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J Philip Miller

Publications and source records attributed to J Philip Miller.

2 recordsLinked to original sources
PubMed2026

Perioperative Depression and Anxiety Care in Older Patients: A Randomized Clinical Trial.

IMPORTANCE: Depression and anxiety are common among older adults undergoing surgery and are associated with adverse postoperative outcomes. However, effective tailored perioperative mental health interventions are lacking. OBJECTIVE: To evaluate a perioperative intervention to optimize mental health. DESIGN, SETTING, AND PARTICIPANTS: A single-blind, hybrid, type 1, effectiveness-implementation randomized clinical trial was conducted (November 1, 2022, to March 31, 2025), with 3-month postoperative follow-up, at a US academic and community practice hospital network. Participants were 60 years or older; scheduled for cardiac, oncologic, or orthopedic surgery; and had clinically meaningful symptoms of depression and/or anxiety based on the Patient Health Questionnaire-Anxiety and Depressive Symptom (PHQ-ADS) scale. A total of 3159 patients were screened for eligibility, with 1518 ineligible, 1079 declining participation, and 236 excluded for other reasons. A total of 326 patients were enrolled and randomized (1:1), with 20 excluded after surgery cancelation. INTERVENTION: Participants were assigned to receive a perioperative intervention combining psychological management and pharmacologic optimization or enhanced usual care (materials for self-managing symptoms). MAIN OUTCOMES AND MEASURES: The primary outcome was change in PHQ-ADS score from baseline to 3 months after surgery. Other outcomes included persistent postsurgical pain, delirium, falls, quality of life, patient satisfaction, length of stay, and rehospitalizations. Implementability was evaluated through semistructured interviews and reach, acceptability, feasibility, appropriateness, and fidelity measures. RESULTS: A total of 306 older adults were included in analysis (mean [SD] age, 68.5 [6.1] years; 209 [68.3%] female; 153 randomized to intervention and 153 randomized to enhanced usual care): 102 cardiac, 100 oncologic, and 104 orthopedic patients. Participants' mean (SD) baseline PHQ-ADS score was 18.5 (7.4). At 3 months, there was a significant decrease in PHQ-ADS scores in the intervention group compared with the enhanced usual care group (mean difference, 2.20; 95% CI, 0.16-4.24; P = .03). Effects varied by surgical subgroups (oncologic patients: mean difference, 4.93; 95% CI, 1.51-8.36; P = .005; cardiac patients: mean difference, 2.68; 95% CI, -0.98 to 6.35; P = .15; and orthopedic patients: mean difference, -1.11; 95% CI, -4.62 to 2.40; P = .54). Patients and interventionists perceived the intervention as appropriate, with high-fidelity delivery and broad reach across the target population. CONCLUSIONS AND RELEVANCE: In this randomized clinical trial, psychological management and pharmacologic optimization reduced anxiety and depression in older adults undergoing surgery. Future studies should assess reproducibility and determine which patients benefit most. TRIAL REGISTRATION: ClinicalTrials.gov Identifiers: NCT05575128, NCT05685511, and NCT05697835.

Humans↗
PubMed2025

BioMedGraphica: An All-in-One Platform for Joint Textual Biomedical Prior Knowledge and Numeric Graph Generation.

Multi-omic data analysis is essential for scientific discovery in precision medicine. However, translating statistical results of omic data analysis into novel scientific hypothesis remains a significant challenge. Human experts must manually review analysis results and generate new hypothesis based on extensive and inter-connected biomedical prior knowledge, which is subjective and not scalable. While large language models (LLMs) can accelerate the discovery, their reasoning improves when grounded in structured, auditable and comprehensive biomedical prior knowledge. Biomedical knowledge, however, is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of AI systems to fully leverage biomedical data for scientific discovery. To address these challenges, we developed BioMedGraphica , an all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified knowledge graph containing 2,306,921 entities and 27,232,091 relations. In addition, to the best of our knowledge, this is the first work to propose a novel Textual-Numeric Graph (TNG) data-structure for multi-omics data analysis. In TNG, textual information captures prior biological knowledge (e.g., transcription start sites, functions, mechanisms), while numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data-structure for the development of graph foundation models, with the potential to improve prediction performance and interpretability, while also augmenting LLMs by supplying graph-structured mechanistic context to strengthen reasoning. The details for BioMedGraphica code can be accessed by github link: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph data can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

biomedical knowledge graph↗