Capital Medical University Seeks $125,000 Licensing Deal for Breakthrough Pediatric Disease Coding Conversion Patent

Recently, the China Technology Exchange and the Beijing Intellectual Property Trading Center jointly released a public notice on the trading of state-owned scientific and technological achievements. Beijing Children’s Hospital, Capital Medical University, intends to license its authorized invention patent"A Disease Code Conversion Method and System"Execute a license agreement with the partner, with the proposed transaction amount for this deal being RMB900,000 yuan. The inventor of this patented technology isNi Xin and His Team。

Core Transaction Details of the PatentCovers a Complete Technical Solution for Disease Code Conversion, includingEncoding Conversion Method, Corresponding System, Adapted Electronic Device, and Computer-Readable Storage Medium, breaking through the bottlenecks of existing technologies and innovativelyAchieved end-to-end standardized conversion of disease diagnosis codes from the National Clinical Version 2.0 to ICD-O-3.1 and then to ICCC-3, with its technological innovations concentrated in four key areas:

First, establishedCovering disease diagnosis codes, pathology diagnosis codes, and the mapping between thema triple-standard quality control system that ensures the accuracy of original coding through data preprocessing and rule-based validation;

Second, we designedMechanism for the Ordered Matching of Tumor Diagnosis with Pathological Diagnosis Results, achieve precise mapping of multiple sets of diagnostic results according to the priority of the primary diagnosis;

Third, we have establishedFlexible Conversion Rules Based on Multi-Relationship Tables and Dictionary Tables, it can output multi-level coding results at the major, minor, and detailed category levels through logic such as code truncation and adaptation to special scenarios;

Fourth, achievedFull-Process Data Storage for Conversion, covering matching dictionaries, raw data, process data, and result data, to support the traceability and reuse of medical data.

Disease CodeIt serves as the core vehicle for standardizing disease classification and achieving information normalization in the healthcare sector. As an early coding standard uniformly adopted by tertiary public hospitals nationwide, the National Clinical Version 2.0 encompasses the "National Clinical Version 2.0 of ICD Codes" and the "National Clinical Version 2.0 of Procedure Codes," aiming to promote the unification of disease and surgical procedure coding across China.

The International Classification of Diseases (ICD) system, developed by the World Health Organization (WHO), classifies and codes diseases based on characteristics such as etiology, pathology, clinical manifestations, and anatomical location. It serves as a foundational support for medical data mining, Diagnosis-Related Groups (DRG), performance evaluation, and health insurance payment and reimbursement.

In addition,International Classification of Childhood Cancer (ICCC)As a specialized coding system exclusively for pediatric malignant tumors (including benign tumors of the central nervous system and germ cells), developed based on the International Classification of Diseases for Oncology (ICD-O), it comprises 12 major categories and 48 subcategories. Its unified classification standard is to carry outResearch on the Etiology, Treatment Optimization, Survival Analysis, and Epidemiological Trend Monitoring of Pediatric Tumorsthe key to providing core data support for improving the prevention and treatment of pediatric tumors.

In current clinical practice, disease code conversion primarily relies on traditional technical approaches. For instance, some methods require the prior collection of multi-version standard disease codes and diagnostic descriptions to establish a standard dictionary database and test set, followed by steps such as constructing vector space models and calculating code similarities to achieve conversion. However, these existing solutions have significant drawbacks:

On one hand, the workflow is extremely complex, involving specialized operations such as building dictionary libraries and constructing vector models. This imposes high demands on users’ professional expertise, making it difficult to popularize and apply in routine medical settings. On the other hand, its functional limitations are significant: it does not support DRG grouping or collaborative coding, lacks a comprehensive maintenance mechanism, and fails to meet the needs for adaptation across diverse clinical scenarios.

