datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.6
What this repo does
This repository contains a Clarus v0.6 intervention pathway dataset focused on ventilator deterioration dynamics.
The dataset evaluates whether a model can determine if a proposed intervention meaningfully stabilizes a deteriorating ventilated system.
The task requires reasoning from:
system state
trajectory toward instability
boundary geometry
recovery geometry
intervention vector
projected trajectory consequence
The model cannot read the answer directly.
It… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.6.clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.4
What this repo does
This repository contains a Clarus v0.4 cascade boundary discovery dataset modeling ventilator deterioration.
Earlier Clarus datasets focused on detecting cascade states or forecasting collapse trajectories.
Version v0.4 extends the framework to a harder task:
detecting whether a system lies on the instability boundary itself.
The dataset models ventilator-linked deterioration as a coupled physiological system in which ventilation stress, declining physiological… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.4.clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.9
Clinical Quad Ventilation Stress Buffer Lag Coupling Ventilator Deterioration (v0.9)
What this repo does
This dataset implements a Clarus v0.9 intervention-competition benchmark.
Earlier dataset versions focused on detecting:
deterioration
regime transitions
boundary proximity
recovery feasibility
v0.9 extends the ladder.
The benchmark now evaluates whether a model can identify the correct rescue path when multiple interventions compete under narrowing rescue windows.… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.9.clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.5
What this repo does
This repository provides a Clarus v0.5 cascade recovery geometry dataset modeling ventilator deterioration.
Earlier Clarus datasets focused on detecting deterioration states and identifying instability boundaries.
Version v0.5 adds a recovery geometry layer that asks a stricter question:
Can the system still return to stability?
The task is binary classification over ventilator-linked deterioration states using:
• a four-variable clinical quad• trajectory… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.5.clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.1
What this repo does
This dataset models the transition from supported but recoverable ventilation to ventilator-associated deterioration using a four-variable coupling structure.
The goal is to detect when a patient is drifting toward respiratory and systemic decline despite ventilatory support.
Ventilator deterioration often unfolds as a cascade: ventilation stress rises, physiological reserve narrows, intervention delays reduce reversibility, and organ interactions amplify… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.1.clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v1.0
ClarusC64/clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v1.0
What this repo does
This repository provides a Clarus v1.0 benchmark for ventilator deterioration under a four-variable clinical quad:
ventilation_stress
buffer_capacity
lag_burden
coupling_stress
The v1.0 upgrade is Closed-Loop Control Geometry.
The task is no longer limited to detecting deterioration or ranking one intervention against another.
It tests whether a controller… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v1.0.clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.8
What this repo does
This repository provides a Clarus v0.8 clinical quad dataset for detecting and reasoning about ventilator deterioration regime transitions.
The dataset models situations where a patient state is no longer contained within a single ventilatory support basin but is shifting between competing regimes such as:
high-pressure ventilatory stress
hypoxemic ventilator failure
hypercapnic ventilator mismatch
multisystem ventilator-associated collapse
This is the… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.8.clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.3
What this repo does
This dataset models ventilator deterioration using the Clarus quad coupling framework combined with trajectory and system dynamics.
The goal is to predict whether a patient is approaching a ventilator deterioration cascade.
The dataset introduces a dynamic forecasting layer that allows models to reason about motion through the stability manifold rather than relying only on static physiological snapshots.
Core quad… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.3.clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.2
What this repo does
This dataset evaluates whether machine learning models can detect ventilator deterioration cascade transitions using both system state and system trajectory.
Earlier Clarus datasets in the v0.1 series tested whether models could classify system state alone.
Clarus v0.2 datasets introduce a trajectory signal so models must determine not only where the system is but where it is moving inside state space.
This benchmark therefore tests whether models can read… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.2.clinical-ventilation-perfusion-instability-v0.1
clinical-ventilation-perfusion-instability-v0.1
What this dataset does
This dataset evaluates whether models can detect respiratory gas-exchange instability from short clinical proxy trajectories.
Each row represents a patient-like respiratory scenario observed across three time points.
The task is to determine whether the system remains controlled or is moving toward ventilation–perfusion instability.
Core stability idea
Respiratory instability does not depend… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-ventilation-perfusion-instability-v0.1.clinical-latent-cross-coupling-ventilation-hemodynamic-fragility-v0.2
Clinical Latent Cross Coupling Ventilation Hemodynamic Fragility v0.2
What this is
A small dataset that tests one question:
Can you detect when a ventilation-hemodynamic system is moving toward hidden fragility, not just carrying visible strain?
This repo focuses on latent cross coupling between ventilation stability and hemodynamic buffering.
It models a system where:
ventilation stability may weaken
hemodynamic buffer capacity may erode
latent coupling pressure may… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-latent-cross-coupling-ventilation-hemodynamic-fragility-v0.2.Ventilation_time_prediction
데이터 명세서
이 문서는 데이터셋의 각 컬럼에 대한 정의와 용도를 설명합니다.
이 데이터셋은 과거 2시간 및 현재(t-2, t-1, t)의 데이터를 기반으로 미래 3시간(t+1, t+2, t+3) 후의 초미세먼지(PM25) 농도를 예측하도록 설계되었습니다.
1. Y: 종속 변수
모델이 최종적으로 예측해야 하는 값들입니다.
컬럼명
설명
단위
PM25_t_plus_1
(Target 1) 현재(t) 시점으로부터 1시간 뒤의 PM25 농도
µg/m³
PM25_t_plus_2
(Target 2) 현재(t) 시점으로부터 2시간 뒤의 PM25 농도
µg/m³
PM25_t_plus_3
(Target 3) 현재(t) 시점으로부터 3시간 뒤의 PM25 농도
µg/m³
2. X: 독립 변수
모델이 Y 값을 예측하는 데 사용하는 Feature들입니다.
2-1 기준 시점 t=0… See the full description on the dataset page: https://huggingface.co/datasets/rlawltjd/Ventilation_time_prediction.clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.7
What this repo does
This repository contains a Clarus v0.7 dataset modeling ventilator deterioration using a quad-coupling system representation.
The dataset extends the v0.6 intervention layer by introducing uncertainty geometry.
The question addressed by earlier versions was:
Can the system be stabilized?
v0.7 adds a second critical question:
How confident are we in that conclusion?
This allows Clarus to distinguish three operational states:
• confident deterioration• confident… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-ventilation-stress-buffer-lag-coupling-ventilator-deterioration-v0.7.