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MENTOR: a Bayesian Model for prediction of mental retardation in newborns
S Mani1, S McDermott, M Valtorta
1Department of Information and Computer Science, University of California at Irvine 92697, USA.
Abstract:
Mental retardation (MR) is a diagnosis that is made with extreme caution because of the many uncertainties in its etiology and prognosis. In fact, most physicians will delay the diagnosis for months or years so that substantial evidence is available to rule the diagnosis in or out. MENTOR is a Bayesian Model for the prediction of MR in newborns that provides probabilities for the full range of cognitive outcomes, ranging from MR to superior intelligence. Using the model to confirm clinical judgment could help physicians decide when to proceed with diagnostic tests. The physician and family could discuss the probabilities for MR, borderline, normal, and superior intelligence, given the child's status in infancy and base their decision about additional testing, in part, on this information.
Insights
This study introduces MENTOR, a Bayesian model to predict cognitive outcomes in newborns, aiding in the early diagnosis of mental retardation (MR). The model helps physicians and families decide on further diagnostic testing by providing probabilities for various intelligence levels.
Area of Science:
- Pediatric neurology
- Developmental psychology
- Medical artificial intelligence
Background:
- Diagnosing mental retardation (MR) in newborns is challenging due to etiological and prognostic uncertainties.
- Physicians often delay diagnosis pending substantial evidence, impacting timely intervention.
- Predicting cognitive outcomes in newborns requires careful consideration of developmental trajectories.
Purpose of the Study:
- To introduce MENTOR, a Bayesian model for predicting cognitive outcomes in newborns.
- To provide probabilities for a spectrum of cognitive abilities, from MR to superior intelligence.
- To assist clinicians in confirming judgment and guiding decisions on diagnostic testing.
Main Methods:
- Development of a Bayesian Model (MENTOR) for probabilistic prediction of cognitive outcomes.
- Utilizing infant status data to generate predictive probabilities.
- Integrating model predictions with clinical judgment for diagnostic decision-making.
Main Results:
- The MENTOR model offers probabilities for cognitive outcomes, including mental retardation, borderline, normal, and superior intelligence.
- The model can support clinical judgment in the diagnostic process for newborns.
- Probabilistic outputs facilitate informed discussions between physicians and families regarding further testing.
Conclusions:
- MENTOR provides a quantitative tool to aid in the prediction of cognitive development in newborns.
- The model can help reduce diagnostic uncertainty and inform the need for further investigations.
- This approach supports evidence-based decision-making for early identification of intellectual disabilities.