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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/hospital_deterioration_hourly_panel.csv filter=lfs diff=lfs merge=lfs -text
+data/hospital_deterioration_ml_ready.csv filter=lfs diff=lfs merge=lfs -text
+data/labs_timeseries.csv filter=lfs diff=lfs merge=lfs -text
+data/vitals_timeseries.csv filter=lfs diff=lfs merge=lfs -text

LICENSE.txt

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+Creative Commons Attribution 4.0 International (CC BY 4.0)
+
+You are free to:
+• Share — copy and redistribute the material in any medium or format.
+• Adapt — remix, transform, and build upon the material for any purpose, even commercially.
+
+Under the following terms:
+• Attribution — You must give appropriate credit, provide a link to the license,
+  and indicate if changes were made, without suggesting endorsement.
+
+Full license text: https://creativecommons.org/licenses/by/4.0/
+
+© 2025 Tarek Masryo
+This dataset is released under the CC BY 4.0 International license.

README.md

@@ -1,3 +1,226 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+task_categories:
+- tabular-classification
+- time-series-forecasting
+language:
+- en
+tags:
+- healthcare
+- clinical
+- hospital
+- early warning
+- sepsis
+- deterioration
+- time series
+- tabular data
+- machine learning
+- classification
+- risk prediction
+- synthetic data
+- open dataset
+- kaggle
+pretty_name: Hospital Deterioration — Simulated Early Warning
+size_categories:
+- 100K<n<1M
+---
+
+# 🏥 Hospital Deterioration — Simulated Early Warning  
+### Clinical Time-Series Benchmark for Early Warning Models
+
+A fully simulated **hospital cohort** for building and testing **early warning models** and **clinical deterioration risk scores**.  
+Each admission includes up to **72 hours** of hourly data: vitals, labs, patient context, and multiple deterioration outcomes — with a main label for **“deterioration in the next 12 hours”**.
+
+All records are **fully simulated**, **internally consistent**, and contain **no missing values**, making the dataset directly usable for **machine learning** and **time-series modeling**.
+
+---
+
+## ⚠️ Simulation & Privacy
+
+- No row corresponds to a real patient or a real hospital.  
+- All values are generated through a simulation pipeline designed to create **plausible clinical patterns**, not to reproduce real EHR data.  
+- The dataset is intended for **research, education, and prototyping**, not for real clinical decision-making.
+
+---
+
+## 📘 Dataset Overview
+
+| Field          | Description                                                                 |
+|---------------|-----------------------------------------------------------------------------|
+| **Files**     | `patients.csv`, `vitals_timeseries.csv`, `labs_timeseries.csv`, `hospital_deterioration_hourly_panel.csv`, `hospital_deterioration_ml_ready.csv` |
+| **Patients**  | 10,000 admissions (one row per patient in `patients.csv`)                   |
+| **Time span** | Up to 72 hours of follow-up per admission (`hour_from_admission` = 0–71)    |
+| **Granularity** | Hourly time series per patient (vitals, labs, labels)                     |
+| **Main target** | `deterioration_next_12h` (binary label, 0/1)                              |
+| **Type**      | Tabular / time-series (simulated)                                           |
+
+---
+
+## 🧠 Feature Groups
+
+### 🧍 Patient-Level Features (`patients.csv`)
+
+- `patient_id`  
+- `age`, `gender`  
+- `comorbidity_index`  
+- `admission_type` (ED / Elective / Transfer)  
+- `baseline_risk_score` (latent baseline deterioration risk, 0–1)  
+- `los_hours` (length of stay, 12–72 hours)  
+- Deterioration summary outcomes:  
+  - `deterioration_event`  
+  - `deterioration_within_12h_from_admission`  
+  - `deterioration_hour` (or -1 if no event)
+
+---
+
+### 📉 Hourly Vitals (`vitals_timeseries.csv`)
+
+Per `(patient_id, hour_from_admission)`:
+
+- `heart_rate`, `respiratory_rate`  
+- `spo2_pct`, `temperature_c`  
+- `systolic_bp`, `diastolic_bp`  
+- `oxygen_device`, `oxygen_flow`  
+- `mobility_score`  
+- `nurse_alert`
