Update README.md

## Overview

**ProcessVenue** delivered a high-accuracy audio annotation dataset that converts natural patient-style conversations into structured, machine-learnable signals. Across 93 real audio clips, each conversation was labeled on 10 standardized clinical and behavioral attributes. Completed in 3 days by a 12-member team, the project reached **95.38%** overall accuracy, producing dependable supervision for healthcare AI training and evaluation.

## What We Built

We transformed raw spoken patient reviews into a clean, consistent dataset that captures:

What the patient is experiencing (dominant complaint, body location, sensation type)
How long and how severe it is (onset timing, pain rating, pattern, trend)
How the patient feels emotionally (overall emotion, stress level)
Whether safety-relevant language occurs (offensive language flag)

This dataset structures real conversations for AI understanding — not for diagnosis.

## Acknowledgement / Credit

This dataset is derived from the **Simulated Patient–Physician Medical Interviews** dataset hosted on Kaggle:
https://www.kaggle.com/datasets/lordpatil/simulated-patient-physician-medical-interviews.

We thank the Kaggle uploader **lordpatil** and acknowledge the original dataset authors who created the simulated OSCE-style patient–physician conversations. All rights and credit for the source audio/transcripts remain with the original creators. Please cite the original publication describing the dataset:

Fareez, F., Parikh, T., Wavell, C., et al. (2022). *A dataset of simulated patient-physician medical interviews with a focus on respiratory cases.* Scientific Data.

README.md

@@ -1,3 +1,15 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- audio-classification
+language:
+- en
+tags:
+- Speech_Processing
+- Healthcare
+- Clinical_Audio
+- Patient_Conversations
+pretty_name: ProcessVenue_Clinical_Audio_Annotation_Dataset
+size_categories:
+- n<1K
+---