Predicting In-Hospital Mortality After Acute Myeloid Leukemia Therapy: Through Supervised Machine Learning Algorithms.
Nauman S SiddiquiAndreas K KleinAmandeep GodaraRachel J BuchsbaumMichael C HughesPublished in: JCO clinical cancer informatics (2022)
Using readily accessible variables, inpatient mortality of patients on track for chemotherapy to treat AML can be predicted through ML algorithms. The model also predicted inpatient mortality when tested on different data representations and paves the way for future research.
Keyphrases
- machine learning
- acute myeloid leukemia
- big data
- end stage renal disease
- artificial intelligence
- cardiovascular events
- palliative care
- deep learning
- ejection fraction
- mental health
- chronic kidney disease
- newly diagnosed
- risk factors
- peritoneal dialysis
- prognostic factors
- allogeneic hematopoietic stem cell transplantation
- squamous cell carcinoma
- working memory
- electronic health record
- type diabetes
- coronary artery disease
- cardiovascular disease
- acute care
- stem cells
- current status
- radiation therapy
- patient reported outcomes
- patient reported
- rectal cancer
- smoking cessation