Background With the rapid adoption of electronic health records (EHRs), it is desirable to
harvest information and knowledge from EHRs to support automated systems at the point of …

Objective Natural language processing (NLP) of symptoms from electronic health records
(EHRs) could contribute to the advancement of symptom science. We aim to synthesize the …

The use and functionality of electronic health records (EHRs) have increased rapidly in the
past decade. Although the primary purpose of EHRs is clinical, researchers have used them …

Objective Natural language processing (NLP) combined with machine learning (ML)
techniques are increasingly used to process unstructured/free-text patient-reported outcome …

Objective To describe the infrastructure, tools, and services developed at Stanford Medicine
to maintain its data science ecosystem and research patient data repository for clinical and …

PURPOSE Symptoms are vital outcomes for cancer clinical trials, observational research,
and population-level surveillance. Patient-reported outcomes (PROs) are valuable for …

The hospitalizations are part of cancer care and has been studied by researchers
worldwide. A better understanding about their associated factors may help to achieve …

Despite the rapid development of natural language processing (NLP) implementation in
electronic medical records (EMRs), Chinese EMRs processing remains challenging due to …

Using evidence derived from previously collected medical records to guide patient care has
been a long-standing vision of clinicians and informaticians and one with the potential to …

Context For patients with cancer, uncontrolled pain and other symptoms are the leading
cause of unplanned hospitalizations. Early access to specialty palliative care (PC) is …