Concetti Chiave
The core message of this article is to develop a flexible probabilistic model that can predict properties of future values of a patient's electronic health record (EHR) given partial sequences of their EHR, using a single model.
Sintesi
The article presents a probabilistic model for analyzing episodic EHR data containing mixed sequences and static information. The model is a mixture over probability distributions tailored to each data type, including collections and sequences.
Key highlights:
The model captures complex relationships between medications, diagnoses, laboratory tests, neurological assessments, and medications in the EHR data.
Inference algorithms are derived to estimate properties of the complete sequences, including their length and presence of specific values, using partial data.
The model is trained on data from the Kaiser Permanente Northern California (KPNC) healthcare system and evaluated on sequence length prediction and ICU presence prediction tasks.
The results show that the model outperforms baseline approaches, indicating that it is able to leverage individualized information contained within the sequences to make more accurate predictions.
The model can be used for various applications, including prognosis of patient health trajectories, resource allocation, treatment planning, and outcome prediction in a dynamic clinical environment.
Statistiche
The average length of the Beds sequence is 2.68 per episode.
The average length of the Laboratory Tests sequence is 197 per episode.
The average length of the Medications sequence is 15.2 per episode.