← Back to Research Radar
Scientific Literature Scientific Literature

Identifying clinical diagnostic trajectories associated with suicide death using temporal sequence mining of linked claims and mortality data

Anas Belouali, Christopher Kitchen, Emily Haroz, Harold Lehmann, Paul Sasha Nestadt, Holly C. Wilcox, Hadi H.K. Kharrazi
October 6, 2026
Published Date

Research Abstract & Technology Focus

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 and mortality data to identify ordered clinical diagnostic trajectories associated with suicide death. The cohort included 3 647 059 insured Maryland residents aged 10 years or older with available claims records in the Maryland Suicide Data Warehouse from January 1, 2016, to December 31, 2020, among whom 768 suicide deaths were ascertained through medical examiner linkage. Sequential pattern mining of ICD-10-CM diagnoses grouped into Clinical Classifications Software Refined categories identified 89 221 candidate sequences, of which 1 816 remained significantly associated with suicide death in time-varying Cox models. Adjusted hazard ratios (AHRs) ranged from 2.4 to 134.1. Half of significant trajectories ended in physical conditions, and 93.7% of these began with a psychiatric diagnosis. Among suicide decedents, 62% were exposed to at least 1 significant sequence (median, 16 per case); median sequence duration was 18.7 months, and median time from completion to death was 13.1 months. In landmark analyses, among patients with depression who later developed suicidal ideation ( n = 26 356), the path through anxiety, then anemia, was associated with a higher hazard of subsequent suicide death than other paths from depression to suicidal ideation (AHR, 4.6; 95% CI, 2.2–9.5), whereas the anxiety-only path was not (AHR, 1.3; 95% CI, 0.8–2.1). Among patients with anxiety who later developed hypertension ( n = 149 215), the path through history of self-harm was associated with a higher hazard of subsequent suicide death than other paths from anxiety to hypertension (AHR, 32.0; 95% CI, 16.6–61.6). Temporal ordering of clinical conditions may carry prognostic information for suicide death. Clinical trajectories incorporating physical illness within psychiatric sequences identified higher-risk groups. These findings suggest that opportunities for risk detection may extend beyond psychiatric settings and that suicide risk signals may be fragmented across care settings and not apparent within isolated encounters.
Read Full Literature

Correlated Market Trend: Computer Science

Bridging academia to market: The 60-day public search velocity mapping directly to the core technology of this paper. Dashed line represents 7-day moving average.

AI Semantic Synergy Context

Connecting this academic literature to real-world market discussions and products.

openalex.org › research concept
0%

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....

roipad.com › narrative analysis
0%

Curse Of Dimensionality

Solutions for managing high-dimensional data are advancing, with new methodologies for causal mediation analysis in biomedical data and dynamic financial risk networks. Concurrently, vector search ...

crossref.org › academic paper
0%

Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning

Abstract Background Sepsis is a severe form of systemic inflammatory response syndrome that is caused by infection. Sepsis is characterized by a marked state of stre...

roipad.com › trend story
0%

AI finds hidden ECG signal that predicts sudden cardiac death risk

A new model flags people at high risk of sudden cardiac death from a routine ECG—and reveals a warning sign in the heart’s electrical activity

crossref.org › academic paper
0%

Comparative study on the predictive value of TG/HDL-C, TyG and TyG-BMI indices for 5-year mortality in critically ill patients with chronic heart failure: a retrospective study

Abstract Background The triglyceride glucose (TyG) index, TyG-body mass index (TyG-BMI), and triglyceride-density lipoprotein cholesterol ratio (TG/HDL-C) are substi...

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...

Cite this Market Intelligence Report

Reference our AI-mapped synergy between this research and the commercial market to instantly build authority.