BACKGROUND: The digital transformation of society has a tremendous impact on both medicine and healthcare. The generation and processing of continuously growing amounts of digital data can be used to facilitate new approaches in research, particularl...
PURPOSE: Electronic medical records (EMRs) and population-based cancer registries contain information on cancer outcomes and treatment, yet rarely capture information on the timing of metastatic cancer recurrence, which is essential to understand can...
PURPOSE: A substantial portion of medical data is unstructured. Extracting data from unstructured text presents a barrier to advancing clinical research and improving patient care. In addition, ongoing studies have been focused predominately on the E...
PURPOSE: Drug development in oncology currently is facing a conjunction of an increasing number of antineoplastic agents (ANAs) candidate for phase I clinical trials (P1CTs) and an important attrition rate for final approval. We aimed to develop a ma...
PURPOSE: We have created a cloud-based machine learning system (CLOBNET) that is an open-source, lean infrastructure for electronic health record (EHR) data integration and is capable of extract, transform, and load (ETL) processing. CLOBNET enables ...
PURPOSE: Robust institutional tumor banks depend on continuous sample curation or else subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation automation is hindered by semistructured free-text clinical pathology no...
PURPOSE: Cancer pathology findings are critical for many aspects of care but are often locked away as unstructured free text. Our objective was to develop a natural language processing (NLP) system to extract prostate pathology details from postopera...
PURPOSE: Natural language processing (NLP) techniques have been adopted to reduce the curation costs of electronic health records. However, studies have questioned whether such techniques can be applied to data from previously unseen institutions. We...
This study assesses the feasibility of using machine learning to automatically populate a review of systems of all symptoms discussed in an encounter between a patient and a clinician.
Zhongguo fei ai za zhi = Chinese journal of lung cancer
May 20, 2019
BACKGROUND: Lung cancer is the cancer with the highest morbidity and mortality at home and abroad at present. Using computed tomography (CT) to screen lung cancer nodules is a huge workload. To test the effect of artificial intelligence in automatic ...
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