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T.G. Meijer
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Large language models evaluated on medical text annotation tasks are typically scored against a single reference label, such as the primary billed ICD-9 code in MIMIC-III. This standard imposes a measurement ceiling on strict accuracy: it conflates genuine clinical extraction failure with cases where the model extracted a valid clinical comorbidity rather than the administratively billed primary code. To address this, we propose a dual-metric evaluation protocol that pairs strict accuracy (matching the primary billed code) with lenient accuracy (matching any billed code for the admission), scoped to clinical ICD-9 chapters, to separate model capability from billing-convention noise. Exercising the protocol through a human-in-the-loop (HITL) framework, we demonstrate an 18 percentage-point gap between strict (0.67) and lenient (0.85) accuracy on a human-reviewed baseline (n = 100); direct on-corpus analysis further shows that approximately 10% of discharge summaries (up to 14% in early iterations) contain gold-label-ambiguous coding artefacts rather than recoverable errors. On the same HITL framework, lenient accuracy converges to a tight [0.86, 0.90] band by iteration 3 across five prompt-engineering conditions, a cross-condition convergence we read as a framework-level reliability gain, and a human-reviewer trajectory shows strict accuracy trending from 0.62 → 0.78 under rich free-text feedback (a directional signal, confounded with batch difficulty at this sample size, that motivates the higher-powered replication we propose). Future LLM-based ICD-coding evaluations on MIMIC-III should adopt the dual-metric protocol so that model capability and billing-convention noise are reported separately.
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Large language models evaluated on medical text annotation tasks are typically scored against a single reference label, such as the primary billed ICD-9 code in MIMIC-III. This standard imposes a measurement ceiling on strict accuracy: it conflates genuine clinical extraction failure with cases where the model extracted a valid clinical comorbidity rather than the administratively billed primary code. To address this, we propose a dual-metric evaluation protocol that pairs strict accuracy (matching the primary billed code) with lenient accuracy (matching any billed code for the admission), scoped to clinical ICD-9 chapters, to separate model capability from billing-convention noise. Exercising the protocol through a human-in-the-loop (HITL) framework, we demonstrate an 18 percentage-point gap between strict (0.67) and lenient (0.85) accuracy on a human-reviewed baseline (n = 100); direct on-corpus analysis further shows that approximately 10% of discharge summaries (up to 14% in early iterations) contain gold-label-ambiguous coding artefacts rather than recoverable errors. On the same HITL framework, lenient accuracy converges to a tight [0.86, 0.90] band by iteration 3 across five prompt-engineering conditions, a cross-condition convergence we read as a framework-level reliability gain, and a human-reviewer trajectory shows strict accuracy trending from 0.62 → 0.78 under rich free-text feedback (a directional signal, confounded with batch difficulty at this sample size, that motivates the higher-powered replication we propose). Future LLM-based ICD-coding evaluations on MIMIC-III should adopt the dual-metric protocol so that model capability and billing-convention noise are reported separately.
How can we reduce the effect of noise on 3D Gaussian Splats?
A Study on Deblurring and Recoloring Techniques to Enhance 3D Reconstructions
Multi-view image recognition is crucial for numerous applications such as autonomous vehicles and robotics, where accurate 3D reconstructions from 2D images are essential. However, the presence of various noise factors like motion blur, variable lighting, and changes in the field of view can significantly degrade the quality of these reconstructions. Through a series of experiments using simulated data created in Blender, I explore the effectiveness of different de-noising methods, including AI-based and traditional image processing techniques. My findings indicate that specific adjustments in the Gaussian Splatting data preprocessing can significantly mitigate the adverse effects of noise, leading to more accurate and reliable 3D reconstructions. The study contributes to the field by providing a detailed analysis of noise impact and proposing viable solutions to enhance the fidelity of multi-view image reconstructions in noisy environments. The code and resources developed for this project are available in the GS-preprocessor repository on GitHub. https://github.com/surftijmen/GS-preprocessor.
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Multi-view image recognition is crucial for numerous applications such as autonomous vehicles and robotics, where accurate 3D reconstructions from 2D images are essential. However, the presence of various noise factors like motion blur, variable lighting, and changes in the field of view can significantly degrade the quality of these reconstructions. Through a series of experiments using simulated data created in Blender, I explore the effectiveness of different de-noising methods, including AI-based and traditional image processing techniques. My findings indicate that specific adjustments in the Gaussian Splatting data preprocessing can significantly mitigate the adverse effects of noise, leading to more accurate and reliable 3D reconstructions. The study contributes to the field by providing a detailed analysis of noise impact and proposing viable solutions to enhance the fidelity of multi-view image reconstructions in noisy environments. The code and resources developed for this project are available in the GS-preprocessor repository on GitHub. https://github.com/surftijmen/GS-preprocessor.