AIMC Topic: Humans

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Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Age-related macular degeneration (AMD) is the leading cause of vision loss. Some patients experience vision loss over a delayed timeframe, others at a rapid pace. Physicians analyze time-of-visit fundus photographs to predict patient risk of developi...

Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Despite impressive success of machine learning algorithms in clinical natural language processing (cNLP), rule-based approaches still have a prominent role. In this paper, we introduce medspaCy, an extensible, open-source cNLP library based on spaCy ...

On the explainability of hospitalization prediction on a large COVID-19 patient dataset.

AMIA ... Annual Symposium proceedings. AMIA Symposium
We develop various AI models to predict hospitalization on a large (over 110k) cohort of COVID-19 positive-tested US patients, sourced from March 2020 to February 2021. Models range from Random Forest to Neural Network (NN) and Time Convolutional NN,...

DL4Burn: Burn Surgical Candidacy Prediction using Multimodal Deep Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Burn wounds are most commonly evaluated through visual inspection to determine surgical candidacy, taking into account burn depth and individualized patient factors. This process, though cost effective, is subjective and varies by provider experience...

Hybrid Ensemble-Rule Algorithm for Improved MEDLINE® Sentence Boundary Detection.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Sentence boundary detection (SBD) is a fundamental building block in the Natural Language Processing (NLP) pipeline. Incorrect SBD may impact subsequent processing stages resulting in decreased performance. In well-behaved corpora, a few simple rules...

Characterizing Brain Signals for Epileptic Pre-ictal Signal Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Epilepsy is a kind of neurological disorder characterized by recurrent epileptic seizures. While it is crucial to characterize pre-ictal brain electrical activities, the problem to this day still remains computationally challenging. Using brain signa...

Controversial Trials First: Identifying Disagreement Between Clinical Guidelines and New Evidence.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Clinical guidelines integrate latest evidence to support clinical decision-making. As new research findings are published at an increasing rate, it would be helpful to detect when such results disagree with current guideline recommendations. In this ...

Development and Evaluation of an Automated Approach to Detect Weight Abnormalities in Pediatric Weight Charts.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Inaccurate body weight measures can cause critical safety events in clinical settings as well as hindering utilization of clinical data for retrospective research. This study focused on developing a machine learning-based automated weight abnormality...

Extraction of Active Medications and Adherence Using Natural Language Processing for Glaucoma Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Accuracy of medication data in electronic health records (EHRs) is crucial for patient care and research, but many studies have shown that medication lists frequently contain errors. In contrast, physicians often pay more attention to the clinical no...

Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still have been manually performed by radiologists, which creates huge burnouts and de...