Predicting Acute Care Events Among Patients Initiating Chemotherapy: A Practice-Based Validation and Adaptation of the PROACCT Model.
Jacob Newton SteinLisette DunhamWilliam A WoodEmily M RayHanna K SanoffJennifer Elston LafataPublished in: JCO oncology practice (2023)
We present three models designed for EHR integration that effectively identify oncology patients at highest risk for ACE after initiation of systemic anticancer treatment. By limiting predictors to structured data fields and including all cancer types, these models offer broad applicability for cancer care organizations and may offer a safety net to identify and target resources to this high risk.
Keyphrases
- acute care
- electronic health record
- papillary thyroid
- primary care
- healthcare
- palliative care
- angiotensin ii
- big data
- squamous cell
- machine learning
- quality improvement
- squamous cell carcinoma
- combination therapy
- replacement therapy
- lymph node metastasis
- artificial intelligence
- smoking cessation
- data analysis
- childhood cancer