5 citations · 6 across the 8 of their papers we have counts for
9 papers
CAMOS: Coupled Oscillatory State-Space Model for Multimodal Clinical Time-Series
Maxx Richard Rahman, Mostafa Hammouda, Wolfgang Maass
Longitudinal clinical cohorts are multimodal, irregularly sampled and pervasively incomplete: in ADNI, positron emission tomography and cerebrospinal fluid assays are absent from r…
MedLLM: An Open Medical Language Model at the Sub-Billion Scale
Maxx Richard Rahman, Asim Ahmed, Mihan Mohagheghzadeh +1
Open medical language models have converged on a single scale: every widely used system runs at 7B parameters or more, leaving the sub-billion regime uncharacterized. We present Me…
INCLAIR: Inception-Based Longitudinal Clinical Anomaly Detection with Informed Reasoning
Maxx Richard Rahman, Wolfgang Maass
Detecting anomalies in longitudinal clinical profiles is clinically important but difficult: abnormal evidence is often sparse, patient histories have unequal length, and expert ex…
LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts
Maxx Richard Rahman, Prakhar Kumar, Wolfgang Maass
Multimodal clinical learning is increasingly important for integrating diverse patient data, including imaging, text, and personalised health records. However, it faces two fundame…
DiGAN: Diffusion-Guided Attention Network for Early Alzheimer's Disease Detection
Maxx Richard Rahman, Mostafa Hammouda, Wolfgang Maass
Early diagnosis of Alzheimer's disease (AD) remains a major challenge due to the subtle and temporally irregular progression of structural brain changes in the prodromal stages. Ex…
RAG for Effective Supply Chain Security Questionnaire Automation
Zaynab Batool Reza, Abdul Rafay Syed, Omer Iqbal +4
In an era where digital security is crucial, efficient processing of security-related inquiries through supply chain security questionnaires is imperative. This paper introduces a…
Incorporating Metabolic Information into LLMs for Anomaly Detection in Clinical Time-Series
Maxx Richard Rahman, Ruoxuan Liu, Wolfgang Maass
Anomaly detection in clinical time-series holds significant potential in identifying suspicious patterns in different biological parameters. In this paper, we propose a targeted me…
LLMRS: Unlocking Potentials of LLM-Based Recommender Systems for Software Purchase
Angela John, Theophilus Aidoo, Hamayoon Behmanush +4
Recommendation systems are ubiquitous, from Spotify playlist suggestions to Amazon product suggestions. Nevertheless, depending on the methodology or the dataset, these systems typ…
AI-based approach for improving the detection of blood doping in sports
Maxx Richard Rahman, Jacob Bejder, Thomas Christian Bonne +5
Sports officials around the world are facing incredible challenges due to the unfair means of practices performed by the athletes to improve their performance in the game. It inclu…