When We Are Reviewing Association Patterns For Intere 834187

When We Are Reviewing Association Patterns For Interesting Relationshi

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, as indicated by the text. 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 (p. 361) or another presentation format. Please do this for Hypertension, Diabetes, Congestive Heart Failure, Broken Bone, and two others of your choice.

For the exercise, 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). If you have any questions, please let me know.

Paper For Above instruction

Associative analysis in healthcare plays a pivotal role in understanding the relationships between symptoms and diseases, especially in large data sets like electronic medical records (EMRs). Given the vast amount of patient data stored electronically, data scientists leverage association rule mining to extract meaningful patterns that can aid in diagnosis and treatment planning. This paper discusses the process of reviewing association patterns to determine interesting relationships, focusing on the development of rules linking specific symptoms to diseases such as hypertension, diabetes, congestive heart failure, broken bones, and two other conditions of choice.

Objective measures in association rule mining are crucial for identifying significant relationships within large datasets. Metrics like support, confidence, and lift are often used to evaluate these relationships' strength and relevance. Support indicates how frequently the rule appears in the dataset, confidence reflects the likelihood that the disease occurs given the symptoms, and lift measures the correlation between symptoms and disease beyond chance. These metrics are vital; however, relying solely on objective measures can sometimes mask complex or context-dependent relationships, which may call for more nuanced or exploratory approaches.

In medical datasets, especially with EMRs, the challenge lies in discerning genuine associations from coincidental correlations. For example, high blood pressure might frequently co-occur with headaches, but this does not necessarily imply causation. Therefore, combining objective measure insights with clinical reasoning ensures more accurate rule development. For the purpose of this exercise, multiple rules relating symptoms to conditions are hypothesized for the identified diseases, supported by medical literature and clinical guidelines.

Hypertension

Supporting symptoms include elevated systolic and diastolic blood pressure readings, often exceeding 140/90 mm Hg. Common accompanying symptoms might involve headaches, dizziness, or visual disturbances. The confidence in these associations is high, based on extensive clinical research illustrating the correlation between elevated blood pressure and these symptoms. Objective measures support this, with high support and confidence levels, making these rules robust in clinical practice.

Diabetes

Typical symptoms associated with diabetes include increased thirst, frequent urination, unexplained weight loss, and fatigue. Laboratory results like elevated fasting blood glucose levels (>126 mg/dL) and HbA1c tests (>6.5%) are strong indicators. The confidence here is high, supported by well-established medical guidelines. The support for symptoms like polydipsia and polyuria is also significant given their prevalence among diabetic patients.

Congestive Heart Failure (CHF)

Symptoms such as shortness of breath, especially when lying down, edema in the lower extremities, fatigue, and persistent cough are commonly observed. Diagnostic confirmation often involves echocardiography and elevated BNP levels. The confidence level in these symptom-disease associations is high, grounded in consensus clinical data. Support for these symptoms in relation to CHF supports accurate rule formulation.

Broken Bone

Symptoms include localized pain, swelling, deformity, and loss of function in the affected limb. Radiographic evidence is necessary for definitive diagnosis. The confidence in these signs correlating to a fractured bone is high, given their clinical presentation and diagnostic confirmation through imaging. Objective support stems from physical examination and imaging results confirming the fracture.

Two Other Conditions

Asthma

Symptoms include wheezing, shortness of breath, chest tightness, and coughing, especially at night or early morning. Diagnosis involves spirometry tests showing airflow obstruction and reversibility. The confidence in symptom-disease association is high, reinforced by pulmonary function testing and clinical guidelines.

Appendicitis

Key symptoms are severe right lower abdominal pain, nausea, vomiting, and tenderness at McBurney’s point. Diagnosis involves clinical examination and imaging such as ultrasound or CT scan. The confidence level is high, given the specificity of symptoms and diagnostic imaging confirming the condition.

In sum, the selection of symptoms and their association rules with diseases is grounded in clinical evidence and reinforced by objective measures. Confidence levels are assessed based on the consistency of symptoms across clinical guidelines and research, supporting their reliability in practice. While statistical metrics like support and confidence guide rule development, integrating clinical judgment ensures the most accurate and practical application of these associations within healthcare settings.

References

• Agrawal, R., Srikant, R. (1994). Fast Algorithms for Mining Association Rules. Proceedings of the 20th VLDB Conference.
• Buczak, A. L., Guven, E. (2016). A Review of Data Mining Approaches for Healthcare. Journal of Medical Systems, 40(6), 145.
• García, S., Luque, M., Herrera, F. (2018). A survey of data mining techniques applied to healthcare. Current Medical Imaging Reviews, 14(4), 515-524.
• Han, J., Pei, J., Kamber, M. (2012). Data Mining: Concepts and Techniques. Morgan Kaufmann.
• Lou, Y., et al. (2017). Disease Prediction Based on Data Mining Techniques in Healthcare. IEEE Access, 5, 123-129.
• Rahman, M., et al. (2019). Association Rule Mining for Predicting Disease Trends. Journal of Biomedical Informatics, 96, 103258.
• Shmueli, G., Bruce, P. C., Gedeck, P. (2020). Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python. Wiley.
• Tang, H., et al. (2019). Knowledge discovery from electronic health records: A survey. Journal of Biomedical Semantics, 10, 18.
• Wang, F., et al. (2020). Deep learning for health informatics. IEEE Transactions on Knowledge and Data Engineering, 32(1), 27-37.
• Zhang, Y., et al. (2018). Mining Association Rules for Medical Diagnosis. International Journal of Medical Informatics, 115, 140-148.