sci Rep

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As clinicians are faced with a deluge of clinical data, data science can play an important role in highlighting key features driving patient outcomes, aiding in the development of new clinical hypotheses. Insight derived from machine learning can serve as a clinical support tool by connecting care providers with reliable results from big data analysis that identify previously undetected clinical patterns. In this work, we show an example of collaboration between clinicians and data scientists during the COVID-19 pandemic, identifying sub-groups of COVID-19 patients with unanticipated outcomes or who are high-risk for severe disease or death. We apply a random forest classifier model to predict adverse patient outcomes early in the disease course, and we connect our classification results to unsupervised clustering of patient features that may underpin patient risk. The paradigm for using data science for hypothesis generation and clinical decision support, as well as our triaged classification approach and unsupervised clustering methods to determine patient cohorts, are applicable to driving rapid hypothesis generation and iteration in a variety of clinical challenges, including future public health crises.

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Section: Discussionmentioning

confidence: 99%

Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients

Cowley

Robinette

Matelsky

et al. 2023

Sci Rep

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“…A subanalysis was performed on patients with at least one 12-item Patient-Reported Outcomes Measurement Information System (PROMIS) survey completed within 180 days before starting chemotherapy. 22 , 23 Two new models were trained on this cohort using same preprocessing, model training, and evaluation steps described above: one with the original features, and a second with the inclusion of 14 features derived from the PROMIS data (12-item survey responses, and the global mental and physical health scores). 24 …”

Section: Methodsmentioning

confidence: 99%

Machine Learning Applied to Electronic Health Records: Identification of Chemotherapy Patients at High Risk for Preventable Emergency Department Visits and Hospital Admissions

Peterson

Ostberg

Blayney

et al. 2021

JCO Clinical Cancer Informatics

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PURPOSE Acute care use (ACU) is a major driver of oncologic costs and is penalized by a Centers for Medicare & Medicaid Services quality measure, OP-35. Targeted interventions reduce preventable ACU; however, identifying which patients might benefit remains challenging. Prior predictive models have made use of a limited subset of the data in the electronic health record (EHR). We aimed to predict risk of preventable ACU after starting chemotherapy using machine learning (ML) algorithms trained on comprehensive EHR data. METHODS Chemotherapy patients treated at an academic institution and affiliated community care sites between January 2013 and July 2019 who met inclusion criteria for OP-35 were identified. Preventable ACU was defined using OP-35 criteria. Structured EHR data generated before chemotherapy treatment were obtained. ML models were trained to predict risk for ACU after starting chemotherapy using 80% of the cohort. The remaining 20% were used to test model performance by the area under the receiver operator curve. RESULTS Eight thousand four hundred thirty-nine patients were included, of whom 35% had preventable ACU within 180 days of starting chemotherapy. Our primary model classified patients at risk for preventable ACU with an area under the receiver operator curve of 0.783 (95% CI, 0.761 to 0.806). Performance was better for identifying admissions than emergency department visits. Key variables included prior hospitalizations, cancer stage, race, laboratory values, and a diagnosis of depression. Analyses showed limited benefit from including patient-reported outcome data and indicated inequities in outcomes and risk modeling for Black and Medicaid patients. CONCLUSION Dense EHR data can identify patients at risk for ACU using ML with promising accuracy. These models have potential to improve cancer care outcomes, patient experience, and costs by allowing for targeted, preventative interventions.

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