HLST 2040 Chapter Notes - Chapter 3: Natural-Language Processing, Predictive Analytics, Data Mining

• School: York University
• Department: Health Studies
• Course: HLST 2040
• Professor: Serban Dinca- Panaitescu

Document Summary

Ibm defines analytics as the systematic use of data and related business insights developed through applied analytical disciplines to drive fact-based decision making for planning, management, measurement and learning. Predictive simulation and modeling techniques that identify trends and portend outcomes of actions taken. Prescriptive optimizing clinical, financial, and other outcomes. Much work is focusing now on predictive analytics, especially in clinical settings attempting to optimize health and financial outcomes. There are a number of terms related to data analytics. The analytics pipeline: the pipeline begins with input data sources, which in healthcare and biomedicine may include clinical records, financial records, genomics and related data, and other types, even those from outside the healthcare setting (e. g. , census data). This is followed by statistical processing, where machine learning and related statistical inference techniques are used to make conclusions from the data. The final step is the output of predictions, often with probabilistic measures of confidence in the results.