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- W1770197690 abstract "This report describes the suicidality prediction models created under the DARPA DCAPS program in association with the Durkheim Project [http://durkheimproject.org/]. The models were built primarily from unstructured text (free-format clinician notes) for several hundred patient records obtained from the Veterans Health Administration (VHA). The models were constructed using a genetic programming algorithm applied to bag-of-words and bag-of-phrases datasets. The influence of additional structured data was explored but was found to be minor. Given the small dataset size, classification between cohorts was high fidelity (98%). Cross-validation suggests these models are reasonably predictive, with an accuracy of 50% to 69% on five rotating folds, with ensemble averages of 58% to 67%. One particularly noteworthy result is that word-pairs can dramatically improve classification accuracy; but this is the case only when one of the words in the pair is already known to have a high predictive value. By contrast, the set of all possible word-pairs does not improve on a simple bag-of-words model." @default.
- W1770197690 created "2016-06-24" @default.
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- W1770197690 date "2013-10-24" @default.
- W1770197690 modified "2023-09-26" @default.
- W1770197690 title "Durkheim Project Data Analysis Report" @default.
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- W1770197690 doi "https://doi.org/10.1371/journal.pone.0085733.s001" @default.
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