+
+**Consistency rule:**  
+When `oxygen_device == "none"`, `oxygen_flow` is always `0.0`.
+
+---
+
+### 🧪 Hourly Labs (`labs_timeseries.csv`)
+
+Per `(patient_id, hour_from_admission)`:
+
+- `wbc_count`  
+- `lactate`  
+- `creatinine`  
+- `crp_level`  
+- `hemoglobin`  
+- `sepsis_risk_score` (latent hourly sepsis risk, 0–1)
+
+---
+
+### 🧾 Joined Panel & ML-Ready View
+
+- `hospital_deterioration_hourly_panel.csv`  
+  - One row per `(patient_id, hour_from_admission)`  
+  - Joins **vitals + labs + patient-level features + all deterioration labels**  
+  - Useful for custom label definitions, multi-task learning, and advanced feature engineering.
+
+- `hospital_deterioration_ml_ready.csv`  
+  - Same hourly granularity  
+  - **Features only** (vitals, labs, static features)  
+  - **Single target**: `deterioration_next_12h` (0/1)  
+  - Recommended entry point for most ML tasks.
+
+---
+
+## 🎯 Target Definition — `deterioration_next_12h`
+
+The main label is:
+
+- `deterioration_next_12h = 1`  
+  if a deterioration event happens **after the current hour** and **within the next 12 hours**.
+
+- `deterioration_next_12h = 0`  
+  if:
+  - there is **no event** in the stay, or  
+  - the event is happening **now**, or  
+  - it happens **more than 12 hours** later.
+
+This framing mirrors real-world **early warning systems**:  
+the model should trigger an alert **before** the deterioration happens, not at the same time.
+
+---
+
+## 🚀 Example Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("TarekMasryo/hospital-deterioration-early-warning")
+
+# Load ML-ready split as a pandas DataFrame
+df = dataset["train"].to_pandas()
+
+X = df.drop(columns=["deterioration_next_12h"])
+y = df["deterioration_next_12h"]
+
+print(X.shape, y.mean())
+```
+
+To reconstruct a full hourly panel from separate files (if you export them):
+
+```python
+import pandas as pd
+
+patients = pd.read_csv("patients.csv")
+vitals = pd.read_csv("vitals_timeseries.csv")
+labs = pd.read_csv("labs_timeseries.csv")
+
+panel = (
+    vitals
+    .merge(labs, on=["patient_id", "hour_from_admission"], how="inner")
+    .merge(patients, on="patient_id", how="left")
+)
+
+print(panel.shape)
+```
+
+---
+
+## 🔬 Research & Applications
+
+- Early warning models for **clinical deterioration**  
+- Sepsis and high-risk trajectory modeling  
+- Sequence models over **hourly vitals + labs**  
+- Risk score calibration and interpretability (e.g., SHAP, partial dependence)  
+- Threshold tuning and policy design (balancing recall vs false alarms)  
+- Teaching end-to-end **clinical ML pipelines** without real-patient data
+
+---
+
+## 🧩 Reproducibility
+
+- No missing values  
+- Clean numeric + categorical schema  
+- Hourly-aligned time indexing (`hour_from_admission`)  
+- Suitable for:
+  - Classic ML (tree-based models, logistic regression)  
+  - Deep learning (RNNs, Temporal CNNs, Transformers)  
+  - Survival-like / time-to-event framing with custom labels
+
+---
+
+## 🧭 Ethical Considerations
+
+- This dataset is **simulated** and must **not** be used for clinical decisions.  
+- Patterns are **plausible**, not calibrated to any specific hospital, region, or population.  
+- Any model trained on this data requires:
+  - Validation on real EHR data  
+  - Clinical oversight  
+  - Regulatory and ethical review before deployment.
+
+Treat this dataset as a **simulation benchmark** and a **teaching tool**, not as a substitute for real-world evidence.
+
+---
+
+## 📚 Citation
+
+If you use this dataset, please cite:
+
+> Tarek Masryo. “Hospital Deterioration — Simulated Early Warning.”  
+> Simulation benchmark dataset for early clinical deterioration modeling and time-series ML.
+
+You may also cite the Hugging Face dataset URL and any associated GitHub repository or notebooks.
+
+---
+
+## 📜 License
+
+**CC BY 4.0 (Attribution Required)**  
+
+Free to use, share, and modify with proper attribution.  
+For full license terms: https://creativecommons.org/licenses/by/4.0/

data/hospital_deterioration_hourly_panel.csv

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data/hospital_deterioration_ml_ready.csv

