AIMC Topic: Electronic Health Records

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A multi-layer soft lattice based model for Chinese clinical named entity recognition.

BMC medical informatics and decision making
OBJECTIVE: Named entity recognition (NER) is a key and fundamental part of many medical and clinical tasks, including the establishment of a medical knowledge graph, decision-making support, and question answering systems. When extracting entities fr...

Natural Language Processing to Improve Prediction of Incident Atrial Fibrillation Using Electronic Health Records.

Journal of the American Heart Association
Background Models predicting atrial fibrillation (AF) risk, such as Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF), have not performed as well in electronic health records. Natural language processing (NLP) may improve mo...

Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records.

Basic & clinical pharmacology & toxicology
We sought to craft a drug safety signalling pipeline associating latent information in clinical free text with exposures to single drugs and drug pairs. Data arose from 12 secondary and tertiary public hospitals in two Danish regions, comprising appr...

"Note Bloat" impacts deep learning-based NLP models for clinical prediction tasks.

Journal of biomedical informatics
One unintended consequence of the Electronic Health Records (EHR) implementation is the overuse of content-importing technology, such as copy-and-paste, that creates "bloated" notes containing large amounts of textual redundancy. Despite the rising i...

Development of a natural language processing algorithm to extract seizure types and frequencies from the electronic health record.

Seizure
OBJECTIVE: To develop a natural language processing (NLP) algorithm to abstract seizure types and frequencies from electronic health records (EHR).

Identification of Preanesthetic History Elements by a Natural Language Processing Engine.

Anesthesia and analgesia
BACKGROUND: Methods that can automate, support, and streamline the preanesthesia evaluation process may improve resource utilization and efficiency. Natural language processing (NLP) involves the extraction of relevant information from unstructured t...

Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment.

Scientific reports
The Electronic Medical Record (EMR) provides an opportunity to manage patient care efficiently and accurately. This includes clinical decision support tools for the timely identification of adverse events or acute illnesses preceded by deterioration....

Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers.

PloS one
The prevalence of patients who are Incapacitated with No Evident Advance Directives or Surrogates (INEADS) remains unknown because such data are not routinely captured in structured electronic health records. This study sought to develop and validate...

Trustworthy assertion classification through prompting.

Journal of biomedical informatics
Accurate identification of the presence, absence or possibility of relevant entities in clinical notes is important for healthcare professionals to quickly understand crucial clinical information. This introduces the task of assertion classification ...