Machine learning predicts risk of cerebrospinal fluid shunt failure in children: a study from the hydrocephalus clinical research network.
Andrew T HaleJay Riva-CambrinJohn C WellonsEric M JacksonJohn R W KestleRobert P NaftelTodd C HankinsonChevis N Shannonnull nullPublished in: Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery (2021)
These data suggest that the ANN, or future iterations thereof, can provide an evidence-based tool to assist in prognostication and patient-counseling immediately after CSF shunt placement.
Keyphrases
- cerebrospinal fluid
- machine learning
- pulmonary artery
- big data
- case report
- young adults
- electronic health record
- artificial intelligence
- current status
- coronary artery
- smoking cessation
- ultrasound guided
- hiv testing
- pulmonary arterial hypertension
- pulmonary hypertension
- neural network
- deep learning
- men who have sex with men
- subarachnoid hemorrhage
- hepatitis c virus
- hiv infected
- data analysis
- network analysis