Scientific Literature Identifying clinical diagnostic trajectories associated with suicide death using temporal sequence mining of linked claims and mortality data
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Data-driven prediction of fifteen-year all-cause mortality among 2.3 million individuals in the VA
ABSTRACT We present a data-driven framework to predict 15-year all-cause mortality using outpatient administrative records for 2.3 million Veterans in the largest integrated U.S. healthcare system....
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Frequently Asked Questions (FAQ)
Curated market intelligence mapped to this research.
What is the core focus of the research titled 'Identifying clinical diagnostic trajectories associated with suicide death using temporal sequence mining of linked claims and mortality data'?
This literature focuses on: Most approaches to suicide risk assessment consider clinical conditions as independent risk factors, potentially overlooking prognostic information in the order in which conditions accumulate. We applied temporal sequence mining to linked claims a...
What other academic literature is closely related to 'Identifying clinical diagnostic trajectories associated with suicide death using temporal sequence mining of linked claims and mortality data'?
Yes, highly correlated activity was mapped. An entry titled 'Data-driven prediction of fifteen-year all-cause mortality among 2.3 million individuals in the VA' discusses this: ABSTRACT We present a data-driven framework to predict 15-year all-cause mortality using outpatient administrative records for 2.3 million Veterans...
Are there commercial applications of 'Identifying clinical diagnostic trajectories associated with suicide death using temporal sequence mining of linked claims and mortality data' in market news publications?
Yes, highly correlated activity was mapped. An entry titled 'Curse Of Dimensionality' discusses this: Solutions for managing high-dimensional data are advancing, with new methodologies for causal mediation analysis in biomedical data and dynamic fin...
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