Poster presented at: 48th Annual Oncology Nursing Society Congress April 26-30, 2023 San Antonio, TX. Predicting anti-cancer treatment-related symptoms in patients with head and neck cancer using a machine learning approach: a scoping review. Each year, more than 2 million Americans have a. She encourages nurses to familiarize themselves with machine learning and its potentially predictive capabilities.Ĭummings M, Nilsen M, Bender C, Al-Zaiti S. The Million Hearts Initiative Preventing Heart Attacks and Strokes. These models were able to predict treatment-related symptoms with an area under the curve (AUC) accuracy range of 0.65 to 0.85.Īccording to Cummings, machine learning may prove a useful tool to optimize symptom management in this patient population. Perspective Facing Covid-19 in Italy Ethics. In the synthesis of these studies, the most commonly predictive symptoms included radiation-induced xerostomia, radiation induced temporal lobe injury, and chemotherapy-induced myelosuppression. Perspective from The New England Journal of Medicine Novel Coronavirus and Old Lessons Preparing the Health System for the Pandemic. Of note, only 3 studies included external validation measures to assess the predictive models. The most explored treatment modality was radiation (62%). Nine studies included patients with esophageal and nasopharyngeal cancers and 1 included patients with laryngeal cancer. Ultimately, a large proportion of articles included all patients with head and neck cancers (n = 11). Introduction Materials and Methods Results Notes Supplementary Material References Abstract Background The discovery of a saliva-based microribonucleic acid (miRNA) signature for endometriosis in. After the abstracts were screened for eligibility, a total of 30 articles were evaluated. They used PubMed to search for articles and selected those that evaluated machine learning to predict anticancer therapy symptoms for patients with head and neck cancer. Investigators conducted their review in accordance with the Preferred Reporting for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. The review was also designed to identify the gaps related to machine learning predictive models. The purpose of the review was to characterize the state of machine learning use in predicting treatment-related symptoms. Machine learning may have a growing role in predicting adverse events in patients undergoing cancer therapy, according to Meredith Cummings BSN, RN, OCN.Ĭummings, who is a PhD student at the University of Pittsburgh School of Nursing, and who works in an outpatient infusion center with Allegheny Health Network Cancer Institute, recently presented on machine learning in a poster presentation, at the 48th Annual Oncology Nursing Society Congress.
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