  Clinical texts, represented in electronic medical records (EMRs), contain
rich medical information and are essential for disease prediction, personalised
information recommendation, clinical decision support, and medication pattern
mining and measurement. Relation extractions between medication mentions and
temporal information can further help clinicians better understand the
patients' treatment history. To evaluate the performances of deep learning (DL)
and large language models (LLMs) in medication extraction and temporal
relations classification, we carry out an empirical investigation of
\textbf{MedTem} project using several advanced learning structures including
BiLSTM-CRF and CNN-BiLSTM for a clinical domain named entity recognition (NER),
and BERT-CNN for temporal relation extraction (RE), in addition to the
exploration of different word embedding techniques. Furthermore, we also
designed a set of post-processing roles to generate structured output on
medications and the temporal relation. Our experiments show that CNN-BiLSTM
slightly wins the BiLSTM-CRF model on the i2b2-2009 clinical NER task yielding
75.67, 77.83, and 78.17 for precision, recall, and F1 scores using Macro
Average. BERT-CNN model also produced reasonable evaluation scores 64.48,
67.17, and 65.03 for P/R/F1 using Macro Avg on the temporal relation extraction
test set from i2b2-2012 challenges. Code and Tools from MedTem will be hosted
at \url{https://github.com/HECTA-UoM/MedTem}
