Create README.md

---
language:
- zh
- en
tags:
- ECG
- potassium
- hyperkalemia
- hypokalemia
- medical
- cardiology
- nephrology
- time-series
- classification
license: other
pipeline_tag: time-series-classification
---

# KaliSense AI — ECG-based Serum Potassium Anomaly Detector

> 🏆 **2025 SelectUSA Investment Summit — Global 2nd Place**
> 🏥 Invited for evaluation by world top-10 medical institutions
> 🏢 By [Precise Bigdata生技 | Precise Intelligent Biotech](https://huggingface.co/precise-biotech)

---

## Model Description | 模型說明

**KaliSense AI** is a CNN-Transformer deep learning model that predicts serum potassium (K⁺) anomalies directly from 12-lead ECG waveforms — **without any blood draw**.

The model classifies ECG inputs into three categories:
- ✅ **Normal** — K⁺ within normal range (3.5–5.0 mEq/L)
- ⚠️ **Hypokalemia** — Low potassium risk (K⁺ < 3.5 mEq/L)
- 🚨 **Hyperkalemia** — High potassium risk (K⁺ > 5.5 mEq/L)

---

## Clinical Background | 臨床背景

Hyperkalemia and hypokalemia are among the most dangerous electrolyte imbalances, especially in:
- Chronic Kidney Disease (CKD) patients
- Hemodialysis patients (9万+ in Taiwan alone)
- Patients on ACE inhibitors, ARBs, or potassium-sparing diuretics

Traditional serum potassium testing requires venous blood draw and lab processing (30 min – hours). KaliSense AI enables **real-time, non-invasive** potassium risk screening from ECG alone.

---

## Model Architecture | 模型架構

```
Input: 12-lead ECG (500 Hz sampling rate)
│
▼
CNN Encoder (local feature extraction)
• T-wave amplitude detection
• QRS complex width measurement
• PR interval analysis
• QTc calculation
│
▼
Transformer Encoder (global temporal context)
• Multi-head self-attention (8 heads)
• Positional encoding for temporal sequence
│
▼
Classification Head
• 3-class output: Normal / Hypokalemia / Hyperkalemia
• Softmax probability scores
```

---

## Performance | 模型效能

Validated on clinical dataset from nephrology and hemodialysis centers (IRB approved):

| Metric | Value |
|--------|-------|
| Overall Accuracy | 91.2% |
| Hyperkalemia Sensitivity | 88.0% |
| Hyperkalemia Specificity | 93.5% |
| Hypokalemia Sensitivity | 85.4% |
| AUC (Hyperkalemia) | 0.947 |
| AUC (Hypokalemia) | 0.921 |

---

## Input Format | 輸入格式

| Property | Specification |
|----------|--------------|
| ECG Leads | 12-lead (or 1/3/6-lead) |
| Sampling Rate | 500 Hz (recommended) |
| Duration | 10 seconds standard |
| File Formats | .CSV, .EDF, HL7 |

---

## Intended Use | 適用範圍

✅ **Intended for:**
- Clinical potassium risk screening support
- Hemodialysis patient continuous monitoring
- Rural clinic and remote care settings
- Emergency department rapid assessment

❌ **Not intended for:**
- Replacing laboratory serum potassium testing
- Standalone diagnosis without physician review
- Pediatric patients (not validated)

---

## Limitations & Disclaimer | 限制與免責聲明

> ⚠️ This model is intended as a **clinical decision support tool only**.
> All outputs must be confirmed by a licensed physician before clinical action.
> Currently under **TFDA SaMD** regulatory review for medical device software certification.

---

## Privacy & Compliance | 隱私與法規

- Model trained on **IRB-approved, de-identified** clinical ECG data
- Core model weights and training pipeline stored in **private repository** under TFDA SaMD compliance controls
- Patient data is **never stored** beyond the analysis session
- Compliant with **ISO 27001** information security standards

---

## Pre-training Datasets | 預訓練資料集

| Dataset | Records | Usage |
|---------|---------|-------|
| [PhysioNet PTB-XL](https://physionet.org/content/ptb-xl/) | 21,799 ECGs | Pre-training |
| [MIMIC-IV-ECG](https://physionet.org/content/mimic-iv-ecg/) | 800,000+ ECGs | Feature extraction pre-training |
| Internal Clinical (IRB) | 2,847 ECGs | Fine-tuning & validation |

---

## Citation | 引用

If you reference this work, please cite:

```bibtex
@software {kalisense2025,
title = {KaliSense AI: Non-Invasive Serum Potassium Anomaly Detection},
author = {Precise Intelligent Biotech Co., Ltd.},
year = {2025},
url = {https://huggingface.co/precise-biotech/kalisense-ecg-potassium}
}
```

---

## Contact & Collaboration | 合作洽詢

🏢 **Precise Bigdata生技股份有限公司 | Precise Intelligent Biotech Co., Ltd.**
📧 contact@precisebiotech.ai
🌐 www.precisebiotech.ai
💻 GitHub: [github.com/curry1982/kalisense-ai](https://github.com/curry1982/kalisense-ai)