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data/labs_timeseries.csv

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data/vitals_timeseries.csv

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+size 23947111

docs/data_dictionary.md

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+# Hospital Deterioration Dataset — Data Dictionary
+
+This dataset represents a **high-fidelity simulated hospital cohort** designed for
+early-warning and deterioration modeling.
+
+The records are generated via a rules-based and probabilistic simulation calibrated
+to typical hospital patterns. They **do not correspond to real patients** and contain
+no identifiable information.
+
+- Time is expressed as **hours from admission**.
+- Each patient stay is capped at **72 hours**.
+- All identifiers (`patient_id`) are artificial and non-identifiable.
+- All features are fully observed (no missing values).
+
+---
+
+## 1. `patients.csv` — Patient-level static data
+
+**Granularity:** one row per patient (10,000 patients).
+
+| column_name                               | type      | description                                                                                  | allowed_values / range                                   | missing_values |
+|-------------------------------------------|-----------|----------------------------------------------------------------------------------------------|----------------------------------------------------------|----------------|
+| `patient_id`                              | int       | Unique patient identifier                                                                    | 1–10,000                                                 | None           |
+| `age`                                     | int       | Age at admission (years)                                                                     | 18–90                                                    | None           |
+| `gender`                                  | category  | Biological sex                                                                               | `"M"`, `"F"`                                             | None           |
+| `comorbidity_index`                       | int       | Aggregate comorbidity burden (higher = more chronic disease)                                 | 0–8                                                      | None           |
+| `admission_type`                          | category  | Admission route to the hospital                                                              | `"ED"`, `"Elective"`, `"Transfer"`                       | None           |
+| `baseline_risk_score`                     | float     | Latent **baseline deterioration risk** at admission on a 0–1 scale (simulation parameter, not a clinical score) | ~0.03–0.98                               | None           |
+| `los_hours`                               | int       | Length of stay in hours (capped at 72 hours)                                                 | 12–72                                                    | None           |
+| `deterioration_event`                     | int (0/1) | Indicator for any clinical deterioration event during the stay                               | 0 = no event, 1 = at least one event                    | None           |
+| `deterioration_within_12h_from_admission` | int (0/1) | Deterioration event occurs within the first 12 hours from admission                          | 0, 1                                                     | None           |
+| `deterioration_hour`                      | int       | Hour-from-admission of the **first** deterioration event; `-1` means no event during the stay | -1 (no event) or 0–(los_hours - 1) (up to 71 in this dataset) | None     |
+
+---
+
+## 2. `vitals_timeseries.csv` — Hourly vital signs
+
+**Granularity:** one row per `(patient_id, hour_from_admission)` for vital signs.
+
+| column_name           | type      | description                                                                 | allowed_values / range                                   | missing_values |
+|-----------------------|-----------|-----------------------------------------------------------------------------|----------------------------------------------------------|----------------|
+| `patient_id`          | int       | Patient identifier (matches `patients.csv`)                                 | 1–10,000                                                 | None           |
+| `hour_from_admission` | int       | Hour index from admission (0 = admission hour)                              | 0–(los_hours - 1), up to 71 in this dataset             | None           |
+| `heart_rate`          | float     | Heart rate at that hour (beats per minute)                                  | ~40–180                                                  | None           |
+| `respiratory_rate`    | float     | Respiratory rate at that hour (breaths per minute)                          | ~8–45                                                    | None           |
+| `spo2_pct`            | float     | Peripheral oxygen saturation percentage                                     | ~70–100                                                  | None           |
+| `temperature_c`       | float     | Body temperature in degrees Celsius                                         | ~35.2–40.5                                               | None           |
+| `systolic_bp`         | float     | Systolic blood pressure (mmHg)                                              | ~70–185                                                  | None           |
+| `diastolic_bp`        | float     | Diastolic blood pressure (mmHg)                                             | ~40–110                                                  | None           |
+| `oxygen_device`       | category  | Type of oxygen delivery device                                              | `"none"`, `"nasal"`, `"mask"`, `"hfnc"`, `"niv"`         | None           |
+| `oxygen_flow`         | float     | Oxygen flow rate (liters per minute). Exactly `0.0` whenever `oxygen_device == "none"`, and strictly positive only when an oxygen device is in use. | 0–~60 (only >0 for `"nasal"`, `"mask"`, `"hfnc"`, `"niv"`) | None |
+| `mobility_score`      | int       | Ordinal mobility score (higher = more independent)                          | 0–4 (ordinal scale)                                     | None           |
+| `nurse_alert`         | int (0/1) | Whether a nurse alert was triggered in that hour                            | 0, 1                                                     | None           |
+
+---
+
+## 3. `labs_timeseries.csv` — Hourly lab results
+
+**Granularity:** one row per `(patient_id, hour_from_admission)` for lab values.
+
+| column_name           | type      | description                                                                 | allowed_values / range                       | missing_values |
+|-----------------------|-----------|-----------------------------------------------------------------------------|----------------------------------------------|----------------|
