Update README.md

README.md

@@ -1,3 +1,56 @@
 ---
 license: mit
+language:
+- en
+tags:
+- medical
+- icd10
+- medical coding
+- clinical
+- healthcare
+- icd10dx
+- CAC
+widget:
+- text: >-
+    Chief Complaint: John has been experiencing shortness of breath and mild
+    chest pain for the past two days.
+
+    Medical History: He has a history of hypertension, which is currently under
+    control with medication. Additionally, he was diagnosed with Type 2 Diabetes
+    five years ago and had an episode of bronchitis last year.
+
+    Examination Findings: During the examination, it was noted that Johns heart
+    rate was elevated, and his blood pressure was slightly high at 150/95 mmHg.
+    His respiratory rate was above normal limits, and his oxygen saturation was
+    94% on room air.
+
+    Investigations: An ECG was performed, which thankfully did not show any
+    significant ST changes, typically indicative of acute coronary syndrome. A
+    chest X-ray was also done, revealing no acute cardiopulmonary processes.
+
+    Assessment and Plan: Given the elevated heart rate and hypertension, coupled
+    with his shortness of breath, there is a concern for the early signs of
+    congestive heart failure. However, the absence of acute changes in the ECG
+    and the normal chest X-ray findings are reassuring. It is important to
+    consider his history of hypertension and diabetes in the overall assessment,
+    as these factors elevate his risk for cardiovascular complications.
+
+    A conservative approach is recommended at this stage. We will start diuretic
+    therapy to manage potential fluid overload and schedule an echocardiogram to
+    further assess his cardiac function. In the meantime, his blood pressure and
+    blood glucose levels will be closely monitored. A follow-up appointment is
+    scheduled for next week, with instructions to return earlier if his symptoms
+    worsen.
+  example_title: Example 1
+- text: 'Impression: fever, chills, cough, chest pain, shortness of breath, N/V.'
+  example_title: Example 2
 ---
+# DX Coding Training Model
+
+## Introduction
+This model is focused on automatically generating DX (Diagnostic) codes using a Named Entity Recognition (NER) approach. It's trained on private data at the token level to ensure precision in identifying relevant medical entities.
+
+## Model Details
+- **Approach**: Named Entity Recognition (NER)
+- **Training Data**: Private dataset, token-level annotations
+- **Primary Use-case**: Generating DX codes from medical texts