AIMC Topic: Machine Learning

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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 ...

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...

Neural gradient boosting in federated learning for hemodynamic instability prediction: towards a distributed and scalable deep learning-based solution.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Federated learning (FL) is a privacy preserving approach to learning that overcome issues related to data access, privacy, and security, which represent key challenges in the healthcare sector. FL enables hospitals to collaboratively learn a shared p...

HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Developing clinical natural language systems based on machine learning and deep learning is dependent on the availability of large-scale annotated clinical text datasets, most of which are time-consuming to create and not publicly available. The lack...

It's Not Only What You Say, But Also How You Say It: Machine Learning Approach to Estimate Trust from Conversation.

Human factors
OBJECTIVE: The objective of this study was to estimate trust from conversations using both lexical and acoustic data.

From modern CNNs to vision transformers: Assessing the performance, robustness, and classification strategies of deep learning models in histopathology.

Medical image analysis
While machine learning is currently transforming the field of histopathology, the domain lacks a comprehensive evaluation of state-of-the-art models based on essential but complementary quality requirements beyond a mere classification accuracy. In o...

Machine learning based dynamic consensus model for predicting blood-brain barrier permeability.

Computers in biology and medicine
The blood-brain barrier (BBB) is an important defence mechanism that restricts disease-causing pathogens and toxins to enter the brain from the bloodstream. In recent years, many in silico methods were proposed for predicting BBB permeability, howeve...

Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery.

Environmental science and pollution research international
Despite the substantial impact of rivers on the global marine litter problem, riverine litter has been accorded inadequate consideration. Therefore, our objective was to detect riverine litter by utilizing middle-scale multispectral satellite images ...