AIMC Topic: Humans

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Quantitative assessment of myocardial fibrosis by digital image analysis: An adjunctive tool for pathologist "ground truth".

Cardiovascular pathology : the official journal of the Society for Cardiovascular Pathology
AIMS: Myocardial fibrosis (MF) is a common pathological process in a wide range of cardiovascular diseases. Its quantity has diagnostic and prognostic relevance. We aimed to assess if the complementary use of an automated artificial intelligence soft...

Semi-Automated Data Curation from Biomedical Literature.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Data curation is a bottleneck for many informatics pipelines. A specific example of this is aggregating data from preclinical studies to identify novel genetic pathways for atherosclerosis in humans. This requires extracting data from published mouse...

Deep Learning vs Traditional Models for Predicting Hospital Readmission among Patients with Diabetes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
A hospital readmission risk prediction tool for patients with diabetes based on electronic health record (EHR) data is needed. The optimal modeling approach, however, is unclear. In 2,836,569 encounters of 36,641 diabetes patients, deep learning (DL)...

Enabling Scientific Reproducibility through FAIR Data Management: An ontology-driven deep learning approach in the NeuroBridge Project.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Scientific reproducibility that effectively leverages existing study data is critical to the advancement of research in many disciplines including neuroscience, which uses imaging and electrophysiology modalities as primary endpoints or key dependenc...

Methodological information extraction from randomized controlled trial publications: a pilot study.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Most biomedical information extraction (IE) approaches focus on entity types such as diseases, drugs, and genes, and relations such as gene-disease associations. In this paper, we introduce the task of methodological IE to support fine-grained qualit...

Characterizing Patient Representations for Computational Phenotyping.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Patient representation learning methods create rich representations of complex data and have potential to further advance the development of computational phenotypes (CP). Currently, these methods are either applied to small predefined concept sets o...

Parsable Clinical Trial Eligibility Criteria Representation Using Natural Language Processing.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Successful clinical trials offer better treatments to current or future patients and advance scientific research. Clinical trials define the target population using specific eligibility criteria to ensure an optimal enrollment sample. Clinical trial ...

An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury.

AMIA ... Annual Symposium proceedings. AMIA Symposium
A longstanding challenge surrounding deep learning algorithms is unpacking and understanding how they make their decisions. Explainable Artificial Intelligence (XAI) offers methods to provide explanations of internal functions of algorithms and reaso...

Identifying Barriers to Post-Acute Care Referral and Characterizing Negative Patient Preferences Among Hospitalized Older Adults Using Natural Language Processing.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Our objective was to detect common barriers to post-acute care (B2PAC) among hospitalized older adults using natural language processing (NLP) of clinical notes from patients discharged home when a clinical decision support system recommended post-ac...