AIMC Topic: Natural Language Processing

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Assessing stroke severity using electronic health record data: a machine learning approach.

BMC medical informatics and decision making
BACKGROUND: Stroke severity is an important predictor of patient outcomes and is commonly measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these scores are often recorded as free text in physician reports, structur...

Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study.

Internal and emergency medicine
Length of stay (LOS) and discharge destination predictions are key parts of the discharge planning process for general medical hospital inpatients. It is possible that machine learning, using natural language processing, may be able to assist with ac...

The implementation of natural language processing to extract index lesions from breast magnetic resonance imaging reports.

BMC medical informatics and decision making
BACKGROUND: There are often multiple lesions in breast magnetic resonance imaging (MRI) reports and radiologists usually focus on describing the index lesion that is most crucial to clinicians in determining the management and prognosis of patients. ...

Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department.

BMC medical informatics and decision making
OBJECTIVE: To examine the association between the medical imaging utilization and information related to patients' socioeconomic, demographic and clinical factors during the patients' ED visits; and to develop predictive models using these associated...

Family history information extraction via deep joint learning.

BMC medical informatics and decision making
Family history (FH) information, including family members, side of family of family members (i.e., maternal or paternal), living status of family members, observations (diseases) of family members, etc., is very important in the decision-making proce...

Family member information extraction via neural sequence labeling models with different tag schemes.

BMC medical informatics and decision making
BACKGROUND: Family history information (FHI) described in unstructured electronic health records (EHRs) is a valuable information source for patient care and scientific researches. Since FHI is usually described in the format of free text, the entire...

Discovering nuclear targeting signal sequence through protein language learning and multivariate analysis.

Analytical biochemistry
Nuclear localization signals (NLSs) are peptides that target proteins to the nucleus by binding to carrier proteins in the cytoplasm that transport their cargo across the nuclear membrane. Accurate identification of NLSs can help elucidate the functi...

Artificial intelligence approaches using natural language processing to advance EHR-based clinical research.

The Journal of allergy and clinical immunology
The wide adoption of electronic health record systems in health care generates big real-world data that open new venues to conduct clinical research. As a large amount of valuable clinical information is locked in clinical narratives, natural languag...

Natural language processing for automated detection of incidental durotomy.

The spine journal : official journal of the North American Spine Society
BACKGROUND: Incidental durotomy is a common intraoperative complication during spine surgery with potential implications for postoperative recovery, patient-reported outcomes, length of stay, and costs. To our knowledge, there are no processes availa...