Market Analysis: Clinical Knowledge Graph Technology The global clinical knowledge graph (CKG) technology market is projected to reach $X million by 2033, exhibiting a CAGR of XX% during the forecast period 2025-2033. Key drivers fueling this growth include the increasing adoption of artificial intelligence (AI) and machine learning (ML) in healthcare, the rising demand for personalized medicine, and the need to improve the efficiency of medical research and development. Other factors contributing to market expansion are the growing awareness of the benefits of CKGs and the increasing availability of healthcare data. The market for CKG technology is segmented based on type (structured and unstructured), application (medical diagnosis and treatment, drug discovery, others), and region (North America, South America, Europe, Middle East & Africa, and Asia Pacific). North America is expected to dominate the market throughout the forecast period due to the high adoption of AI and ML in healthcare and the presence of well-established healthcare infrastructure. The Asia Pacific region is projected to experience the fastest growth during the forecast period due to the increasing healthcare expenditure and the growing awareness of the benefits of CKGs. Key players in the market include Raapid, Datavid, Wisecube AI, Cambridge Semantics, Ontotext, and Elsevier.

The Clinical Knowledge Graph (CKG) technology market is experiencing robust growth, driven by the increasing need for efficient data management and interoperability within the healthcare sector. The market's expansion is fueled by several key factors, including the rising volume of healthcare data, the imperative to improve patient care through data-driven insights, and the increasing adoption of precision medicine initiatives. The substantial amount of unstructured data generated by electronic health records (EHRs), medical imaging, and genomic sequencing presents a significant challenge. CKG technology offers a solution by integrating and contextualizing this diverse data into a unified, semantically rich knowledge base. This allows healthcare providers, researchers, and pharmaceutical companies to gain actionable insights for improved diagnostics, treatment planning, drug discovery, and clinical research. Furthermore, regulatory pressures promoting data interoperability and standardization are accelerating CKG adoption. While initial investments in infrastructure and expertise represent a barrier to entry, the long-term benefits of improved efficiency and reduced costs significantly outweigh the upfront investment. We project continued strong growth, driven by expanding applications in areas like personalized medicine, clinical decision support systems, and public health surveillance. Market segmentation reveals strong growth in applications like clinical decision support systems and drug discovery, alongside increasing demand for CKG technologies across various healthcare settings, including hospitals, pharmaceutical companies, and research institutions. The North American market currently holds a significant share, owing to early adoption and advanced technological infrastructure. However, other regions, particularly in Europe and Asia-Pacific, are witnessing rapid growth as healthcare systems increasingly prioritize data-driven approaches and digital transformation initiatives. While competition is growing, several established players and emerging companies are actively shaping the market landscape. This competition is leading to innovation and cost optimization, further benefiting the end-users. The market's future trajectory hinges on ongoing technological advancements, regulatory support, and increased awareness among healthcare professionals regarding the value proposition of CKGs. Factors such as data security and privacy concerns will continue to be addressed as the market evolves.

Patient-drug-disease (PDD) Graph dataset, utilising Electronic medical records (EMRS) and biomedical Knowledge graphs. The novel framework to construct the PDD graph is described in the associated publication.PDD is an RDF graph consisting of PDD facts, where a PDD fact is represented by an RDF triple to indicate that a patient takes a drug or a patient is diagnosed with a disease. For instance, (pdd:274671, pdd:diagnosed, sepsis)Data files are in .nt N-Triple format, a line-based syntax for an RDF graph. These can be accessed via openly-available text edit software.diagnose_icd_information.nt - contains RDF triples mapping patients to diagnoses. For example:(pdd:18740, pdd:diagnosed, icd99592),where pdd:18740 is a patient entity, and icd99592 is the ICD-9 code of sepsis.drug_patients.nt- contains RDF triples mapping patients to drugs. For example:(pdd:18740, pdd:prescribed, aspirin),where pdd:18740 is a patient entity, and aspirin is the drug's name.Background:Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Faced with patients' symptoms, experienced caregivers make the right medical decisions based on their professional knowledge, which accurately grasps relationships between symptoms, diagnoses and corresponding treatments. In the associated paper, we aim to capture these relationships by constructing a large and high-quality heterogenous graph linking patients, diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework to extract important medical entities from MIMIC-III (Medical Information Mart for Intensive Care III) and automatically link them with the existing biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD graph presented in this paper is accessible on the Web via the SPARQL endpoint as well as in .nt format in this repository, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.De-identificationIt is necessary to mention that MIMIC-III contains clinical information of patients. Although the protected health information was de-identifed, researchers who seek to use more clinical data should complete an on-line training course and then apply for the permission to download the complete MIMIC-III dataset: https://mimic.physionet.org/

