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Supervised Natural Language Processing Classification of Violent Death Narratives: Development and Assessment of a Compact Large Language Model

Supervised Natural Language Processing Classification of Violent Death Narratives: Development and Assessment of a Compact Large Language Model

For example, often when NVDRS abstractors referred to victims and suspects in the report narratives, the abbreviations “v” for victim and “s” for suspect appeared rather than the full word. Abbreviations referring to victims, suspects, police, and gunshot wounds were replaced (Table S1 in Multimedia Appendix 1). Finally, the analysis simulated omitting coroner report text from the training data.

Susan T Parker

JMIR AI 2025;4:e68212

mindLAMPVis as a Co-Designed Clinician-Facing Data Visualization Portal to Integrate Clinical Observations From Digital Phenotyping in Schizophrenia: User-Centered Design Process and Pilot Implementation

mindLAMPVis as a Co-Designed Clinician-Facing Data Visualization Portal to Integrate Clinical Observations From Digital Phenotyping in Schizophrenia: User-Centered Design Process and Pilot Implementation

In our proposed visualization tool, mind LAMPVis, we support the following visualizations (V) of active (A) and passive (P) data: MCA trend (V1 A) of survey data (Figure S1 in Multimedia Appendix 1), MCA eigengap (V2 A) of survey data (Figure S2 in Multimedia Appendix 1), Date-clustering (V3 A) of survey data (Figure 1), Home time (V1 P) of significant locations data (Figure 2), Significant location (V2 P) of significant locations data (Figure 2).

Karthik Sama, Jaya Sreevalsan-Nair, Soumya Choudhary, Srilakshmi Nagendra, Preethi V Reddy, Asher Cohen, Urvakhsh Meherwan Mehta, John Torous

JMIR Form Res 2025;9:e70073