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Decision Making

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...

Decision Making: P-value Method

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Avoidance Learning and Learned Helplessness

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...

Self-Evaluation: Self-Enhancement and Self-Verification

Self-Evaluation: Self-Enhancement and Self-Verification

Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...

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Dual Ontology-enhanced Clinical Decision Learning for First-admission Mortality Prediction.

Fangchen Yin, Hu Nie, Xiaorong Pu

IEEE Journal of Biomedical and Health Informatics

|January 21, 2026

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Predicting patient mortality upon first admission is challenging due to limited data. Dual Ontology-enhanced Clinical Decision learning (DOCD) effectively uses clinical knowledge for accurate early mortality prediction, even with single visit records.

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Area of Science:

Artificial Intelligence in Healthcare
Clinical Informatics
Biomedical Data Science

Background:

Deep learning on electronic health records (EHR) excels at predictive healthcare tasks.
Mortality prediction for first-time admissions is difficult due to a lack of historical patient data.
A significant percentage of patients in MIMIC-III and MIMIC-IV have only single visit records, often directly to the ICU.

Purpose of the Study:

To develop an effective model for predicting mortality in patients with no prior visit history.
To leverage clinical knowledge to improve early mortality prediction for first-admission patients.
To address the challenge of limited data in predicting outcomes for critically ill patients upon initial presentation.

Main Methods:

Proposed Dual Ontology-enhanced Clinical Decision learning (DOCD) model.
Utilized dual ontology learning to extract hierarchical representations from diagnosis and procedure taxonomies.
Implemented an a priori-guided attention mechanism with probability-based regularization for knowledge integration.
Employed information fusion to combine demographic data, vital signs, and knowledge-enhanced medical codes.

Main Results:

DOCD achieved superior performance on MIMIC-III (AUROC: 0.9528, AUPRC: 0.8971) and MIMIC-IV (AUROC: 0.9817, AUPRC: 0.8857) datasets.
Demonstrated significant improvement over existing baseline models for first-admission mortality prediction.
Provided interpretable visualizations that align with established clinical knowledge.

Conclusions:

DOCD effectively addresses the challenge of mortality prediction with limited patient history.
The model's integration of clinical knowledge enhances prediction accuracy and interpretability.
DOCD offers a promising approach for timely interventions and improved patient outcomes in critical care settings.