AIMC Topic: Electronic Health Records
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Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality Utilizing Electronic Medical Record Data Available at the End of Hospitalization in Multicondition Patients: a Proof-of-Concept Study.
Journal of general internal medicine
Jan 30, 2018
BACKGROUND: Predicting death in a cohort of clinically diverse, multicondition hospitalized patients is difficult. Prognostic models that use electronic medical record (EMR) data to determine 1-year death risk can improve end-of-life planning and ris...
Natural Language-based Machine Learning Models for the Annotation of Clinical Radiology Reports.
Radiology
Jan 30, 2018
Purpose To compare different methods for generating features from radiology reports and to develop a method to automatically identify findings in these reports. Materials and Methods In this study, 96 303 head computed tomography (CT) reports were ob...
Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning.
Journal of medical Internet research
Jan 30, 2018
BACKGROUND: As a high-prevalence health condition, hypertension is clinically costly, difficult to manage, and often leads to severe and life-threatening diseases such as cardiovascular disease (CVD) and stroke.
Machine learning to parse breast pathology reports in Chinese.
Breast cancer research and treatment
Jan 29, 2018
INTRODUCTION: Large structured databases of pathology findings are valuable in deriving new clinical insights. However, they are labor intensive to create and generally require manual annotation. There has been some work in the bioinformatics communi...
Labeling for Big Data in radiation oncology: The Radiation Oncology Structures ontology.
PloS one
Jan 19, 2018
PURPOSE: Leveraging Electronic Health Records (EHR) and Oncology Information Systems (OIS) has great potential to generate hypotheses for cancer treatment, since they directly provide medical data on a large scale. In order to gather a significant am...
Federated learning of predictive models from federated Electronic Health Records.
International journal of medical informatics
Jan 12, 2018
BACKGROUND: In an era of "big data," computationally efficient and privacy-aware solutions for large-scale machine learning problems become crucial, especially in the healthcare domain, where large amounts of data are stored in different locations an...
Max-margin weight learning for medical knowledge network.
Computer methods and programs in biomedicine
Jan 11, 2018
BACKGROUND AND OBJECTIVE: The application of medical knowledge strongly affects the performance of intelligent diagnosis, and method of learning the weights of medical knowledge plays a substantial role in probabilistic graphical models (PGMs). The p...
Harnessing electronic medical records to advance research on multiple sclerosis.
Multiple sclerosis (Houndmills, Basingstoke, England)
Jan 9, 2018
BACKGROUND: Electronic medical records (EMR) data are increasingly used in research, but no studies have yet evaluated similarity between EMR and research-quality data and between characteristics of an EMR multiple sclerosis (MS) population and known...
Prediction of venous thromboembolism using semantic and sentiment analyses of clinical narratives.
Computers in biology and medicine
Jan 3, 2018
Venous thromboembolism (VTE) is the third most common cardiovascular disorder. It affects people of both genders at ages as young as 20 years. The increased number of VTE cases with a high fatality rate of 25% at first occurrence makes preventive mea...
How Confounder Strength Can Affect Allocation of Resources in Electronic Health Records.
Perspectives in health information management
Jan 1, 2018
When electronic health record (EHR) data are used, multiple approaches may be available for measuring the same variable, introducing potentially confounding factors. While additional information may be gleaned and residual confounding reduced through...
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