AIMC Topic: Electronic Health Records
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Natural Language Processing Accurately Calculates Adenoma and Sessile Serrated Polyp Detection Rates.
Digestive diseases and sciences
Apr 26, 2018
BACKGROUND: ADR is a widely used colonoscopy quality indicator. Calculation of ADR is labor-intensive and cumbersome using current electronic medical databases. Natural language processing (NLP) is a method used to extract meaning from unstructured o...
EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning.
Artificial intelligence in medicine
Apr 23, 2018
OBJECTIVE: Electronic medical records (EMRs) contain medical knowledge that can be used for clinical decision support (CDS). Our objective is to develop a general system that can extract and represent knowledge contained in EMRs to support three CDS ...
DDC-Outlier: Preventing Medication Errors Using Unsupervised Learning.
IEEE journal of biomedical and health informatics
Apr 17, 2018
Electronic health records have brought valuable improvements to hospital practices by integrating patient information. In fact, the understanding of these data can prevent mistakes that may put patients' lives at risk. Nonetheless, to the best of our...
Predicting Hospital Readmission via Cost-Sensitive Deep Learning.
IEEE/ACM transactions on computational biology and bioinformatics
Apr 16, 2018
With increased use of electronic medical records (EMRs), data mining on medical data has great potential to improve the quality of hospital treatment and increase the survival rate of patients. Early readmission prediction enables early intervention,...
Identifying Falls Risk Screenings Not Documented with Administrative Codes Using Natural Language Processing.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
Quality reporting that relies on coded administrative data alone may not completely and accurately depict providers' performance. To assess this concern with a test case, we developed and evaluated a natural language processing (NLP) approach to iden...
Contralateral Breast Cancer Event Detection Using Nature Language Processing.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
To facilitate the identification of contralateral breast cancer events for large cohort study, we proposed and implemented a new method based on features extracted from narrative text in progress notes and features from numbers of pathology reports f...
Predicting Changes in Pediatric Medical Complexity using Large Longitudinal Health Records.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
Medically complex patients consume a disproportionate amount of care resources in hospitals but still often end up with sub-optimal clinical outcomes. Predicting dynamics of complexity in such patients can potentially help improve the quality of care...
Learning Doctors' Medicine Prescription Pattern for Chronic Disease Treatment by Mining Electronic Health Records: A Multi-Task Learning Approach.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
Increasing learning ability from massive medical data and building learning methods robust to data quality issues are key factors toward building data-driven clinical decision support systems for medicine prescription decision support. Here, we attem...
A Multi-Task Framework for Monitoring Health Conditions via Attention-based Recurrent Neural Networks.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
Monitoring the future health status of patients from the historical Electronic Health Record (EHR) is a core research topic in predictive healthcare. The most important challenges are to model the temporality of sequential EHR data and to interpret t...
Leveraging Collaborative Filtering to Accelerate Rare Disease Diagnosis.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
In the USA, rare diseases are defined as those affecting fewer than 200,000 patients at any given time. Patients with rare diseases are frequently misdiagnosed or undiagnosed which may due to the lack of knowledge and experience of care providers. We...
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