Joseph Geraci

Modular Meta-Evolutionary AI Architecture Enables Interpretable Stratification in Heterogeneous Clinical Trials

Joseph Geraci, Bessi Qorri, Christian Cumbaa, Mike Tsay et autres

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Explainable AI-driven precision clinical trial enrichment: demonstration of the NetraAI platform with a phase II depression trial

Joseph Geraci, Bessi Qorri, Mike Tsay, Christian Cumbaa et autres

Clinical trial failures are frequently driven by patient heterogeneity and limited sample sizes that obscure treatment effects by diluting statistical power. We introduce NetraAI, a novel explainable artificial intelligence (AI) platform that integrates dynamical-systems modeling, evolutionary long-range memory feature selection, and large-language …

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Identifying Site‐Level Data Integrity Risks in Alzheimer’s Trials via Subject‐Level Heuristics and a Novel Machine Learning Framework

Joseph Geraci

BACKGROUND: Undetected data integrity issues in Alzheimer's disease (AD) trials often appear as "paradoxical" participant profiles with atypical or implausible profiles that can distort efficacy signals and threaten the validity of study conclusions. A unique mathematically-augmented machine learning (MAML) framework was used …

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Detecting Site‐Level Fraud via an Artificial Intelligence/Machine Learning Paradoxical Patient Analysis in an Alzheimer's Disease Clinical Trial

Joseph Geraci, Patrick P O'Keefe, Bessi Qorri, Paul Leonchyk et autres

BACKGROUND: Ensuring data integrity in Alzheimer's Disease (AD) clinical trials is crucial, yet the subjective nature of endpoints and potential site-level inconsistencies can undermine study outcomes. Paradoxical patients, defined as individuals whose data clustering patterns deviate significantly from expected norms, may indicate …

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Breaking barriers in rare disease research: The RARE-X Open Science Data Challenge as a model for collaborative innovation and community partnership

Karmen M Trzupek, Ravi Bhargava, Fanny Sie, Vanessa Vogel‐Farley et autres

Trzupek et al. describe a rare disease Open Science Data Challenge, using data collected systematically on RARE-X across 27 neurodevelopmental disorders. Clinical diagnoses, symptoms, genetic data, and PROs were included. Researchers and statisticians generated solutions that identified previously underappreciated symptoms and used …

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Abstract 3655: Leveraging alternative AIML technologies to identify predictive biomarkers for chemotherapy selection: An analysis of the COMPASS trial evaluating chemotherapy response in advanced PDAC

Joseph Geraci, Bessi Qorri, Mike J. Tsay, Christian Cumbaa et autres

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with limited effective therapeutic options. Gemcitabine plus nab-Paclitaxel (GnP) and FOLFIRINOX (FFX) are commonly used first-line chemotherapies for advanced PDAC. However, disease heterogeneity and late-stage diagnosis remain to result in a poor …

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462. Preferential Drug Response Model in a Schizophrenia Trial via an Alternative AI Method: Revisiting the Catie Schizophrenia Trial

Joseph Geraci, Bessi Qorri, Mike Tsay, Christian Cumbaa et autres

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Using Machine Learning to Explore Multimodal Digital Markers for Early Detection of Cognitive Impairment in Alzheimer’s Disease

Joseph Geraci, Edward Searls, Bessi Qorri, Spencer Low et autres

Abstract Background Recent technological advancements have revolutionized our approach to healthcare, enabling us to harness the potential of smartphones and wearables to collect data that can be used to characterize Alzheimer’s disease (AD) heterogeneity and to develop digital biomarkers. Our focus is …

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Abstract B066: An AI approach to unraveling treatment response in pancreatic cancer: Insights from the COMPASS trial leveraging large language models (LLMs)

Joseph Geraci, Bessi Qorri, Mike Tsay, Christian Cumbaa et autres

Abstract Pancreatic cancer, often termed the “silent killer” due to its vague symptoms and late diagnosis, continues to challenge oncologists with its significant treatment challenges and poor prognosis. Standard-of-care regimens, such as FOLFIRINOX (FFX) and Gemcitabine + nab-paclitaxel (GnP), show varied efficacy …

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Revealing heterogeneity in chronic lymphocytic leukemia: AI-driven insights into aggressive and indolent disease subtypes.

Bessi Qorri, Joseph Geraci, Mike Tsay, Christian Cumbaa et autres

e19029 Background: Chronic lymphocytic leukemia (CLL) exhibits a broad spectrum of clinical behaviors, from indolent courses requiring minimal intervention to aggressive forms demanding immediate treatment. The underlying heterogeneity of CLL poses significant challenges in predicting disease progression and tailoring patient-specific therapeutic strategies. …

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Abstract LB396: The power of NetraAI: Precision medicine in oncology through sub-insight learning from small data sets

Bessi Qorri, Mike J. Tsay, Paul Leonchyk, Larry Alphs et autres

Abstract The capabilities of artificial intelligence (AI) and machine learning (ML) are pivotal for refining patient stratification and subtype discrimination in clinical trials. Conventional ML methods often rely on large data sets for meaningful discoveries. NetraAI is a novel ML approach designed …

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Abstract LB395: NetraAI-driven discovery of novel biomarkers in MSI-high colon cancer for precision immunotherapy

Bessi Qorri, Mike J. Tsay, Paul Leonchyk, Larry Alphs et autres

Abstract By leveraging NetraAI, a novel machine learning (ML) approach, we identify potential biomarkers in microsatellite instability-high (MSI-H) colon cancer. MSI, marked by DNA mismatch repair (MMR) defects characterizes 5-20% of colorectal cancers (CRCs). MSI-H tumors harbor higher mutational burdens, produce neoantigens, …

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