Working Group Webinar Library
Webinar Library
TriNetX Journey: Challenges in Building a sustainable global data and analytics platform for clinical trials and research
We look back at the decade of effort to build, operate and grow the TriNetX strategic platform found in research informatics portfolios of many healthcare organizations and academic medical centers world-wide.
Development of a Natural Language Processing System for Extracting Rheumatoid Arthritis Outcomes From Clinical Notes Using the National Rheumatology Informatics System for Effectiveness Registry
Patient reported outcomes (PROs) include any report of the status of a patient's health condition that comes directly from the patient, without interpretation of the patient's response by a clinician or anyone else. PROs for functional status information describes the patient's physical and mental wellness at the whole-person level (as opposed to the cellular or organ level).
Improving Acute Kidney Injury Prediction and Risk Factor Analysis with Personalized Transfer Learning
Acute kidney injury (AKI) is a life-threatening clinical syndrome prevalent in hospitalized patients (10% to 15% affected), especially among critically ill patients (>50% affected), and has exceeded the annual incidence of myocardial infarction. AKI patients are at much higher risk for developing poor long-term outcomes including incident and progressive chronic kidney disease, cardiovascular disease, and death.
Leveraging Procedural Video Data for Quality, Safety, and Knowledge
High-dimensional data from procedural recordings are increasingly being leveraged by institutions for quality, safety, and efficiency. With advances in technology enabling high fidelity recordings and large scale analytics, the possible applications for this data continue to expand. Institutions must consider their data strategy and the ethical, legal, privacy, and insurance implications of recording clinical procedures.
Recommended practices and ethical considerations for natural language processing-assisted observational research: A scoping review
An increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical information from electronic health records (EHRs). Currently, no standardized reporting guidelines for NLP-assisted observational studies exist. The absence of detailed reporting guidelines may create ambiguity in the use of NLP-derived content, knowledge gaps in the current research reporting practices, and reproducibility challenges.