Adverse Event Signal Detection Using Patients' Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models.
Satoshi NishiokaSatoshi WatabeYuki YanagisawaKyoko SayamaHayato KizakiShungo ImaiMitsuhiro SomeyaRyoo TaniguchiShuntaro YadaEiji AramakiSatoko HoriPublished in: Journal of medical Internet research (2024)
Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients' subjective information recorded in pharmaceutical care records accumulated during pharmacists' daily work.
Keyphrases
- deep learning
- end stage renal disease
- ejection fraction
- healthcare
- newly diagnosed
- palliative care
- prognostic factors
- machine learning
- artificial intelligence
- peritoneal dialysis
- convolutional neural network
- high throughput
- patient reported outcomes
- pain management
- chronic pain
- depressive symptoms
- patient reported
- electronic health record