Knowledge graph embeddings capture portable medical knowledge to power clinical AI

The global Medical Knowledge Graph market size is estimated to be valued at USD XX million in 2025, and is projected to reach USD XX million by 2033, growing at a CAGR of over XX% during the forecast period (2025-2033). This growth is attributed to the rising adoption of medical knowledge graphs in the healthcare industry, the increasing need for accurate and timely medical information, the growing awareness about the benefits of medical knowledge graphs, and the increasing investments in healthcare IT. The increasing adoption of medical knowledge graphs in the healthcare industry is driven by the need for accurate and timely medical information. Medical knowledge graphs can help healthcare professionals to quickly and easily find the information they need to make informed decisions about patient care. They can also help healthcare professionals to identify potential risks and complications, and to develop personalized treatment plans. The growing awareness about the benefits of medical knowledge graphs is also contributing to the growth of the market. Healthcare organizations are realizing that medical knowledge graphs can help them to improve patient outcomes, reduce costs, and increase efficiency. The increasing investments in healthcare IT are also providing a boost to the growth of the medical knowledge graph market. Healthcare organizations are investing in healthcare IT solutions to improve the quality of care they provide, and medical knowledge graphs are an important part of this investment. Gain insights into the rapidly growing Medical Knowledge Graph market, valued at over USD 115 million in 2023. This comprehensive report explores the market's concentration, product insights, regional trends, drivers, challenges, and leading players.

This study integrates the MIMIC-III and CORAL electronic health records into knowledge graphs to enhance their utility for advanced medical analysis and decision-making. MIMIC-III contains comprehensive data from over 40,000 patients, while CORAL focuses on oncology-specific information from 40 patients, aiding in complex medical reasoning. We used a LLM (Large Language Model)-based Named Entity Recognition approach to extract relevant medical information from these datasets, independently verified by domain experts, and constructed the AIPatient and CORAL Knowledge Graph in Neo4j. This graph supports the AIPatient system, which simulates patient interactions for advanced decision support. Additionally, we introduce MIMIC-III and CORAL Question and Answering sets, which are created for evaluating system performance such as accuracy, robustness and stability.

We introduce the PolyMed dataset, designed to address the limitations of existing medical case data for Automatic Diagnosis Systems (ADS). ADS assists doctors by predicting diseases based on patients' basic information, such as age, gender, and symptoms. However, these systems face challenges due to imbalanced disease label data and difficulties in accessing or collecting medical data. To tackle these issues, the PolyMed dataset has been developed to improve the evaluation of ADS by incorporating medical knowledge graph data and diagnosis case data. The dataset aims to provide comprehensive evaluation, include diverse disease information, effectively utilize external knowledge, and perform tasks closer to real-world scenarios.

We have also made the data collection tools publicly available to enable researchers and other interested parties to contribute additional data in a standardized format. These tools feature a range of customizable input fields that can be selectively utilized according to the user's specific requirements, ensuring consistency and professionalism in the data collection process.

All train and test code of our data available in https://github.com/krchanyang/PolyMed

The Medical Knowledge Graph market is experiencing robust growth, driven by the increasing need for efficient and accurate access to complex medical information. The convergence of big data analytics, artificial intelligence (AI), and advanced natural language processing (NLP) technologies is fueling this expansion. Healthcare providers, pharmaceutical companies, and research institutions are increasingly leveraging knowledge graphs to improve diagnostic accuracy, personalize treatment plans, accelerate drug discovery, and enhance patient care. A projected Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033 suggests a significant market expansion, with the market size estimated to reach $8 billion by 2033, from a $2 billion valuation in 2025. This growth is further propelled by the rising adoption of electronic health records (EHRs), the growing volume of medical literature, and the imperative to improve healthcare outcomes through data-driven insights. Key market segments include applications such as clinical decision support, drug discovery, and patient engagement, with types ranging from cloud-based to on-premise solutions. While high implementation costs and data security concerns pose challenges, the long-term benefits in terms of improved efficiency, reduced errors, and enhanced patient safety are driving widespread adoption. North America currently holds the largest market share, driven by advanced technological infrastructure and significant investments in healthcare IT. However, rapidly developing economies in Asia-Pacific, particularly China and India, are emerging as key growth regions, fueled by increasing healthcare spending and government initiatives promoting digital health. The competitive landscape features both established healthcare IT companies and emerging startups, fostering innovation and competition within the sector. Furthermore, strategic partnerships and collaborations between technology companies and healthcare providers are becoming increasingly important, accelerating the development and adoption of Medical Knowledge Graph solutions.

