Disease Definition
Setting clear disease definitions helps us determine what data to collect and use. Using standard systems like ICD (International Classification of Diseases) or SNOMED CT ensures consistency. Doctors and data experts should work together to refine these definitions so they match real-world cases.
Data Quality and Missing Data
If data is missing or incorrect, it becomes unusable. To fix this, we need to check data accuracy when collecting it. If data is missing, we can use methods like multiple imputation or regression imputation to fill in gaps with estimated values.
Unstructured Data
Audio recordings and videos may contain useful medical details, such as a patient’s heartbeat sound or movement patterns in a video. However, these types of data are difficult to analyze directly. To make them useful, we can convert speech to text/number or use machine learning to recognize patterns. This helps turn unstructured data into structured data that can be used in research and treatment.
Data Analysis and Training
Learning how to handle big data is essential. Machine learning can help identify patterns in large datasets, while tools like Apache Spark and Hadoop process big data efficiently. Training programs and workshops can help healthcare professionals improve their data skills.