AIMC Topic: Electronic Health Records
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"Note Bloat" impacts deep learning-based NLP models for clinical prediction tasks.
Journal of biomedical informatics
Jul 22, 2022
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
Jul 20, 2022
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
Jul 15, 2022
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
Jul 11, 2022
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
Jul 11, 2022
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
Jul 8, 2022
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 ...
Electronic Health Record-Based Deep Learning Prediction of Death or Severe Decompensation in Heart Failure Patients.
JACC. Heart failure
Jul 6, 2022
BACKGROUND: Surgical mechanical ventricular assistance and cardiac replacement therapies, although life-saving in many heart failure (HF) patients, remain high-risk. Despite this, the difficulty in timely identification of medical therapy nonresponde...
Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system.
Scientific reports
Jul 5, 2022
Postoperative patients are at risk of life-threatening complications such as hemodynamic decompensation or arrhythmia. Automated detection of patients with such risks via a real-time clinical decision support system may provide opportunities for earl...
SiBERT: A Siamese-based BERT network for Chinese medical entities alignment.
Methods (San Diego, Calif.)
Jul 4, 2022
Entity alignment aims at associating semantically similar entities in knowledge graphs from different sources. It is widely used in the integration and construction of professional medical knowledge. The existing deep learning methods lack term-level...
An Explainable Transformer-Based Deep Learning Model for the Prediction of Incident Heart Failure.
IEEE journal of biomedical and health informatics
Jul 1, 2022
Predicting the incidence of complex chronic conditions such as heart failure is challenging. Deep learning models applied to rich electronic health records may improve prediction but remain unexplainable hampering their wider use in medical practice....
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