Health Data Mining Using Machine Learning for Unclean Data

The patent employs inexpensive, easily deployable data collection methods such as mobile health applications, wearable sensors, and electronic health records that can rapidly gather large volumes of data without the lengthy recruitment and protocol adherence requirements of traditional clinical trials. This enables fast data accumulation while maintaining sufficient quality for statistical analysis.

The patent transforms the parameters of data collection by shifting from small sample sizes with strict inclusion criteria to large sample sizes with more flexible data capture methods. This parameter change allows real-world data to be collected at scale, improving productivity while the large sample size compensates for individual data point variability through statistical power.

The patent segments the complex task of analyzing large datasets into manageable components through standardized data collection protocols, modular analysis pipelines, and distributed computing architectures. This segmentation reduces processing complexity by breaking down the overall analytical challenge into smaller, independently manageable tasks that can be executed efficiently.

The patent introduces intermediary layers including data standardization frameworks, preprocessing algorithms, and intermediate storage systems that mediate between raw data collection and final analysis. These intermediaries simplify the processing complexity by preparing and organizing data before it reaches the analytical engine, making large-scale data handling more manageable.

The patent converts the previously harmful characteristic of data 'dirtiness' into a benefit by embracing real-world variability and heterogeneity as valuable features rather than noise to be eliminated. This approach allows analysis of authentic population diversity, and through appropriate statistical methods, extracts meaningful patterns that reflect actual clinical practice and patient populations.

The patent changes the acceptance parameters for data quality by lowering thresholds for inclusion criteria and embracing greater variability in data sources, collection methods, and population characteristics. This parameter change enables rapid data collection from diverse sources while statistical techniques account for the increased heterogeneity.