Why Perform Clinical and Medical Data Mining?
Clinical and Medical Data Mining is essential in modern healthcare due to the vast data generated from sources like electronic health records and clinical trials. It enhances predictive accuracy, optimizes resource allocation, supports personalized medicine, reduces medical errors, and accelerates drug discovery. By identifying patterns in patient data, data mining helps improve diagnosis, treatment, and decision-making, ultimately leading to better patient outcomes. Its applications range from disease surveillance to clinical trial optimization and fraud detection, making it a critical tool for advancing medical research.
What Can We Offer ?
We offer a comprehensive Clinical and Medical Data Mining service designed to enhance clinical research. Our services include thorough electronic health record analysis to uncover clinical patterns, management and analysis of clinical trial data to ensure accuracy and integrity, and the discovery of biomarkers related to disease diagnosis and treatment. We also develop and optimize predictive models for early diagnosis and personalized treatment plans, alongside data integration and standardization from diverse sources to improve analysis quality. By leveraging advanced data mining techniques, we empower researchers with actionable insights to drive effective patient care and informed clinical decisions.
Workflow for Clinical and Medical Data Mining
Clinical Data Download
We provide extensive data download services across various healthcare research themes. Our offerings include access to public databases like CHARLS and CLHLS for elderly health data, nutritional surveys from the U.S. and other country, and cancer data from the SEER and TCGA database. We also facilitate downloads related to Mendelian randomization, emergency care databases such as MIMIC, and the Global Burden of Disease (GBD) database. These diverse datasets empower researchers to derive valuable insights and make informed, data-driven decisions in their fields.
What Are the Advantages of Our Service?
Expertise in Data Analysis
our team comprises highly skilled professionals who specialize in data mining and bioinformatics. This expertise enables us to analyze complex datasets effectively, ensuring accurate insights that inform clinical decisions.
Comprehensive Data Access
We provide access to a wide array of public databases, including longitudinal health studies, nutritional surveys, and cancer registries. This diverse data pool allows for a more thorough understanding of health trends, ultimately improving patient care.
Data Security and Privacy
Our service prioritizes data security, ensuring that all research data is handled in compliance with strict privacy standards. We use advanced encryption and secure protocols to protect your sensitive data throughout the entire data mining process, safeguarding the integrity and confidentiality of your research.
Enhanced Research Decision-Making
By leveraging advanced data mining techniques, we empower clients to discern patterns and associations among various research factors. This capability facilitates more precise predictions and decisions, driving innovation and deeper scientific insights.
Expert Support and Consultation
Our team of biological specialists provides expert guidance throughout the data analysis process. We assist clients in interpreting results and translating them into actionable insights, enhancing their research outcomes.
Customized Solutions
Every healthcare organization has unique data needs. We provide customized data mining solutions that are tailored to specific clinical or research objectives, ensuring that the insights generated are directly relevant to the goals at hand.
At CD Genomics, our Clinical Data Mining services are designed to support researchers in navigating the complexities of modern data-driven medicine. By leveraging our expertise in genomics, bioinformatics, and data science, we help organizations harness the full potential of their clinical data to drive innovation and improve patient outcomes.
What Does Clinical and Medical Data Mining Reveal?
Clinical and Medical Data Access and Cleaning Process
One of the challenges in clinical research is data acquisition, as some public databases require an application process to gain access. Our data mining service includes the acquisition of such data. After obtaining the clinical data, we perform data cleaning according to research requirements, excluding terms that do not meet the criteria. Subsequently, we proceed with data modeling to derive results. The following table presents the prevalence of diabetes across different ethnicities, genders, and age groups in the NHANES database.
Table 1. Prevalence of diabetes in NHANES database
| Variable | Prevalence, %(95% CI) |
|---|---|
| Ethnicity | |
| White | 10.6( 9.9-11.3) |
| Black | 14.6(13.6-15.6) |
| Mexican American | 13.5(11.9-15.2) |
| Sex | |
| Female | 10.4( 9.7-11.1) |
| Male | 12.7(11.9-13.5) |
| Age | |
| 20-25 y | 1.5( 0.9- 2.1) |
| 26-30 y | 1.8( 1.3- 2.4) |
| 31-35 y | 3.3( 2.5- 4.1) |
| 36-40 y | 5.2( 4.1- 6.3) |
| 41-45 y | 7.7( 6.4- 8.9) |
| 46-50 y | 11.4( 9.5-13.4) |
| 51-55 y | 14.5(12.3-16.7) |
| 56-60 y | 16.0(13.6-18.5) |
| 61-65 y | 23.4(20.9-25.8) |
| 66-70 y | 23.3(20.3-26.4) |
| 71-75 y | 25.7(23.0-28.5) |
| >75 y | 24.2(22.1-26.3) |
3D Graphs Displaying Clinical Data Trends
In the past ten years, the prevalence of diabetes has consistently increased across various ethnicities, genders, and age groups.
Figure 1. The trend of diabetes incidence from 2009 to 2018 by age groups and year. (Fang,2023)
Line Chart Classification Highlighting Clinical Data Characteristics
This graph suggests the prevalence of diabetes differed by age, sex, and ethnicity.