More critically, existing technologies have yet to achieve a full-chain standardized conversion of disease diagnosis codes from the National Clinical Version 2.0 to ICD-O-3.1 and then to ICCC-3. In practical applications, medical institutions across different regions often implement differentiated expansions of the National Clinical Version 2.0 codes based on local clinical characteristics. Coupled with the inherent discrepancies in rules and terminological descriptions among different coding versions, this results in multiple coding representations for the same disease, severely hindering the interoperability and in-depth mining of medical data across institutions and regions.

There is an urgent need for coding conversion technology in the current clinical field:National authorities have explicitly mandated the timely completion of coding dictionary conversion to ensure the consistency and accuracy of data collected on medical record front pages. The healthcare industry must rely on standardized coding to facilitate core operations, including DRG-based health insurance payment settlement and medical performance evaluation. In the field of pediatric oncology diagnosis and treatment, the uniform application of ICCC-3 coding is even more critical for the precise collection of tumor surveillance data, thereby providing a reliable data foundation for etiological research, optimization of treatment protocols, and prognostic analysis.

In this context, the development of a disease coding conversion technology—characterized by streamlined processes, strong adaptability, and the capability to achieve full-chain conversion across multiple coding versions—has become key to addressing industry pain points and meeting the dual demands of clinical practice and scientific research.

Leveraging end-to-end standardized design and multi-dimensional innovation, this disease coding conversion technology and system establishes an efficient and precise solution for code transformation. Its core advantages and innovations are primarily reflected in the following four aspects:

First, we innovatively established a triple-standard quality control system to ensure coding accuracy from the source.This technology pioneers comprehensive, full-dimensional quality control by integrating disease diagnosis codes, pathology diagnosis codes, and their mapping relationships. Through preprocessing of codes and names (e.g., filtering special characters, accommodating case sensitivity and various delimiters, and supporting conversion between Roman numerals and English letters) alongside rigorous rule-based validation (e.g., ensuring code-name pairs conform to the National Clinical Version 2.0 dictionary, restricting prohibited codes and names, and verifying the logical consistency of diagnostic mappings), it thoroughly resolves issues of disorganized raw data formats and logical contradictions inherent in traditional conversion methods. This provides a highly reliable data foundation for subsequent conversions.

Second, design an orderly matching mechanism to achieve precise correspondence among multiple sets of diagnostic results.In clinical scenarios involving multiple concurrent diagnostic results for tumor diagnosis and treatment, the technology screens tumor diagnostic outcomes and ranks them by primary diagnosis priority. It then performs one-to-one matching and conversion between pathological diagnostic results and the correspondingly ranked tumor diagnostic results, thereby avoiding coding mismatches in multi-diagnosis settings. This approach significantly enhances conversion adaptability in complex clinical scenarios, a design feature not yet seen in existing technologies.

Third, establish a flexible and adaptable system of conversion rules to achieve breakthroughs in end-to-end coding transformation.This innovation creatively integrates the disease diagnosis conversion table, pathological diagnosis conversion table, and ICCC-3 coding dictionary. By employing rules such as key-digit code extraction, adaptation to special scenarios (e.g., adjusting matches when the first match starts with C46 and meets specific conditions), and multi-dimensional collaborative code lookup, it achieves, for the first time, a full-chain standardized transformation from the National Clinical Version 2.0 to ICD-O-3.1 and then to ICCC-3. Simultaneously, it outputs multi-level coding results at the major, minor, and detailed categories, perfectly meeting the needs of various scenarios such as DRG grouping and pediatric oncology research, thereby addressing the limitations of existing technologies in covering comprehensive coding system transformations.

Fourth, establish a full-process data storage mechanism to support data traceability and reuse.The technology comprehensively stores matching dictionary data, raw data, process data (such as individual match items), and result data (including ICD-10, ICD-O-3.1 multi-dimensional codes, ICCC-3 codes, etc.) generated during the conversion process. This not only ensures the traceability of code conversion but also provides data support for subsequent medical data mining and optimization of coding rules. Furthermore, when integrated with a closed-loop design involving review and optimization by medical experts, it further enhances the practicality and accuracy of the technology.

