Predicting anti-cancer treatment-related symptoms in patients with head and neck cancer using a machine learning approach: a scoping review. She encourages nurses to familiarize themselves with machine learning and its potentially predictive capabilities.Ĭummings M, Nilsen M, Bender C, Al-Zaiti S. 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. We are also a leader in participatory and open science approaches, and offer the option to publish new submissions immediately as preprints, which receive DOIs. In the synthesis of these studies, the most commonly predictive symptoms included radiation-induced xerostomia, radiation induced temporal lobe injury, and chemotherapy-induced myelosuppression. 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). After the abstracts were screened for eligibility, a total of 30 articles were evaluated. Rapper and actor Ice-T’s father-in-law, after. 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. Former New Jersey Governor Chris Christie, who was hospitalized with Covid-19, wrote a Wall Street Journal op-ed, I Should Have Worn a Mask.
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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