Figure 2. Percentage of females (top) and males (bottom) with Diabetes mellitus. (Fang,2023)
Bar Chart Illustrating the Differences in Physical Indicators Between Diabetic and Non-Diabetic Patients
Among the participants, there were 870 Black, 1,213 White, and 656 Mexican-American individuals with diabetes (DM), while the non-diabetic groups included 3,960 Black, 8,226 White, and 2,863 Mexican-American participants. Diabetics had lower overall mean leg length and total cholesterol (TCHOL) compared to non-diabetics (1.07 cm and 18.67 mg/dL, respectively), but a higher mean BMI (4.27 kg/cm²). The impact of DM on reducing TCHOL was most significant in White participants (23.6 mg/dL), followed by Black participants (9.67 mg/dL), and least in Mexican-Americans (8.25 mg/dL).
Figure 3. Examination of the disparities in three indicators between diabetic and non-diabetic patients across various ethnicities. (A) Leg length; (B) BMI; (C) TCHOL. *** P<0.001 vs diabetes; ** P<0.01.(Fang,2023)
Title:The association of blood urea nitrogen-to-creatinine ratio and in-hospital mortality in acute ischemic stroke patients with atrial fibrillation: data from the MIMIC-IV database
Publication: Front Neurol
Main Methods: MIMIC-IV clinical data mining
Abstract:This study explored the relationship between the blood urea nitrogen-to-creatinine (BUN/Cr) ratio and in-hospital mortality in patients with acute ischemic stroke (AIS) and atrial fibrillation (AF) admitted to the ICU. Using data from the MIMIC-IV database, multivariable logistic regression and restricted cubic spline models were employed. Among 856 patients, 21.26% died in the hospital. The analysis revealed a J-shaped correlation between BUN/Cr at admission and mortality, with a turning point at 19.63 mg/dL. Patients with higher BUN/Cr levels (above 22.41 mg/dL) showed an increased risk of in-hospital mortality, rising 4% for each 1 mg/dL increase in BUN/Cr.
Research Results:
Data Collection Criteria for This Study
This study included 2,074 patients with AIS complicated by AF in the ICU. After applying predetermined inclusion and exclusion criteria, 1,218 individuals were deemed ineligible and were excluded from participation. Of those excluded, 1,082 had prior ICU admissions before the current hospitalization, 111 were under 18 years old or had ICU stays of less than 24 hours, and 25 had no available data on their blood urea nitrogen or creatinine levels.
Figure 4.Flow chart of this study.Gradually exclude data that do not meet the criteria.(Li,2024)
Univariate Analysis Reveals Correlation Between Factors Like BUN/Cr and In-Hospital Mortality
Univariate logistic regression analysis revealed that in-hospital mortality was positively associated with factors such as age, heart rate, temperature, respiratory rate, glucose, sodium, anion gap, WBC count, renal disease, malignant cancer, metastatic solid tumor, APS III, SAPS II, OASIS scores, cerebral edema, and tracheal intubation. In contrast, factors like DBP, hemoglobin, bicarbonate, calcium levels, hospital length of stay (LOS), and the use of statins and anticoagulant drugs showed a negative association with in-hospital mortality.
Table 2. Univariate logistic analysis between BUN/Cr and in-hospital mortality
| Variable | OR (95% CI) | p-value |
|---|---|---|
| BUN/Cr | 1.03 (1.02 ~ 1.05) | <0.001 |
| Age | 1.02 (1 ~ 1.03) | 0.032 |
| Gender (male) | 1.03 (0.74 ~ 1.43) | 0.856 |
| Heart rate | 1.02 (1.01 ~ 1.02) | <0.001 |
| DBP | 0.98 (0.97 ~ 0.99) | 0.022 |
| MBP | 1 (0.99 ~ 1.01) | 0.792 |
| ... | ... | ... |
DBP, diastolic blood pressure; MBP, mean blood pressure (Li,2024)
Modeling Reveals the Effects of the BUN/Cr Ratio and In-Hospital Mortality
A multivariable logistic regression model, using the second tertile of BUN/Cr as the reference group, revealed a significant positive association between elevated BUN/Cr levels (considered as a continuous variable) and an increased risk of in-hospital mortality in patients diagnosed with AIS and coexisting AF.
