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Can machine learning identify childhood characteristics that predict future development of bipolar disorder a decade later? - ScienceDirect
Source: sciencedirect.com.https.hebutlib.proxy.hebut.edu.cn
Article summary:
1. This study examined whether machine learning algorithms can be used to predict the development of bipolar disorder in children.
2. The study found that the Balanced Random Forest algorithm was able to predict subsequent bipolar disorder with 75% sensitivity, 76% specificity, and an Area Under the Receiver Operating Characteristics of 75%.
3. Predictors best differentiating between children who did or did not develop bipolar disorder were the Child Behavioral Checklist Externalizing and Internalizing behaviors, the Child Behavioral Checklist Total t-score, problematic school functions indexed through the Child Behavioral Checklist School Competence scale, and the Child Behavioral Checklist Anxiety/Depression and Aggression scales.
Article analysis:
The article is generally reliable and trustworthy as it provides a detailed description of a study conducted to examine whether machine learning algorithms can be used to predict the development of bipolar disorder in children. The article is well-structured and provides clear evidence for its claims. It also includes a comprehensive discussion section which outlines potential limitations of the study as well as implications for future research.
However, there are some potential biases that should be noted. Firstly, the sample size of 492 participants may not be large enough to accurately represent all children with emergent psychopathology referred to clinical practice. Secondly, there is no mention of any control group in this study which could have provided further insight into how effective machine learning algorithms are at predicting bipolar disorder compared to other methods such as traditional statistical models or clinical judgement alone. Finally, there is no mention of any ethical considerations taken when conducting this research which could have implications for how data was collected from participants and their families.