When we are reviewing association patterns for interesting relationships, objective measures are commonly used. These are required as the relationships may be hidden by the large data set. These measures, taken in whole, may instead give us inconsistent data on the interesting nature of the relationship. With large data sets, the data scientist may depend too much on objective measures, and not explore alternatives, which may provide a better analysis.

QUESTION

When we are reviewing association patterns for interesting relationships, objective measures are commonly used. These are required as the relationships may be hidden by the large data set. These measures, taken in whole, may instead give us inconsistent data on the interesting nature of the relationship. With large data sets, the data scientist may depend too much on objective measures, and not explore alternatives, which may provide a better analysis.

This has been used at length in the medical field. At any hospital, there is a massive data set to work with. This is in the form of the patient’s medical records. Presently, most hospitals or healthcare facilities use EMR (electronic medical records). This would make the project much more timely, as the researchers would not have to go through all of the boxes of patient files, but could have a program do this portion of the work for them.

At times, the doctor may not be sure of the disease based on the symptoms the patient is presenting. We want to theoretically review the data set and arrive at rules for symptoms and disease. You want to find the best rules to match the symptoms with the disease, or Symptom(s) → Disease. Feel free to use the format in the text (page. 361) or other presentation format. Please do this for Hypertension, Diabetes, Congestive Heart Failure, Broken Bone, and two others of your choice.

For the essay, do not try and find electronic records to work on. You may do research online for the exercise. Please explain why you chose the particular symptoms and the confidence level (low, medium, or high).

Textbook(related chapter) is attached.
Please follow APA format with minimum of 2-3 pages (500 words)

ANSWER

Exploring Association Patterns between Symptoms and Diseases in Medical Data Analysis

Introduction

In the field of data analysis, reviewing association patterns can unveil interesting relationships that might otherwise remain hidden within vast datasets. Objective measures are commonly employed to extract meaningful insights from these datasets, especially in the medical field, where patient records provide a wealth of information. Electronic Medical Records (EMRs) have revolutionized the way healthcare facilities handle patient data, making it more accessible and facilitating comprehensive analyses. In this essay, we will theoretically review a dataset to establish rules correlating symptoms with diseases. Specifically, we will explore the relationships between symptoms and Hypertension, Diabetes, Congestive Heart Failure, Broken Bone, and two additional diseases. By identifying the best rules for symptom-disease matching, we aim to shed light on potential diagnosis frameworks.

Selecting Symptoms and Confidence Levels

When considering symptoms and their correlation with diseases, it is crucial to select symptoms that are commonly associated with the diseases under investigation. To ensure a comprehensive analysis, we should choose symptoms that exhibit a high likelihood of being indicative of a particular disease (Balogh, 2015). The confidence level assigned to each symptom-disease association reflects the strength of the relationship based on existing medical knowledge, research, and clinical experience.

Hypertension: a. High Blood Pressure (Confidence: High) b. Frequent Headaches (Confidence: Medium) c. Fatigue (Confidence: Medium) d. Dizziness (Confidence: Low)

Diabetes: a. Frequent Urination (Confidence: High) b. Excessive Thirst (Confidence: Medium) c. Unexplained Weight Loss (Confidence: Medium) d. Blurred Vision (Confidence: Low)

Congestive Heart Failure: a. Shortness of Breath (Confidence: High) b. Swelling in Ankles/Legs (Confidence: Medium) c. Rapid Weight Gain (Confidence: Medium) d. Persistent Cough (Confidence: Low)

Broken Bone: a. Severe Pain at Injury Site (Confidence: High) b. Visible Deformity (Confidence: Medium) c. Swelling/Discoloration (Confidence: Medium) d. Numbness/Tingling (Confidence: Low)

Pneumonia: a. Persistent Cough (Confidence: High) b. Fever (Confidence: Medium) c. Chest Pain (Confidence: Medium) d. Shortness of Breath (Confidence: Low)

Migraine: a. Intense Headache (Confidence: High) b. Sensitivity to Light or Sound (Confidence: Medium) c. Nausea/Vomiting (Confidence: Medium) d. Visual Disturbances (Confidence: Low)

Explanation and Analysis

The chosen symptoms for each disease are based on common associations reported in medical literature, clinical practice, and empirical evidence. High-confidence symptoms have strong links with the respective disease, while medium-confidence symptoms indicate a moderate correlation (Maxim et al., 2014). Low-confidence symptoms suggest a possible association but are less definitive in diagnosis.

For example, Hypertension is strongly associated with high blood pressure, which serves as a high-confidence symptom. Frequent headaches and fatigue exhibit a medium level of confidence as they are commonly reported by patients with hypertension, although they may also be present in individuals without the disease. Dizziness, with low confidence, might occasionally accompany hypertension but can also be caused by various other factors.

By assigning confidence levels to symptoms, we can prioritize the higher-confidence associations when establishing rules for symptom-disease relationships. Data scientists can use these rules to create decision-support systems, aiding doctors in diagnosing patients based on their reported symptoms (Jiang et al., 2017). However, it is important to note that these associations are theoretical and should be validated using empirical research and clinical trials to ensure accuracy and reliability.

Conclusion

Analyzing association patterns between symptoms and diseases within large medical datasets is a powerful tool for diagnosis support and medical research. While objective measures are essential for extracting meaningful insights, it is crucial for data scientists and healthcare professionals to explore alternative approaches and consider additional factors beyond statistical analysis. Theoretical reviews, like the one presented here, serve as a starting point for further investigation and the development of reliable diagnosis frameworks. By utilizing electronic medical records and advanced data analysis techniques, we can enhance patient care, improve diagnostic accuracy, and pave the way for more efficient and effective healthcare systems.

References

Balogh, E. P. (2015, December 29). The Diagnostic Process. Improving Diagnosis in Health Care – NCBI Bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK338593/ 

Jiang, Y., Qiu, B., Xu, C., & Li, C. (2017). The Research of Clinical Decision Support System Based on Three-Layer Knowledge Base Model. Journal of Healthcare Engineering, 2017, 1–8. https://doi.org/10.1155/2017/6535286 

Maxim, L. D., Niebo, R., & Utell, M. J. (2014). Screening tests: a review with examples. Inhalation Toxicology, 26(13), 811–828. https://doi.org/10.3109/08958378.2014.955932