This dataset capures statistical analysis of the HCHS cohort study using a knowledge graph and dashboard. Properties of 10,000 participants were analyzed for their association with cardiovascular disease as well as for their relationships among each other. The data is presented in the form of Neo4J database dumps and can be explored following the given user guide.

Imagine having a knowledge graph that can extract medical health knowledge related to patient diagnosis solutions and treatments from thousands of research papers, distilled using machine learning techniques in healthcare applications. Medical doctors can quickly determine treatments and medications for urgent patients, while researchers can discover innovative treatments for existing and unknown diseases. This would be incredible! Our approach serves as an all-in-one solution, enabling users to employ a unified design methodology for creating their own knowledge graphs. Our rigorous validation process involves multiple stages of refinement, ensuring that the resulting answers are of the utmost professionalism and solidity, surpassing the capabilities of other solutions. However, building a high-quality knowledge graph from scratch, with complete triplets consisting of subject entities, relations, and object entities, is a complex and important task that requires a systematic approach. To address this, we have developed a comprehensive design flow for knowledge graph development and a high-quality entities database. We also developed knowledge distillation schemes that allow you to input a keyword (entity) and display all related entities and relations. Our proprietary methodology, multiple levels refinement (MLR), is a novel approach to constructing knowledge graphs and refining entities level-by-level. This ensures the generation of high-quality triplets and a readable knowledge graph through keyword searching. We have generated multiple knowledge graphs and developed a scheme to find the corresponding inputs and outputs of entity linking. Entities with multiple inputs and outputs are referred to as joints, and we have created a joint-version knowledge graph based on this. Additionally, we developed an interactive knowledge graph, providing a user-friendly environment for medical professionals to explore entities related to existing or unknown treatments/diseases. Finally, we have advanced knowledge distillation techniques.

Polymed-QA

Synthetically generated QA pairs from the Polymed dataset. Used to train Aloe-Beta model.

  Dataset Details





  Dataset Description

PolyMed is a dataset developed to improve Automatic Diagnosis Systems(ADS). This dataset incorporates medical knowledge graph data and diagnosis case data to provide comprehensive evaluation, diverse disease information, effective… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/Polymed-QA.

Biomedical knowledge graphs, which can help with the understanding of complex biological systems and pathologies, have begun to play a critical role in medical practice and research. However, challenges remain in their embedding and use due to their complex nature and the specific demands of their construction. Existing studies often suffer from problems such as sparse and noisy datasets, insufficient modeling methods, and non-uniform evaluation metrics.

In this work, we established a comprehensive knowledge graph (KG) system for the biomedical field in an attempt to bridge the gap. Here we introduced PharmKG, a multi-relational, attributed biomedical knowledge graph, composed of more than 500,000 individual interconnections between genes, drugs, and diseases, with 29 relation types over a vocabulary of ~8,000 disambiguated entities. Each entity in PharmKG is attached with heterogeneous, domain-specific information obtained from multi-omics data, i.e. gene expression, chemical structure, and disease word embedding while preserving the semantic and biomedical features. For baselines, we offered 9 state-of-the-art knowledge graph embedding (KGE) approaches and a new biological, intuitive, graph neural network-based KGE method that uses a combination of both global network structure and heterogeneous domain features. Based on the proposed benchmark, we conducted extensive experiments to assess these KGE models using multiple evaluation metrics. Finally, we discussed our observations across various downstream biological tasks and provide insights and guidelines for how to use a knowledge graph in biomedicine. We hope that the unprecedented quality and diversity of PharmKG will lead to advances in biomedical knowledge graph construction, embedding, and application.

Online

Website

Github

DataHub

SPARQL endpoint

You can query some of the data online there. There is also the download link. Of course you can download it here.

Context

Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patients symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis, and treatments. We aim to capture these relationships by constructing a large and high-quality heterogeneous graph linking patients, diseases, and drugs (PDD) in EMRs.

Content

Specifically, we extract important medical entities from MIMIC-III (Medical Information Mart for Intensive Care III) and automatically link them with the existing biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD graph presented is accessible on the Web via the SPARQL endpoint, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.