README.md

@@ -1,5 +1,162 @@
----
-license: other
-license_name: kalisense-ecg-potassium
-license_link: LICENSE
----
+---
+language:
+- zh
+- en
+tags:
+- ECG
+- potassium
+- hyperkalemia
+- hypokalemia
+- medical
+- cardiology
+- nephrology
+- time-series
+- classification
+license: other
+pipeline_tag: time-series-classification
+---
+
+# KaliSense AI — ECG-based Serum Potassium Anomaly Detector
+
+> 🏆 **2025 SelectUSA Investment Summit — Global 2nd Place**  
+> 🏥 Invited for evaluation by world top-10 medical institutions  
+> 🏢 By [Precise Bigdata生技 | Precise Intelligent Biotech](https://huggingface.co/precise-biotech)
+
+---
+
+## Model Description | 模型說明
+
+**KaliSense AI** is a CNN-Transformer deep learning model that predicts serum potassium (K⁺) anomalies directly from 12-lead ECG waveforms — **without any blood draw**.
+
+The model classifies ECG inputs into three categories:
+- ✅ **Normal** — K⁺ within normal range (3.5–5.0 mEq/L)
+- ⚠️ **Hypokalemia** — Low potassium risk (K⁺ < 3.5 mEq/L)
+- 🚨 **Hyperkalemia** — High potassium risk (K⁺ > 5.5 mEq/L)
+
+---
+
+## Clinical Background | 臨床背景
+
+Hyperkalemia and hypokalemia are among the most dangerous electrolyte imbalances, especially in:
+- Chronic Kidney Disease (CKD) patients
+- Hemodialysis patients (9万+ in Taiwan alone)
+- Patients on ACE inhibitors, ARBs, or potassium-sparing diuretics
+
+Traditional serum potassium testing requires venous blood draw and lab processing (30 min – hours). KaliSense AI enables **real-time, non-invasive** potassium risk screening from ECG alone.
+
+---
+
+## Model Architecture | 模型架構
+
+```
+Input: 12-lead ECG (500 Hz sampling rate)
+    │
+    ▼
+CNN Encoder (local feature extraction)
+• T-wave amplitude detection
+• QRS complex width measurement  
+• PR interval analysis
+• QTc calculation
+    │
+    ▼
+Transformer Encoder (global temporal context)
+• Multi-head self-attention (8 heads)
+• Positional encoding for temporal sequence
+    │
+    ▼
+Classification Head
+• 3-class output: Normal / Hypokalemia / Hyperkalemia
+• Softmax probability scores
+```
+
+---
+
+## Performance | 模型效能
+
+Validated on clinical dataset from nephrology and hemodialysis centers (IRB approved):
+
+| Metric | Value |
+|--------|-------|
+| Overall Accuracy | 91.2% |
+| Hyperkalemia Sensitivity | 88.0% |
+| Hyperkalemia Specificity | 93.5% |
+| Hypokalemia Sensitivity | 85.4% |
+| AUC (Hyperkalemia) | 0.947 |
+| AUC (Hypokalemia) | 0.921 |
+
+---
+
+## Input Format | 輸入格式
+
+| Property | Specification |
+|----------|--------------|
+| ECG Leads | 12-lead (or 1/3/6-lead) |
+| Sampling Rate | 500 Hz (recommended) |
+| Duration | 10 seconds standard |
+| File Formats | .CSV, .EDF, HL7 |
+
+---
+
+## Intended Use | 適用範圍
+
+✅ **Intended for:**
+- Clinical potassium risk screening support
+- Hemodialysis patient continuous monitoring
+- Rural clinic and remote care settings
+- Emergency department rapid assessment
+
+❌ **Not intended for:**
+- Replacing laboratory serum potassium testing
+- Standalone diagnosis without physician review
+- Pediatric patients (not validated)
+
+---
+
+## Limitations & Disclaimer | 限制與免責聲明
+
+> ⚠️ This model is intended as a **clinical decision support tool only**.  
+> All outputs must be confirmed by a licensed physician before clinical action.  
+> Currently under **TFDA SaMD** regulatory review for medical device software certification.
+
+---
+
+## Privacy & Compliance | 隱私與法規
+
+- Model trained on **IRB-approved, de-identified** clinical ECG data
+- Core model weights and training pipeline stored in **private repository** under TFDA SaMD compliance controls
+- Patient data is **never stored** beyond the analysis session
+- Compliant with **ISO 27001** information security standards
+
+---
+
+## Pre-training Datasets | 預訓練資料集
+
+| Dataset | Records | Usage |
+|---------|---------|-------|
+| [PhysioNet PTB-XL](https://physionet.org/content/ptb-xl/) | 21,799 ECGs | Pre-training |
+| [MIMIC-IV-ECG](https://physionet.org/content/mimic-iv-ecg/) | 800,000+ ECGs | Feature extraction pre-training |
+| Internal Clinical (IRB) | 2,847 ECGs | Fine-tuning & validation |
+
+---
+
+## Citation | 引用
+
+If you reference this work, please cite:
+
+```bibtex
+@software{kalisense2025,
+  title     = {KaliSense AI: Non-Invasive Serum Potassium Anomaly Detection},
+  author    = {Precise Intelligent Biotech Co., Ltd.},
+  year      = {2025},
+  url       = {https://huggingface.co/precise-biotech/kalisense-ecg-potassium}
+}
+```
+
+---
+
+## Contact & Collaboration | 合作洽詢
+
+🏢 **Precise Bigdata生技股份有限公司 | Precise Intelligent Biotech Co., Ltd.**  
+📧 contact@precisebiotech.ai  
+🌐 www.precisebiotech.ai  
+💻 GitHub: [github.com/curry1982/kalisense-ai](https://github.com/curry1982/kalisense-ai)