AIMC Topic: Electronic Health Records

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Natural Language Processing to Quantify Microbial Keratitis Measurements.

Ophthalmology
A natural language processing (NLP) algorithm to extract microbial keratitis morphology measurements from the electronic health record (EHR) was 75-96% sensitive and 91%-96% specific. NLP accurately extracts data from the corneal exam free-text EHR f...

An intelligent warning model for early prediction of cardiac arrest in sepsis patients.

Computer methods and programs in biomedicine
BACKGROUND: Sepsis-associated cardiac arrest is a common issue with the low survival rate. Early prediction of cardiac arrest can provide the time required for intervening and preventing its onset in order to reduce mortality. Several studies have be...

Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.

International journal of medical informatics
BACKGROUND: Last-minute surgery cancellation represents a major wastage of resources and can cause significant inconvenience to patients. Our objectives in this study were: 1) To develop predictive models of last-minute surgery cancellation, utilizin...

Measuring the effect of different types of unsupervised word representations on Medical Named Entity Recognition.

International journal of medical informatics
BACKGROUND: This work deals with Natural Language Processing applied to the clinical domain. Specifically, the work deals with a Medical Entity Recognition (MER) on Electronic Health Records (EHRs). Developing a MER system entailed heavy data preproc...

Statistical supervised meta-ensemble algorithm for medical record linkage.

Journal of biomedical informatics
Identifying unique patients across multiple care facilities or services is a major challenge in providing continuous care and undertaking health research. Identifying and linking patients without compromising privacy and security is an emerging issue...

Supervised methods to extract clinical events from cardiology reports in Italian.

Journal of biomedical informatics
Clinical narratives are a valuable source of information for both patient care and biomedical research. Given the unstructured nature of medical reports, specific automatic techniques are required to extract relevant entities from such texts. In the ...

Deep Sequential Models for Suicidal Ideation From Multiple Source Data.

IEEE journal of biomedical and health informatics
This paper presents a novel method for predicting suicidal ideation from electronic health records (EHR) and ecological momentary assessment (EMA) data using deep sequential models. Both EHR longitudinal data and EMA question forms are defined by asy...

An empirical evaluation of deep learning for ICD-9 code assignment using MIMIC-III clinical notes.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Code assignment is of paramount importance in many levels in modern hospitals, from ensuring accurate billing process to creating a valid record of patient care history. However, the coding process is tedious and subjective,...

Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer.

International journal of medical informatics
BACKGROUND: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through all discharge summaries to ...