+| `patient_id`          | int       | Patient identifier (matches `patients.csv`)                                 | 1–10,000                                     | None           |
+| `hour_from_admission` | int       | Hour index from admission (aligned with vital signs)                        | 0–(los_hours - 1), up to 71 in this dataset | None           |
+| `wbc_count`           | float     | White blood cell count (arbitrary clinical units, simulation-based)         | ~2–30                                        | None           |
+| `lactate`             | float     | Serum lactate level (approx. mmol/L)                                       | ~0.5–8.0                                     | None           |
+| `creatinine`          | float     | Serum creatinine (approx. mg/dL)                                           | ~0.4–4.5                                     | None           |
+| `crp_level`           | float     | C-reactive protein level (approx. mg/L)                                    | ~0–250                                       | None           |
+| `hemoglobin`          | float     | Hemoglobin concentration (approx. g/dL)                                    | ~7–17                                        | None           |
+| `sepsis_risk_score`   | float     | Latent **sepsis risk** score at that hour on a 0–1 scale (simulation parameter) | ~0.02–1.00                              | None           |
+
+---
+
+## 4. `hospital_deterioration_hourly_panel.csv` — Full joined hourly panel
+
+**Granularity:** one row per `(patient_id, hour_from_admission)` combining:
+
+- Hourly vital signs  
+- Hourly lab values  
+- Static patient features  
+- All deterioration labels
+
+This file is convenient for EDA and modeling where you want everything in one table.
+
+| column_name                               | type      | description                                                                                               | allowed_values / range                                   | missing_values |
+|-------------------------------------------|-----------|-----------------------------------------------------------------------------------------------------------|----------------------------------------------------------|----------------|
+| `patient_id`                              | int       | Patient identifier                                                                                        | 1–10,000                                                 | None           |
+| `hour_from_admission`                     | int       | Hour index from admission                                                                                 | 0–(los_hours - 1), up to 71 in this dataset             | None           |
+| `heart_rate`                              | float     | Heart rate (beats per minute) at this hour                                                                | ~40–180                                                  | None           |
+| `respiratory_rate`                        | float     | Respiratory rate (breaths per minute)                                                                     | ~8–45                                                    | None           |
+| `spo2_pct`                                | float     | Peripheral oxygen saturation (%)                                                                          | ~70–100                                                  | None           |
+| `temperature_c`                           | float     | Body temperature in °C                                                                                    | ~35.2–40.5                                               | None           |
+| `systolic_bp`                             | float     | Systolic blood pressure (mmHg)                                                                            | ~70–185                                                  | None           |
+| `diastolic_bp`                            | float     | Diastolic blood pressure (mmHg)                                                                           | ~40–110                                                  | None           |
+| `oxygen_device`                           | category  | Oxygen delivery device                                                                                    | `"none"`, `"nasal"`, `"mask"`, `"hfnc"`, `"niv"`         | None           |
+| `oxygen_flow`                             | float     | Oxygen flow rate (L/min). Exactly `0.0` whenever `oxygen_device == "none"`, and strictly positive only when an oxygen device is in use. | 0–~60 (only >0 for `"nasal"`, `"mask"`, `"hfnc"`, `"niv"`) | None |
+| `mobility_score`                          | int       | Ordinal mobility score                                                                                    | 0–4                                                      | None           |
+| `nurse_alert`                             | int (0/1) | Nurse alert triggered during this hour                                                                    | 0, 1                                                     | None           |
+| `wbc_count`                               | float     | White blood cell count                                                                                    | ~2–30                                                    | None           |
+| `lactate`                                 | float     | Serum lactate                                                                                             | ~0.5–8.0                                                 | None           |
+| `creatinine`                              | float     | Serum creatinine                                                                                          | ~0.4–4.5                                                 | None           |
+| `crp_level`                               | float     | C-reactive protein                                                                                        | ~0–250                                                   | None           |
+| `hemoglobin`                              | float     | Hemoglobin                                                                                                | ~7–17                                                    | None           |
+| `sepsis_risk_score`                       | float     | Latent sepsis risk score (0–1)                                                                            | ~0.02–1.00                                               | None           |
+| `age`                                     | int       | Age at admission                                                                                          | 18–90                                                    | None           |
+| `gender`                                  | category  | Biological sex                                                                                            | `"M"`, `"F"`                                             | None           |
+| `comorbidity_index`                       | int       | Aggregate comorbidity burden                                                                              | 0–8                                                      | None           |