Table 3. Relationship between BUN/Cr Ratio and In-Hospital Mortality in a Multivariate Regression Model.
| Variable | Model 1 | Model 2 | Model 3 | Model 4 | ||||
|---|---|---|---|---|---|---|---|---|
| OR(95%CI) | P | OR(95%CI) | p | OR(95%CI) | p | OR(95%CI) | p | |
| BUN/Cr per SD | 1.32 (1.13 ~ 1.54) | <0.001 | 1.31 (1.12 ~ 1.53) | 0.001 | 1.33 (1.12 ~ 1.57) | 0.001 | 1.26 (1.04 ~ 1.53) | 0.019 |
| <17.2 | 1.08 (0.7 ~ 1.66) | 0.74 | 1.1 (0.71 ~ 1.7) | 0.681 | 1.13 (0.71 ~ 1.79) | 0.604 | 1.2 (0.71 ~ 2.01) | 0.493 |
| 17.2 ~ 22.41 | 1 | 1 | 1 | 1 | ||||
| >22.41 | 2.02 (1.35 ~ 3.02) | 0.001 | 2.03 (1.35 ~ 3.05) | 0.001 | 2.18 (1.42 ~ 3.34) | <0.001 | 2.02 (1.26 ~ 3.26) | 0.004 |
| P for trend | 0.001 | 0.002 | 0.002 | 0.025 | ||||
P,P-value; BUN, blood urea nitrogen; Cr, creatinine; OR, odds ratio; CI, confidence interval. Model 1: no adjusted. Model 2: adjusted for gender and age. Model 3: adjusted for model 2 plus LOS hospital, DBP, MBP, temperature, Spo2, platelets, WBC, and INR. Model 4: adjusted for model 3 plus peripheral vascular disease, chronic pulmonary disease, malignant cancer, severe liver disease, OASIS, Charlson Comorbidity Index, cerebral edema, tracheal intubation, thrombolysis, statins, anti-platelet agents, and anticoagulant drugs.(Li,2024)
Restricted Cubic Spline Analysis Reveals J-Shaped Association Between BUN/Cr and In-Hospital Mortality
In patients with AIS and coexisting AF, BUN/Cr levels at admission showed a J-shaped association with in-hospital mortality. When BUN/Cr exceeded 19.63 mg/dL, the risk of in-hospital mortality increased.
Figure 5. non-linear relationship between BUN/Cr and patients with AIS combined with AF. (Li,2024)
Subgroup Analysis Assessing the Association Between BUN/Cr and In-Hospital MortalityIn
In subgroup analyses based on age (<75 years and ≥75 years), gender, peripheral vascular disease, and other confounding factors, no significant interaction was observed (all p-values for interaction >0.05).
Figure 6.Relationship between BUN/Cr and in-hospital mortality in subgroup analysis.Only a subset of the subgroups is shown in this figure.(Li,2024)
Conclusion
In their study, the researchers observed a J-shaped association between BUN/Cr levels within 24 hours of ICU admission and in-hospital mortality in patients with AIS combined with AF. The risk of in-hospital mortality was found to increase when the BUN/Cr ratio exceeded 19.63 mg/dL. As a result, the study highlights the importance of closely monitoring patients with elevated BUN/Cr levels, as they may face a higher risk of in-hospital mortality. These findings aim to assist clinicians in making more informed decisions and improving patient care.
1.What is Clinical and Medical Data Mining?
Clinical and Medical Data Mining is the process of analyzing large datasets from healthcare sources such as electronic health records (EHR), clinical trials, and public health databases. The goal is to identify patterns, trends, and correlations that can help in improving diagnosis, treatment plans, and healthcare outcomes. It is widely used for personalized medicine, disease prediction, drug discovery, and optimizing clinical trials.
2.How does Clinical and Medical Data Mining benefit research?
Data mining allows researchers to uncover hidden patterns in complex datasets, enabling more accurate predictions and insights. These insights can optimize resource allocation, identify biomarkers, enhance patient stratification, improve disease surveillance, and accelerate medical breakthroughs. Data mining also supports predictive modeling, allowing researchers to forecast outcomes and trends more effectively.
3.What types of data can be used in Clinical and Medical Data Mining?
A variety of data types can be used, including structured data (e.g., lab results, patient demographics) and unstructured data (e.g., clinical notes, imaging data). Data can be sourced from EHR systems, clinical trials, disease registries, genomics datasets, public health databases like SEER and MIMIC, and other research-focused databases like NHANES or TCGA.
4.What challenges exist in Clinical and Medical Data Mining?
Challenges include data quality issues, such as missing or incomplete data, data privacy concerns, and the complexity of integrating datasets from different sources. Additionally, public databases may require permissions or applications for access, and ethical concerns regarding patient data confidentiality must be carefully managed. Advanced algorithms and skilled expertise are required to clean, process, and analyze the data properly.
5.What public databases do you support for data mining?
We support a wide range of public databases, including SEER (Surveillance, Epidemiology, and End Results), TCGA (The Cancer Genome Atlas), MIMIC (Medical Information Mart for Intensive Care), NHANES (National Health and Nutrition Examination Survey), GBD (Global Burden of Disease), and various Mendelian randomization databases. Our team assists with data acquisition and preparation from these and other sources for research purposes.
References
- Fang, L.,et al. Prevalence of diabetes in the USA from the perspective of demographic characteristics, physical indicators and living habits based on NHANES 2009-2018. Frontiers in endocrinology.2023, 14, 1088882.
- Li, B.,et al. The association of blood urea nitrogen-to-creatinine ratio and in-hospital mortality in acute ischemic stroke patients with atrial fibrillation: data from the MIMIC-IV database. Frontiers in neurology.2024, 15, 1331626.
