Tootfinder

Aiming for Relevance
Bar Eini Porat, Danny Eytan, Uri Shalit
https://arxiv.org/abs/2403.18668 https://arxiv.org/pdf/2403.18668…

Aiming for Relevance
Vital signs are crucial in intensive care units (ICUs). They are used to track the patient's state and to identify clinically significant changes. Predicting vital sign trajectories is valuable for early detection of adverse events. However, conventional machine learning metrics like RMSE often fail to capture the true clinical relevance of such predictions. We introduce novel vital sign prediction performance metrics that align with clinical contexts, focusing on deviations from clinical norms…

This https://arxiv.org/abs/2201.11291 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Survey of PPG's Application in Authentication
Biometric authentication prospered because of its convenient use and security. Early generations of biometric mechanisms suffer from spoofing attacks. Recently, unobservable physiological signals (e.g., Electroencephalogram, Photoplethysmogram, Electrocardiogram) as biometrics offer a potential remedy to this problem. In particular, Photoplethysmogram (PPG) measures the change in blood flow of the human body by an optical method. Clinically, researchers commonly use PPG signals to obtain patien…

This https://arxiv.org/abs/2211.16879 has been replaced.
link: https://scholar.google.com/scholar?q=a

Robust, fast and accurate mapping of diffusional mean kurtosis
Diffusional kurtosis imaging (DKI) is a methodology for measuring the extent of non-Gaussian diffusion in biological tissue, which has shown great promise in clinical diagnosis, treatment planning and monitoring of many neurological diseases and disorders. However, robust, fast and accurate estimation of kurtosis from clinically feasible data acquisitions remains a challenge. In this study, we first outline a new accurate approach of estimating mean kurtosis via the sub-diffusion mathematical f…

Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications
Fabian Baldenweg, Manuel Burger, Gunnar R\"atsch, Rita Kuznetsova
https://arxiv.org/abs/2403.18316

Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications
Electronic Health Record (EHR) datasets from Intensive Care Units (ICU) contain a diverse set of data modalities. While prior works have successfully leveraged multiple modalities in supervised settings, we apply advanced self-supervised multi-modal contrastive learning techniques to ICU data, specifically focusing on clinical notes and time-series for clinically relevant online prediction tasks. We introduce a loss function Multi-Modal Neighborhood Contrastive Loss (MM-NCL), a soft neighborhoo…

Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology
Nur Yildirim, Hannah Richardson, Maria T. Wetscherek, Junaid Bajwa, Joseph Jacob, Mark A. Pinnock, Stephen Harris, Daniel Coelho de Castro, Shruthi Bannur, Stephanie L. Hyland, Pratik Ghosh, Mercy Ranjit, Kenza Bouzid, Anton Schwaighofer, Fernando P\'erez-Garc\'ia, Harshita Sharma, Ozan Oktay, Matthew Lungren, Javier Alvarez-Valle, Aditya Nori, Anja Thieme

Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology
Recent advances in AI combine large language models (LLMs) with vision encoders that bring forward unprecedented technical capabilities to leverage for a wide range of healthcare applications. Focusing on the domain of radiology, vision-language models (VLMs) achieve good performance results for tasks such as generating radiology findings based on a patient's medical image, or answering visual questions (e.g., 'Where are the nodules in this chest X-ray?'). However, the clinical utility of poten…

This https://arxiv.org/abs/2211.09887 has been replaced.
link: https://scholar.google.com/scholar?q=a

Spherical convolutional neural networks can improve brain microstructure estimation from diffusion MRI data
Diffusion magnetic resonance imaging is sensitive to the microstructural properties of brain tissue. However, estimating clinically and scientifically relevant microstructural properties from the measured signals remains a highly challenging inverse problem that machine learning may help solve. This study investigated if recently developed rotationally invariant spherical convolutional neural networks can improve microstructural parameter estimation. We trained a spherical convolutional neural …

An Executable Specification of Oncology Dose-Escalation Protocols with Prolog
David C. Norris, Markus Triska
https://arxiv.org/abs/2402.08334 https://

An Executable Specification of Oncology Dose-Escalation Protocols with Prolog
We present, as a pure Prolog program, the first executable specification of the 3 + 3 dose-escalation protocol commonly used in early-phase oncology drug development. In this program, the imperative operations of the protocol emerge as consequences of clinically meaningful anticipatory-regret scenarios that are declared as CLP(Z) constraints. This 'regret-constrained' (RC) specification yields a robust formulation which can be used to prove clinically meaningful safety and liveness properties o…

This https://arxiv.org/abs/2403.10581 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction
Heart failure (HF) poses a significant public health challenge due to its rising global mortality rate. Addressing this issue through early diagnosis and prevention could significantly reduce the disease's impact. This work introduces a methodology for HF risk prediction using clinically acquired 12-lead electrocardiograms (ECGs). We present a novel, lightweight dual-attention ECG network designed to capture complex ECG features essential for early HF prediction, despite the notable imbalance b…

This https://arxiv.org/abs/2402.08987 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Multi-modality transrectal ultrasound vudei classification for identification of clinically significant prostate cancer
Prostate cancer is the most common noncutaneous cancer in the world. Recently, multi-modality transrectal ultrasound (TRUS) has increasingly become an effective tool for the guidance of prostate biopsies. With the aim of effectively identifying prostate cancer, we propose a framework for the classification of clinically significant prostate cancer (csPCa) from multi-modality TRUS videos. The framework utilizes two 3D ResNet-50 models to extract features from B-mode images and shear wave elastog…

Multi-modality transrectal ultrasound vudei classification for identification of clinically significant prostate cancer
Hong Wu, Juan Fu, Hongsheng Ye, Yuming Zhong, Xuebin Zhou, Jianhua Zhou, Yi Wang
https://arxiv.org/abs/2402.08987

Multi-modality transrectal ultrasound vudei classification for identification of clinically significant prostate cancer
Prostate cancer is the most common noncutaneous cancer in the world. Recently, multi-modality transrectal ultrasound (TRUS) has increasingly become an effective tool for the guidance of prostate biopsies. With the aim of effectively identifying prostate cancer, we propose a framework for the classification of clinically significant prostate cancer (csPCa) from multi-modality TRUS videos. The framework utilizes two 3D ResNet-50 models to extract features from B-mode images and shear wave elastog…