Irregularly sampled time series (ISTS) data has irregular temporal intervals between
observations and different sampling rates between sequences. ISTS commonly appears in …

Due to the discrepancy of diseases and symptoms, patients usually visit hospitals irregularly
and different physiological variables are examined at each visit, producing large amounts of …

The broad adoption of electronic health records (EHRs) has led to vast amounts of data
being accumulated on a patient's history, diagnosis, prescriptions, and lab tests. Advances …

Patients with co-morbidities often require multiple medications to manage their conditions.
However, existing medication recommendation systems only offer class-level medications …

Medical time series have been widely employed for disease prediction. Missing data hinders
accurate prediction. While existing imputation methods partially solve the problem, there are …

Advancement in Machine Learning (ML) has opened new gateways for transforming the
healthcare sector. This paper explores the integration of ML techniques within the …

Risk prediction using electronic health records (EHR) is a challenging data mining task due
to the two-level hierarchical structure of EHR data. EHR data consist of a set of time-ordered …

In intensive care units (ICUs), mortality prediction is performed by combining information
from these two sources of ICU patients by monitoring patient health. Respectively, time …

Influenced by the dynamic changes in the severity of illness, patients usually take
examinations in hospitals irregularly, producing a large volume of irregular medical time …

Background Designing appropriate clinical dental treatment plans is an urgent need
because a growing number of dental patients are suffering from partial edentulism with the …