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
- randomized controlled trial
- chronic kidney disease
- palliative care
- prognostic factors
- artificial intelligence
- machine learning
- quality improvement
- primary care
- convolutional neural network
- physical activity
- sleep quality
- health information
- general practice
- adverse drug
- real time pcr