4 citations · 8 across the 9 of their papers we have counts for
11 papers
Decision language models as affordable and rapid alternatives to LLM judges of clinical generative AI
Rotem Gershon, Seffi Cohen, Dean Ariel +11
Automated judges of clinical artificial intelligence (AI) outputs are necessary, but large language model (LLM) judges are costly and slow at scale. We compared Jev, a decision lan…
Agentic discovery of blood biomarker from distilled private health records
Seffi Cohen, Liat Antwarg Friedman, Amir Anisman +7
Routine complete blood counts (CBCs) could yield new biomarkers, but the private records needed to evaluate candidates cannot be shared with frontier language model agents that exc…
Laboratory Trajectories Improve Kidney Failure Risk Estimation
Morgan Sanchez, James A. Diao, Jesse Cummings +8
Accurate kidney failure risk assessment is critical to timely intervention in chronic kidney disease (CKD). Existing equations (e.g. Kidney Failure Risk Equation; KFRE) rely on sin…
Learning Normal Representations for Blood Biomarkers
Aashna P. Shah, Michelle M. Li, Yash Lal +9
Blood-based biomarkers underpin clinical diagnosis and management, yet their interpretation relies largely on fixed population reference intervals that ignore stable, intra-patient…
Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations
Yehonatan Elisha, Seffi Cohen, Oren Barkan +1
Saliency maps are widely used for visual explanations in deep learning, but a fundamental lack of consensus persists regarding their intended purpose and alignment with diverse use…
DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM Performance
Seffi Cohen, Niv Goldshlager, Nurit Cohen-Inger +2
Large Language Models (LLMs) have shown remarkable capabilities across various natural language processing tasks but often struggle to excel uniformly in diverse or complex domains…
Forget What You Know about LLMs Evaluations -- LLMs are Like a Chameleon
Nurit Cohen-Inger, Yehonatan Elisha, Bracha Shapira +2
Large language models (LLMs) often appear to excel on public benchmarks, but these high scores may mask an overreliance on dataset-specific surface cues rather than true language u…
FairTTTS: A Tree Test Time Simulation Method for Fairness-Aware Classification
Nurit Cohen-Inger, Lior Rokach, Bracha Shapira +1
Algorithmic decision-making has become deeply ingrained in many domains, yet biases in machine learning models can still produce discriminatory outcomes, often harming unprivileged…
BiasGuard: Guardrailing Fairness in Machine Learning Production Systems
Nurit Cohen-Inger, Seffi Cohen, Neomi Rabaev +2
As machine learning (ML) systems increasingly impact critical sectors such as hiring, financial risk assessments, and criminal justice, the imperative to ensure fairness has intens…
BagStacking: An Integrated Ensemble Learning Approach for Freezing of Gait Detection in Parkinson's Disease
Seffi Cohen, Lior Rokach
This paper introduces BagStacking, a novel ensemble learning method designed to enhance the detection of Freezing of Gait (FOG) in Parkinson's Disease (PD) by using a lower-back se…
Boosting Anomaly Detection Using Unsupervised Diverse Test-Time Augmentation
Seffi Cohen, Niv Goldshlager, Lior Rokach +1
Anomaly detection is a well-known task that involves the identification of abnormal events that occur relatively infrequently. Methods for improving anomaly detection performance h…