AIMC Topic: Natural Language Processing

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The Language of Innovation.

PloS one
Predicting innovation is a peculiar problem in data science. Following its definition, an innovation is always a never-seen-before event, leaving no room for traditional supervised learning approaches. Here we propose a strategy to address the proble...

Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients.

BMC medical informatics and decision making
BACKGROUND: Automated de-identification methods for removing protected health information (PHI) from the source notes of the electronic health record (EHR) rely on building systems to recognize mentions of PHI in text, but they remain inadequate at e...

Rape narratives analysis through natural language processing: Survivor self-label, narrative time span, faith, and rape terminology.

Psychological trauma : theory, research, practice and policy
OBJECTIVE: Rape-survivor identity is a sign of recovery and positive therapeutic progress among rape victims. This study is one of the few to focus on factors predicting self-labeling as a survivor among self-acknowledged rape victims by evaluating t...

Comparison of deep learning models for natural language processing-based classification of non-English head CT reports.

Neuroradiology
PURPOSE: Natural language processing (NLP) can be used for automatic flagging of radiology reports. We assessed deep learning models for classifying non-English head CT reports.

Finding warning markers: Leveraging natural language processing and machine learning technologies to detect risk of school violence.

International journal of medical informatics
INTRODUCTION: School violence has a far-reaching effect, impacting the entire school population including staff, students and their families. Among youth attending the most violent schools, studies have reported higher dropout rates, poor school atte...

Emergency department disposition prediction using a deep neural network with integrated clinical narratives and structured data.

International journal of medical informatics
BACKGROUND: Emergency department (ED) overcrowding has been a serious issue and demands effective clinical decision-making of patient disposition. In previous studies, emergency clinical narratives provide a rich context for clinical decisions. We ai...

Natural Language Generation Model for Mammography Reports Simulation.

IEEE journal of biomedical and health informatics
Extending the size of labeled corpora of medical reports is a major step towards a successful training of machine learning algorithms. Simulating new text reports is a key solution for reports augmentation, which extends the cohort size. However, tex...

Artificial Intelligence and Digital Tools: Future of Diabetes Care.

Clinics in geriatric medicine
Diabetes mellitus has become a global threat, especially in the emerging economies. In the United States, there are about 24 million people with diabetes mellitus. Diabetes represents a trove of physiologic and sociologic data that are only superfici...

Neural negated entity recognition in Spanish electronic health records.

Journal of biomedical informatics
This work deals with negation detection in the context of clinical texts. Negation detection is a key for decision support systems since negated events (detection of absence of some events) help ascertain current medical conditions. For artificial in...

Development of machine learning and natural language processing algorithms for preoperative prediction and automated identification of intraoperative vascular injury in anterior lumbar spine surgery.

The spine journal : official journal of the North American Spine Society
BACKGROUND: Intraoperative vascular injury (VI) may be an unavoidable complication of anterior lumbar spine surgery; however, vascular injury has implications for quality and safety reporting as this intraoperative complication may result in serious ...