Predicting anxiety in cancer survivors presenting to primary care – A machine learning approach accounting for physical comorbidity
| Autor: | Verena Zimmermann‐Schlegel, Markus W. Haun, Halina Sklenarova, Mechthild Hartmann, Hans-Christoph Friederich, Laura Simon |
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| Rok vydání: | 2021 |
| Předmět: |
Male
0301 basic medicine Cancer Research computer.software_genre 0302 clinical medicine Sleep Initiation and Maintenance Disorders Fatigue RC254-282 Original Research Muscle Weakness Neoplasms. Tumors. Oncology. Including cancer and carcinogens Regression analysis Cancer Pain Middle Aged anxiety health services research comorbidity machine learning Oncology 030220 oncology & carcinogenesis Colonic Neoplasms Regression Analysis Anxiety Female Supervised Machine Learning medicine.symptom Bioinformatics Breast Neoplasms Accounting Machine learning primary care 03 medical and health sciences Breast cancer Predictive Value of Tests medicine Humans cancer survivors Radiology Nuclear Medicine and imaging Generalizability theory Aged Primary Health Care business.industry Model selection Prostatic Neoplasms Secondary data prediction medicine.disease Comorbidity 030104 developmental biology Test set Artificial intelligence business computer Stress Psychological |
| Zdroj: | Cancer Medicine, Vol 10, Iss 14, Pp 5001-5016 (2021) Cancer Medicine |
| ISSN: | 2045-7634 |
| Popis: | Background The purpose of this study was to explore predictors for anxiety as the most common form of psychological distress in cancer survivors while accounting for physical comorbidity. Methods We conducted a secondary data analysis of a large study within the German National Cancer Plan which enrolled primary care cancer survivors diagnosed with colon, prostatic, or breast cancer. We selected candidate predictors based on a systematic MEDLINE search. Using supervised machine learning, we developed a prediction model for anxiety by splitting the data into a 70% training set and a 30% test set and further split the training set into 10‐folds for cross‐validating the hyperparameter tuning step during model selection. We fit six different regression models, selected the model that maximized the root mean square error (RMSE) and fit the selected model to the entire training set. Finally, we evaluated the model performance on the holdout test set. Results In total, data from 496 cancer survivors were analyzed. The LASSO model (α = 1.0) with weakly penalized model complexity (λ = 0.015) slightly outperformed all other models (RMSE = 0.370). Physical symptoms, namely, fatigue/weakness (β = 0.18), insomnia (β = 0.12), and pain (β = 0.04), were the most important predictors, while the degree of physical comorbidity was negligible. Conclusions Prediction of clinically significant anxiety in cancer survivors using readily available predictors is feasible. The findings highlight the need for considering cancer survivors’ physical functioning regardless of the degree of comorbidity when assessing their psychological well‐being. The generalizability of the model to other populations should be investigated in future external validations. Prediction of clinically significant anxiety in cancer survivors using readily available predictors and machine learning is feasible. Clinicians need to consider cancer survivors’ physical functioning regardless of the degree of comorbidity when assessing their psychological well‐being. |
| Databáze: | OpenAIRE |
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