AIMC Topic: Humans

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Weak Supervision for Affordable Modeling of Electrocardiogram Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Analysing electrocardiograms (ECGs) is an inexpensive and non-invasive, yet powerful way to diagnose heart disease. ECG studies using Machine Learning to automatically detect abnormal heartbeats so far depend on large, manually annotated datasets. Wh...

Using Machine Learning to Support Transfer of Best Practices in Healthcare.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The adoption of best practices has been shown to increase performance in healthcare institutions and is consistently demanded by both patients, payers, and external overseers. Nevertheless, transferring practices between healthcare organizations is a...

Parsing Immune Correlates of Protection Against SARS-CoV-2 from Biomedical Literature.

AMIA ... Annual Symposium proceedings. AMIA Symposium
After the emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in 2019, identification of immune correlates of protection (CoPs) have become increasingly important to understand the immune response to SARS-CoV-2. The vast amount ...

Predictive and Causal Analysis of No-Shows for Medical Exams During COVID-19: A Case Study of Breast Imaging in a Nationwide Israeli Health Organization.

AMIA ... Annual Symposium proceedings. AMIA Symposium
"No-shows", defined as missed appointments or late cancellations, is a central problem in healthcare systems. It has appeared to intensify during the COVID-19 pandemic and the nonpharmaceutical interventions, such as closures, taken to slow its sprea...

Comparing Deep Learning and Conventional Machine Learning Models for Predicting Mental Illness from History of Present Illness Notations.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Mental illness, a serious problem across the globe, requires multi-pronged solutions including effective computational models to predict illness. Mental illness diagnosis is complicated by the pronounced sharing of symptoms and mutual pre-disposition...

Machine Learning Predictability of Clinical Next Generation Sequencing for Hematologic Malignancies to Guide High-Value Precision Medicine.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Advancing diagnostic testing capabilities such as clinical next generation sequencing methods offer the potential to diagnose, risk stratify, and guide specialized treatment, but must be balanced against the escalating costs of healthcare to identify...

Bias Assessment and Correction in Machine Learning Algorithms: A Use-Case in a Natural Language Processing Algorithm to Identify Hospitalized Patients with Unhealthy Alcohol Use.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Unhealthy alcohol use represents a major economic burden and cause of morbidity and mortality in the United States. Implementation of interventions for unhealthy alcohol use depends on the availability and accuracy of screening tools. Our group previ...

Semantic Search for Large Scale Clinical Ontologies.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Finding concepts in large clinical ontologies can be challenging when queries use different vocabularies. A search algorithm that overcomes this problem is useful in applications such as concept normalisation and ontology matching, where concepts can...

Word Embedding and Clustering for Patient-Centered Redesign of Appointment Scheduling in Ambulatory Care Settings.

AMIA ... Annual Symposium proceedings. AMIA Symposium
. A key to a more efficient scheduling systems is to ensure appointments are designed to meet patient's needs and to design and simplify appointment scheduling less prone to error. Electronic Health Records (EHR) consist of valuable information about...