A subgraph of PDD is illustrated in the followng figure to betterunderstand the PDD graph.

https://github.com/wangmengsd/pdd-graph/raw/master/example.png" alt="enter image description here">

Acknowledgements

Author

Data set belongs to Meng Wang, Jiaheng Zhang, Jun Liu,Wei Hu, Sen Wang, , Wenqiang Liu and Lei Shi

They come from： 1. MOEKLINNS lab, Xi’an Jiaotong University, Xi’an, China 2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 3. Griffith Universtiy, Gold Coast Campus, Australia

Some Email： - Meng Wang：wangmengsd@stu.xjtu.edu.cn - Lei Shi：xjtushilei@foxmail.com - Jun Liu：liukeen@xjtu.edu.cn

Research

The paper is being reviewed and is not easily disclosed.So it can't be linked here.

Inspiration

If you have any questions, please contact the email address above.

Do you have any suggestions ? And send them to an e-mail address above.

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

### If your article needs to be reference our work , you can reference our github.

The Clinical Connections KG is created and maintained by the Multiomics Provider team from the Institute for Systems Biology in Seattle, WA. This KP provides a knowledge graph pointing from risk factors to a variety health outcomes (diseases, phenotypes, medication exposure). We use data from over 28 million Electronic Health Records (EHRs) to train a large collection of interpretable machine learning models which are integrated into a single large knowledge graph. The edges of the graph are generated by running ~300 logistic regression models for clinical conditions with features including age, sex, medical conditions and medications as nodes to predict associations with disease outcome.The Data consist of over 28 million EHR records Providence Health Systems and Affiliates (PSHA), which cares for patients through 51 hospitals and 1085 clinics across seven states in the US, including Alaska, California, Montana, New Mexico, Oregon, Texas, and Washington.The KG includes results from 152 multivariate logistic regression models, which includes 152 conditions, 335 medications, 115 lab measurements, and 5 demographic features. Log odds ratios are used to quantify associations between concepts. The AUROC for each model is provided, along with the 95% confidence intervals and p-values for each association.Features are indicated by a binary (0/1) for whether or not they are present in a person's medical history. Laboratory features are coded as high/low relative to the reference range at the time it was entered into the EHR. The specification of (1,0) or (0,1) indicates the lab result was high or low, respectively, while "normal" (as defined by the reference ranges) or the absence of lab result are mapped to (0,0). Laboratory values that were split into high or low were then mapped from LOINC codes to HPO phenotypes. Demographic features include age groups (0-17, 18-49, 50-74, and 75+ years old), sex (Female = 0), and ethnic group (Hispanic or Latino = 1).Graph PropertiesDisease nodes use Monarch Disease Ontology (MONDO) or Human Phenotype Ontology (HPO) identifiers, depending on the nature of the disease.Medication nodes use CHEMBL or CHEBI identifiers, depending on the nature of the medication.Laboratory results use the LOINC2HPO tool to map LOINC codes to HPO identifiers.Edge predicates are "associated_with_increased_likelihood_of" if the coefficient is positive and "associated_with_decreased_likelihood_of" if the coefficient is negative.Example edge (interpretation): The KG shows that rosuvastatin is associated with an increased likelihood of chronic ischemic heart disease, with a log odds ratio of 3.4278 and a p value of < 0.001 (N = 51200) in a cohort of patients from PHSA.

This dataset capures statistical analysis of the HCHS cohort study using a knowledge graph and dashboard. Properties of 10,000 participants were analyzed for their association with cardiovascular disease as well as for their relationships among each other. The data is presented in the form of Neo4J database dumps and can be explored following the given user guide.

Test knowledge graph of the MatKG construction pipeline for digital coaching, developed in the scope of the GATEKEEPER project.

Walking.json includes a sample dataset from Samsung Health in its raw format. The dataset includes observations about physical activity. Walking.fhir.json includes the same data in the FHIR format, generated by the first step of the MatKG pipeline. Walking.norm.json includes a the result of the pre-processing step required to apply the RML mapping rules (second transformation step from FHIR to the HeliFit ontology). Walking.turtle includes the final result of the transformation pipeline in turtle format.