+| `admission_type`                          | category  | Admission route                                                                                           | `"ED"`, `"Elective"`, `"Transfer"`                       | None           |
+| `baseline_risk_score`                     | float     | Latent baseline deterioration risk at admission on a 0–1 scale (simulation parameter)                    | ~0.03–0.98                                               | None           |
+| `los_hours`                               | int       | Length of stay in hours                                                                                   | 12–72                                                    | None           |
+| `deterioration_event`                     | int (0/1) | Any deterioration event during the stay                                                                   | 0, 1                                                     | None           |
+| `deterioration_within_12h_from_admission` | int (0/1) | Deterioration occurs within the first 12 hours                                                            | 0, 1                                                     | None           |
+| `deterioration_hour`                      | int       | Hour of first deterioration event; `-1` = no event                                                        | -1 (no event) or 0–(los_hours - 1)                      | None           |
+| `deterioration_next_12h`                  | int (0/1) | Label: deterioration occurs **after this hour** and **within the next 12 hours** (see definition below)  | 0, 1                                                     | None           |
+
+**Definition of `deterioration_next_12h`:**
+
+For a given row with `(patient_id, hour_from_admission = t)` and corresponding `deterioration_hour = h`:
+
+- `deterioration_next_12h = 1` if `t < h ≤ t + 12`
+- `deterioration_next_12h = 0` otherwise (including no event in the stay).
+
+---
+
+## 5. `hospital_deterioration_ml_ready.csv` — ML-ready classification panel
+
+**Granularity:** one row per hourly observation (per patient and `hour_from_admission`).  
+**Intended use:** feed directly into ML models for **next-12h deterioration prediction**.
+
+This file keeps only **features + target**, and omits identifiers and auxiliary targets to reduce leakage risk.
+
+| column_name               | type      | description                                                                 | allowed_values / range                                   | missing_values |
+|---------------------------|-----------|-----------------------------------------------------------------------------|----------------------------------------------------------|----------------|
+| `hour_from_admission`     | int       | Hour index from admission                                                  | 0–71 (per-stay capped length in this dataset)           | None           |
+| `heart_rate`              | float     | Heart rate (beats per minute)                                              | ~40–180                                                  | None           |
+| `respiratory_rate`        | float     | Respiratory rate (breaths per minute)                                      | ~8–45                                                    | None           |
+| `spo2_pct`                | float     | Peripheral oxygen saturation (%)                                           | ~70–100                                                  | None           |
+| `temperature_c`           | float     | Body temperature (°C)                                                      | ~35.2–40.5                                               | None           |
+| `systolic_bp`             | float     | Systolic blood pressure (mmHg)                                             | ~70–185                                                  | None           |
+| `diastolic_bp`            | float     | Diastolic blood pressure (mmHg)                                            | ~40–110                                                  | None           |
+| `oxygen_device`           | category  | Oxygen delivery device                                                     | `"none"`, `"nasal"`, `"mask"`, `"hfnc"`, `"niv"`         | None           |
+| `oxygen_flow`             | float     | Oxygen flow rate (L/min). Exactly `0.0` whenever `oxygen_device == "none"`, and strictly positive only when an oxygen device is in use. | 0–~60 (only >0 for `"nasal"`, `"mask"`, `"hfnc"`, `"niv"`) | None |
+| `mobility_score`          | int       | Ordinal mobility score                                                     | 0–4                                                      | None           |
+| `nurse_alert`             | int (0/1) | Nurse alert active in this hour                                            | 0, 1                                                     | None           |
+| `wbc_count`               | float     | White blood cell count                                                     | ~2–30                                                    | None           |
+| `lactate`                 | float     | Serum lactate                                                              | ~0.5–8.0                                                 | None           |
+| `creatinine`              | float     | Serum creatinine                                                           | ~0.4–4.5                                                 | None           |
+| `crp_level`               | float     | C-reactive protein                                                         | ~0–250                                                   | None           |
+| `hemoglobin`              | float     | Hemoglobin                                                                 | ~7–17                                                    | None           |
+| `sepsis_risk_score`       | float     | Latent sepsis risk score (0–1)                                            | ~0.02–1.00                                               | None           |
+| `age`                     | int       | Age at admission                                                           | 18–90                                                    | None           |
+| `gender`                  | category  | Biological sex                                                             | `"M"`, `"F"`                                             | None           |
+| `comorbidity_index`       | int       | Aggregate comorbidity burden                                               | 0–8                                                      | None           |
+| `admission_type`          | category  | Admission route                                                            | `"ED"`, `"Elective"`, `"Transfer"`                       | None           |
+| `deterioration_next_12h`  | int (0/1) | Target label: deterioration occurs **after this hour** and **within the next 12 hours** | 0, 1                                         | None           |
+
+The definition of `deterioration_next_12h` is identical to the one in `hospital_deterioration_hourly_panel.csv`.