Mining on Alzheimer's diseases related knowledge graph to identity potential AD-related semantic triples for drug repurposing.

BACKGROUND: To date, there are no effective treatments for most neurodegenerative diseases. Knowledge graphs can provide comprehensive and semantic representation for heterogeneous data, and have been successfully leveraged in many biomedical applications including drug repurposing. Our objective is to construct a knowledge graph from literature to study the relations between Alzheimer's disease (AD) and chemicals, drugs and dietary supplements in order to identify opportunities to prevent or delay neurodegenerative progression. We collected biomedical annotations and extracted their relations using SemRep via SemMedDB. We used both a BERT-based classifier and rule-based methods during data preprocessing to exclude noise while preserving most AD-related semantic triples. The 1,672,110 filtered triples were used to train with knowledge graph completion algorithms (i.e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention. RESULTS: Among three knowledge graph completion models, TransE outperformed the other two (MR = 10.53, Hits@1 = 0.28). We leveraged the time-slicing technique to further evaluate the prediction results. We found supporting evidence for most highly ranked candidates predicted by our model which indicates that our approach can inform reliable new knowledge. CONCLUSION: This paper shows that our graph mining model can predict reliable new relationships between AD and other entities (i.e., dietary supplements, chemicals, and drugs). The knowledge graph constructed can facilitate data-driven knowledge discoveries and the generation of novel hypotheses.

RefAI: a GPT-powered retrieval-augmented generative tool for biomedical literature recommendation and summarization.

OBJECTIVES: Precise literature recommendation and summarization are crucial for biomedical professionals. While the latest iteration of generative pretrained transformer (GPT) incorporates 2 distinct modes-real-time search and pretrained model utilization-it encounters challenges in dealing with these tasks. Specifically, the real-time search can pinpoint some relevant articles but occasionally provides fabricated papers, whereas the pretrained model excels in generating well-structured summaries but struggles to cite specific sources. In response, this study introduces RefAI, an innovative retrieval-augmented generative tool designed to synergize the strengths of large language models (LLMs) while overcoming their limitations. MATERIALS AND METHODS: RefAI utilized PubMed for systematic literature retrieval, employed a novel multivariable algorithm for article recommendation, and leveraged GPT-4 turbo for summarization. Ten queries under 2 prevalent topics ("cancer immunotherapy and target therapy" and "LLMs in medicine") were chosen as use cases and 3 established counterparts (ChatGPT-4, ScholarAI, and Gemini) as our baselines. The evaluation was conducted by 10 domain experts through standard statistical analyses for performance comparison. RESULTS: The overall performance of RefAI surpassed that of the baselines across 5 evaluated dimensions-relevance and quality for literature recommendation, accuracy, comprehensiveness, and reference integration for summarization, with the majority exhibiting statistically significant improvements (P-values <.05). DISCUSSION: RefAI demonstrated substantial improvements in literature recommendation and summarization over existing tools, addressing issues like fabricated papers, metadata inaccuracies, restricted recommendations, and poor reference integration. CONCLUSION: By augmenting LLM with external resources and a novel ranking algorithm, RefAI is uniquely capable of recommending high-quality literature and generating well-structured summaries, holding the potential to meet the critical needs of biomedical professionals in navigating and synthesizing vast amounts of scientific literature.

Dynamic Prognosis Prediction for Patients on DAPT After Drug-Eluting Stent Implantation: Model Development and Validation.

BACKGROUND: The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug-eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. METHODS AND RESULTS: We developed and validated a new AI-based pipeline using retrospective data of drug-eluting stent-treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum's de-identified Clinformatics Data Mart Database (n=9978). The 36 months following drug-eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI-DAPT model. The AI-DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%-92%] for ischemia and 84% [95% CI, 82%-87%] for bleeding predictions. CONCLUSIONS: Our AI-DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients' clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability.

Associations of Antihypertensive Medication Consumption and Drug-Drug Interaction with Statin and Metformin with Reduced Alzheimer's Disease and Related Dementias Risk among Hypertensive Patients with Mild Cognitive Impairment using High Volume Claims Data.

Background: While hypertension is a modifiable risk factor of Alzheimer's disease and related dementias (ADRD), limited studies have been conducted on the effectiveness of antihypertensive medications (AHMs) in altering the progression from mild cognitive impairment (MCI) to ADRD; similarly, few studies have assessed drug-drug interactions of AHMs with drugs targeted to modify other risk factors of ADRD such as type II diabetes and hypercholesterolemia. Method: 128,683 unique hypertensive patients with MCI on US-based Optum claims data were identified. Diuretics, beta blockers (BBs), calcium channel blockers (CCBs), angiotensin-converting enzyme inhibitors (ACE inhibitors), and angiotensin II receptor antagonists (ARBs) were identified as five major AHM classes. Baseline characteristics were compared. Cox proportional hazards (PH) models were used to study the association between specific AHM exposure and the progression from MCI to ADRD while controlling for demographic variables, comorbidities, and the use of Statins and Metformin. To examine the association of AHM-Statin or AHM-Metformin interaction with ADRD progression, we also investigated models controlling for the aforementioned confounders, as well as drug-drug interactions. Result: The study included 100,678 patients who were taking at least one class of AHM and 28,005 who were not taking any AHMs during the study period. AHM users had a higher incidence of comorbidities (all P≤0.039) and consumption of Metformin and Statins (both P<0.001) compared to non-users. Users of each major AHM class showed significantly lower risk of developing ADRD compared to non-users of that specific drug class (adjusted hazard ratio (aHR): 0.96-0.98; all P≤0.048). Within patients on monotherapy (using only one AHM drug), no specific AHM class had significantly lower risk of ADRD diagnosis compared to other AHM drug classes (aHR: 0.97-1.11; all P≥0.053). Use of Diuretics or CCBs in combination with Metformin consumption (aHR: 0.89, 0.91, respectively) showed lower risk of MCI to ADRD progression than use without Metformin consumption (aHR: 0.97, 0.98, respectively), whereas use of any of the five major AHMs with Statin consumption (aHR: 0.91-0.94) all showed lower risk than without Statin consumption (aHR: 0.98-1.04). Conclusion: All five major AHM classes showed a protective effect against ADRD progression among hypertensive patients with MCI. Also, certain combinations of AHMs with Metformin or Statins showed a stronger protective effect compared to AHMs alone, and some drug-drug interactions of AHM-Metformin or AHM-Statin also showed protective effects against progression from MCI to ADRD.