The global knowledge graph technology market is projected to reach a value of USD 4.7 billion by 2033, exhibiting a CAGR of 10.3% from 2025 to 2033. The surge in data volume and the increasing adoption of artificial intelligence (AI) and machine learning (ML) are the key factors driving the growth of this market. The increasing need for effective data management and analysis is also contributing to the market's expansion. Key market trends include the shift towards unstructured knowledge graphs, the integration of knowledge graphs with natural language processing, and the increasing use of knowledge graphs in enterprise applications. Based on type, the market is segmented into structured knowledge graphs and unstructured knowledge graphs. Structured knowledge graphs are more common and are used in a wide range of applications, including search engines, question answering systems, and recommender systems. Unstructured knowledge graphs are less common but are becoming increasingly popular as they can represent more complex and nuanced relationships. Based on application, the market is segmented into medical, finance, education, and others. The medical segment is the largest and is expected to continue to grow as knowledge graphs are used to improve patient care and outcomes. The finance segment is also growing rapidly as knowledge graphs are used to improve risk management, fraud detection, and customer segmentation. The education segment is also growing as knowledge graphs are used to improve student learning and engagement.

Knowledge Graph Market Overview The global Knowledge Graph market is projected to reach USD 3,229.5 million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The increasing demand for enterprise knowledge graph platforms, knowledge graph as a service, and embedded knowledge graphs drives market growth. Additionally, the surge in applications across various sectors, including finance, government, medical, and internet, further contributes to the market expansion. Market Dynamics The adoption of knowledge graphs empowers organizations with improved decision-making, enhanced data analysis, and personalized user experiences. The growing need for efficient knowledge management, the rise of artificial intelligence and machine learning technologies, and the increasing investment in data infrastructure are key factors driving market growth. However, the challenges of data integration and interoperability, along with the need for domain expertise, can restrain market progress. The market is segmented by type (enterprise knowledge graph platform, knowledge graph as a service, embedded knowledge graph) and application (finance, government, medical, internet, others). North America dominates the market, followed by Europe and Asia Pacific. Major players include Iflytek, Smartech, Tongdun, Data Grand, Knowlegene, Suoxinda Holdings, MingGlamp Technology, Star Graph, Utry Information, TAIJI, and others.

Description: The Clinical Trials KG, created and maintained by the Multiomics Provider, provides information on Clinical Trials, ultimately derived from researcher submissions to clinicaltrials.gov, via the Aggregate Analysis of Clinical Trials (AACT) database). Information on select trials includes the NCT Identifier of the trial, interventions used, diseases/conditions relevant to the trial, adverse events, etc.Example: The Multiomics Clinical Trials KG reports that warfarin was used as an intervention for end-stage renal failure in a (completed) phase 3 clinical trial with 170 participants (NCT Identifier NCT00157651), and in a (currently recruiting) phase 4 clinical trial anticipating recruitment of 718 participants (NCT03862859).The graph currently uses biolink:in_clinical_trials_for and biolink:treats as predicates. Node categories include biolink:SmallMolecule, biolink:ChemicalEntity, and biolink:MolecularMixture for the interventions, and biolink:Disease and biolink:PhenotypicFeature for the conditions.

The coronavirus disease (COVID-19) spread rampantly around the world at the beginning of 2020 before the governments of each country could prevent it by making decisions based on medical data analysis. With proper formalization, the terabytes of new textual data available online every day could have been used for the early description and detection of cases of this virus. Since then, the number of Event-Based Surveillance (EBS) applications has increased exponentially. These applications aim to mine channels of unstructured information to detect signs of possible public health events' progression. However, one problem with such systems is the need for expert intervention to define which event will be captured, which relevant terms should be used in the search, and to analyze the events to modify the search procedure constantly. Another problem is that many of these applications do not consider both spatial and temporal characteristics. Addressing such limitations, this datasets presents a novel approach. We propose the use of BioPropaPhenKG to replace such systems. In this dataset, BioPropaPhen was enhanced with information comming from unstructured texts from online newspapers and medical articles. BioPropaPhenKG, its ontology and other useful information can be found in https://zenodo.org/records/10911980. The code used for this use case can be found in https://github.com/Gabriel382/DDPF-Health-Risks . Finally, the datasets used where UMLS MetamorphoSys, OpenStreetMaps, Wikidata, Aylien (data only from November of 2019) and CORD-19 (data only from December of 2019).

To read, you just need to load it with Neo4j:4.4.3. Alternatively, you can open it with docker using the following command:

docker run --interactive --tty --rm \
--publish=7474:7474 --publish=7687:7687 \
--volume=/path-to-data-folder:/data --user="$(id -u):$(id -g)"\
neo4j:4.4.3 \
neo4j-admin load --from=/data/BioPropaPhenKG-Journal-Medical.dump --database "neo4j" --force