AIMC Topic: Humans

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Data and Model Biases in Social Media Analyses: A Case Study of COVID-19 Tweets.

AMIA ... Annual Symposium proceedings. AMIA Symposium
During the coronavirus disease pandemic (COVID-19), social media platforms such as Twitter have become a venue for individuals, health professionals, and government agencies to share COVID-19 information. Twitter has been a popular source of data for...

Predicting Motor Responsiveness to Deep Brain Stimulation with Machine Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Deep brain stimulation is a complex movement disorder intervention that requires highly invasive brain surgery. Clinicians struggle to predict how patients will respond to this treatment. To address this problem, we are working toward developing a cl...

Impact of Clinical and Genomic Factors on COVID-19 Disease Severity.

AMIA ... Annual Symposium proceedings. AMIA Symposium
To date, there have been 180 million confirmed cases of COVID-19, with more than 3.8 million deaths, reported to WHO worldwide. In this paper we address the problem of understanding the host genome's influence, in concert with clinical variables, on ...

Prediction of Resuscitation for Pediatric Sepsis from Data Available at Triage.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Pediatric sepsis imposes a significant burden of morbidity and mortality among children. While the speedy application of existing supportive care measures can substantially improve outcomes, further improvements in delivering that care require tools ...

Towards more patient friendly clinical notes through language models and ontologies.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Clinical notes are an efficient way to record patient information but are notoriously hard to decipher for non-experts. Automatically simplifying medical text can empower patients with valuable information about their health, while saving clinicians ...

On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Early detection and mitigation of disease recurrence in non-small cell lung cancer (NSCLC) patients is a nontrivial problem that is typically addressed either by rather generic follow-up screening guidelines, self-reporting, simple nomograms, or by m...

Understanding Heart Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Heart failure (HF) is a major cause of mortality. Accurately monitoring HF progress and adjusting therapies are critical for improving patient outcomes. An experienced cardiologist can make accurate HF stage diagnoses based on combination of symptoms...

State of the Art Causal Inference in the Presence of Extraneous Covariates: A Simulation Study.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The central task of causal inference is to remove (via statistical adjustment) confounding bias that would be present in naive unadjusted comparisons of outcomes in different treatment groups. Statistical adjustment can roughly be broken down into tw...

A Federated Mining Approach on Predicting Diabetes-Related Complications: Demonstration Using Real-World Clinical Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Chronic diabetes can lead to microvascular complications, including diabetic eye disease, diabetic kidney disease, and diabetic neuropathy. However, the long-term complications often remain undetected at the early stages of diagnosis. Developing a ma...

Practical Perfusion Quantification in Multispectral Endoscopic Video: Using the Minutes after ICG Administration to Assess Tissue Pathology.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The wide availability of near infrared light sources in interventional medical imaging stacks enables non-invasive quantification of perfusion by using fluorescent dyes, typically Indocyanine Green (ICG). Due to their often leaky and chaotic vasculat...