Predictive modeling for classification of positive valence system symptom severity from initial psychiatric evaluation records.

@article{Posada2017PredictiveMF,
  title={Predictive modeling for classification of positive valence system symptom severity from initial psychiatric evaluation records.},
  author={Jos{\'e} D. Posada and Amie J. Barda and Lingyun Shi and Diyang Xue and Victor M. Ruiz and Pei-Han Kuan and Neal D. Ryan and Fuchiang R Tsui},
  journal={Journal of biomedical informatics},
  year={2017},
  volume={75S},
  pages={
          S94-S104
        }
}

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References

SHOWING 1-10 OF 23 REFERENCES
Predicting early psychiatric readmission with natural language processing of narrative discharge summaries
TLDR
Prediction of readmission, as measured by area under receiver-operating characteristic curves in the testing data set, was improved and inclusion of topics derived from narrative notes allows more accurate discrimination of individuals at high risk for psychiatric readmission in this cohort.
Toward the future of psychiatric diagnosis: the seven pillars of RDoC
TLDR
The rationale, status and long-term goals of RDoC are summarized, challenges in developing a research classification system are outlined, and seven distinct differences in conception and emphasis from current psychiatric nosologies are discussed.
Abnormal reward functioning across substance use disorders and major depressive disorder: Considering reward as a transdiagnostic mechanism.
  • A. Baskin-Sommers, D. Foti
  • Psychology, Biology
    International journal of psychophysiology : official journal of the International Organization of Psychophysiology
  • 2015
Text mining applications in psychiatry: a systematic literature review
TLDR
Text mining approaches are becoming essential to facilitate the automated extraction of useful biomedical information from unstructured text, and it is demonstrated that TM can contribute to complex research tasks in psychiatry.
Application of the Research Domain Criteria (RDoC) Framework to Eating Disorders: Emerging Concepts and Research
TLDR
Findings from R doC-informed studies across the five domains of functioning included in the RDoC matrix are reviewed and directions for future research utilizing R doctrine to enhance study design and treatment development in eating disorders are described.
Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications
TLDR
The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text, and its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations.
Cognitive Control and Negative and Positive Valence Systems in the Development of an NIMH RDoC-Based Model for Alcohol Use Disorder.
TLDR
A dimensional RDoC-based model focused on the cognitive control of drinking that is currently working to develop a Research Domain Criteria for Abstinence, Control, and Impaired Control over Alcohol Use is developed, which includes elements that resemble Koob and Le Moal’s (2001) view of positive and negative reinforcement.
Exercise is an effective treatment for positive valence symptoms in major depression.
...
1
2
3
...