Predicting cardiovascular health trajectories in time-series electronic health records with LSTM models.
Aixia GuoRahmatollah BeheshtiYosef M KhanJames R LangabeerRandi E ForakerPublished in: BMC medical informatics and decision making (2021)
We suggest that future CVH levels can be predicted using previous CVH measurements for each submetric, which has implications for population cardiovascular health management. Predicting patients' future CVH levels might directly increase patient CVH health and thus quality of life, while also indirectly decreasing the burden and cost for clinical health system caused by CVD and cancers.
Keyphrases
- electronic health record
- end stage renal disease
- newly diagnosed
- healthcare
- current status
- public health
- ejection fraction
- chronic kidney disease
- mental health
- peritoneal dialysis
- clinical decision support
- depressive symptoms
- patient reported outcomes
- risk assessment
- risk factors
- climate change
- human health
- childhood cancer