AIMC Topic: Clinical Coding
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Computer-Assisted Diagnostic Coding: Effectiveness of an NLP-based approach using SNOMED CT to ICD-10 mappings.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Dec 5, 2018
Computer-assisted (diagnostic) coding (CAC) aims to improve the operational productivity and accuracy of clinical coders. The level of accuracy, especially for a wide range of complex and less prevalent clinical cases, remains an open research proble...
Towards automated clinical coding.
International journal of medical informatics
Oct 2, 2018
BACKGROUND: Patients' encounters with healthcare services must undergo clinical coding. These codes are typically derived from free-text notes. Manual clinical coding is expensive, time-consuming and prone to error. Automated clinical coding systems ...
Prediction task guided representation learning of medical codes in EHR.
Journal of biomedical informatics
Jun 19, 2018
There have been rapidly growing applications using machine learning models for predictive analytics in Electronic Health Records (EHR) to improve the quality of hospital services and the efficiency of healthcare resource utilization. A fundamental an...
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...
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...
An ontological approach to identifying cases of chronic kidney disease from routine primary care data: a cross-sectional study.
BMC nephrology
Apr 10, 2018
BACKGROUND: Accurately identifying cases of chronic kidney disease (CKD) from primary care data facilitates the management of patients, and is vital for surveillance and research purposes. Ontologies provide a systematic and transparent basis for cli...
Automated ICD-9 Coding via A Deep Learning Approach.
IEEE/ACM transactions on computational biology and bioinformatics
Mar 20, 2018
ICD-9 (the Ninth Revision of International Classification of Diseases) is widely used to describe a patient's diagnosis. Accurate automated ICD-9 coding is important because manual coding is expensive, time-consuming, and inefficient. Inspired by the...
Deep neural models for ICD-10 coding of death certificates and autopsy reports in free-text.
Journal of biomedical informatics
Feb 26, 2018
We address the assignment of ICD-10 codes for causes of death by analyzing free-text descriptions in death certificates, together with the associated autopsy reports and clinical bulletins, from the Portuguese Ministry of Health. We leverage a deep n...
Assigning clinical codes with data-driven concept representation on Dutch clinical free text.
Journal of biomedical informatics
Apr 8, 2017
Clinical codes are used for public reporting purposes, are fundamental to determining public financing for hospitals, and form the basis for reimbursement claims to insurance providers. They are assigned to a patient stay to reflect the diagnosis and...
Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals.
Journal of the American Medical Informatics Association : JAMIA
Aug 7, 2016
OBJECTIVE: Phenotyping algorithms applied to electronic health record (EHR) data enable investigators to identify large cohorts for clinical and genomic research. Algorithm development is often iterative, depends on fallible investigator intuition, a...
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