ICD-10-CM Clinical Terminology Mapper

**ICD-10-CM Clinical Terminology Mapper**

This NLP model is designed to streamline the process of clinical coding in healthcare settings. It accurately maps diverse clinical findings to their corresponding ICD-10-CM codes, facilitating efficient and accurate medical billing and documentation.

**Key Features:**
- The model skillfully matches clinical findings with the most relevant ICD-10-CM codes, ensuring high accuracy in coding and reducing the risk of errors in medical claims and records.
- The model boasts a vocabulary that is quadruple the size of its non-augmented counterpart, achieved by integrating a comprehensive synonym database. This feature allows for a broader range of clinical terms and expressions to be effectively recognized and mapped.

**Extracted Entities** : Cerebrovascular Diseases, Communicable Diseases, Diabetes, Disease Syndromes and Disorders, EKG Findings, Heart Diseases, Hyperlipidemia, Hypertension, Imaging Findings, Injuries or Poisonings, Kidney Diseases, Obesity, Oncological Conditions, Overweight, Pregnancy-related Conditions, Psychological Conditions, Symptoms, Vital Sign Findings

This model improves the medical billing process by streamlining it through the provision of precise ICD-10-CM codes. This reduces claim denials and enhances revenue cycle management. It assists healthcare professionals in accurately documenting clinical findings, leading to improved quality of patient records in clinical documentation. Additionally, it facilitates health data analysis and research by providing standardized coding of clinical findings. This enables easier data aggregation and interpretation for research purposes.

**Additional Model Information**
- [Full model info on John Snow Labs Models Hub](https://nlp.johnsnowlabs.com/2024/08/22/icd10cm_resolver_pipeline_en.html)
- **Domain:** Clinical Text Analysis
- **Subdomain:** Terminology Codes
- **Predictable entities:** ICD 10 CM Codes

**How to run this model:**
1. Acquire a John Snow Labs Pay As You Go (PAYG) license from [Sales](mailto:[e-mail])
2. Import this listing.
3. See the attached notebook to deploy and use the model.

This model comes with optimized CPU and GPU builds. You can select which one to deploy via the notebook.

搜集汇总

背景与挑战

背景概述

该数据集是一个ICD-10-CM临床术语映射NLP模型，旨在通过准确匹配临床发现与对应ICD-10-CM代码，优化医疗计费和文档流程。它整合了扩展的词汇库以提升术语识别范围，并支持标准化编码，从而减少索赔错误并促进健康数据分析。