Multiple patented technologies have already been implemented in the field of disease code conversion, covering various technical directions such as algorithmic models, code mapping, and end-to-end conversion. Among these, the patented technologies held by institutions such as Fuwai Hospital of the Chinese Academy of Medical Sciences, Shenyang Neusoft Intelligent Medical Technology Research Institute, and Guangdong Bohua Ultra-High Definition Innovation Center are representative. These technologies feature distinct functional mechanisms that complement one another within the industry. Meanwhile, driven by the advancement of medical digitalization and standardization, products related to disease code conversion demonstrate significant market value and development potential.

Regarding the functional mechanisms of core patented technologies,Fuwai Hospital, Chinese Academy of Medical Sciences: "A Method and System for Automatic Cataloging of Disease Codes"Centered on an AI model, the approach first constructs training data comprising medical texts and disease coding information. It then builds a model consisting of modules for medical text feature extraction, disease code feature extraction, and label attention mechanism-based feature fusion. By mining associations among ICD codes using an undirected weighted graph and combining this with features extracted from medical texts via pre-trained models, the system outputs predicted coding probabilities after feature fusion. Automatic ICD coding is completed by comparing these probabilities against a threshold, thereby achieving intelligent generation of disease codes from medical texts.

"A Method for Implementing Medical Coding Mapping" by Shenyang Neusoft Intelligent Medical Technology Research InstituteFocusing on precise matching between codes, this approach obtains combinations of source medical codes and standard medical codes, calculates the matching scores for codes and terms separately, and computes a weighted sum. It then establishes a mapping relationship between the source codes and the standard code combination with the highest matching score, thereby resolving the interoperability issues between different coding systems.

Guangdong Bohua Ultra-High-Definition Innovation CenterNewly Applied in 2025“Automatic International Classification of Diseases Coding Method Based on Multi-Synonym Matching Network”This approach integrates a multi-synonym matching network with a multimodal large language model. It first constructs a medical text database to train algorithmic models, then completes feature extraction and encoding conversion through encoders and decoders, while simultaneously optimizing model parameters to enhance matching accuracy, thereby achieving efficient and precise automated ICD coding conversion. Meanwhile, the patented technology of Beijing Children’s Hospital, Capital Medical University, represents a specialized breakthrough in the field of pediatric oncology. Through four core designs—triple-standard quality control, ordered matching, full-chain conversion, and end-to-end storage—it achieves, for the first time, the exclusive coding transformation from the National Clinical Version 2.0 to ICD-O-3.1 and then to ICCC-3, filling the technical gap in coherent multi-version coding conversion within the pediatric oncology domain.

The market value of disease code conversion products is concentrated in three major dimensions: policy, clinical practice, and industry.At the policy level,The nation is promoting the interconnectivity of medical data, reforming DRG/DIP health insurance payment methods, and standardizing the front page of medical records, thereby clarifying requirements for code conversion in medical institutions; public hospitals at all levels and health insurance agencies have become the core procurement entities.At the clinical level,The product reduces manual coding workload and error rates, enhances the efficiency of data collection and analysis, provides standardized data support for specialty research in fields such as oncology and rare diseases, and its specialized offerings, such as those for pediatric oncology, precisely address critical pain points in specialty-specific data management;At the industry level,Medical digitalization has made data a core element, with coding conversion products serving as foundational tools for data governance. Their application scenarios span the entire industry chain, including healthcare institutions, medical big data enterprises, pharmaceutical R&D organizations, and commercial insurance providers.

As the demand for cross-regional and cross-institutional data sharing grows, the upgrading and productization of coding conversion technologies may become pivotal to healthcare digitalization. Specialized niche products and general-purpose solutions covering all scenarios will foster differentiated competition. Future optimizations in technical precision and expansion into diverse application scenarios will further enhance market penetration and commercial value, positioning this field as one of the core tracks in health technology.

* Patent transaction information provided by CSTT