Machine Learning Risk Adjustment of the C-section Rate: Impact by Provider Cynth

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Machine Learning Risk Adjustment of the C-section Rate: Impact by Provider Cynth

Avila, Sergio, General Assignment Reporter has reference to this Academic Journal, PHwiki organized this Journal Machine Learning Risk Adjustment of the C-section Rate: Impact by Provider Cynthia J. Sims MD, Obstetrics, Gynecology & Reproductive Sciences, Magee Womens Hospital, Pittsburgh, PA 15213 Rich Caruana, Peng Jia, Radu Stefan Niculescu, Matt Troup, Carnegie Mellon University, Pittsburgh, PA 15213 R. Bharat Rao, Data Mining Group, Siemens Corporate Research, Inc. Princeton, NJ 08540 Objective: We observed a significant variation in C-section rates as long as 17 physician groups, 13% to 23%. The objective of this study was to determine how much of the observed variation was due to differences in the patient sub-population in addition to how much was due to differences inherent to the group practices. Method: We studied a population of 22,176 patients (1995-1997) stratified by provider group. We trained a machine-learning decision-tree model on all 22,176 patients. The model had an accuracy of 90%, in addition to an ROC area of 0.92. Care was taken to prevent over-fitting. The decision-tree model was applied to the patients in each group to determine the aggregate risk as long as C-section as long as the sub-population predicted by average physician practice as represented by the 17 physician groups. Results: 1. Little of the observed variation in C-section rate was attributable to variation in the patient sub-populations (the correlation between the observed C-section rates in addition to the rates predicted by the machine learning model was only 0.21). 2. After adjusting as long as patient sub-population risk, we found that several groups had differences between actual in addition to predicted rates that were highly significant. 3. Raw C-section rates are misleading. Some groups with a high rate had a high risk patient population that justified the high rate. Other groups with a high rate did not have high risk patient populations. Conclusions: There was significant variation in the C-section rate of the different sub-populations. (See table to right.) Only a fraction of the observed variation was explained by differences in predicted risk as long as C-section of the population. When determining which groups have high c-section rates, it is important to adjust as long as the relative risk of the different sub-populations. The raw, unadjusted cesarean section rate of different sub-populations can be misleading. We conclude that the substantial differences among the groups were not predicted by patient risk.

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RESUBSTITUTION ROC AREA MACHINE LEARNING DECISION TREE MODEL TRAINED ON 22,176 CASES Observed in addition to Predicted C-Section Rates as long as 17 Physician Groups Sorted by Observed C-Section Rates. Physician Groups 7, 8, in addition to 10 are particularly Interesting. Last Column is Estimated C-Section Rate that Would Result if the Physician Group Treated all 22,176 Patients. G M A E K J H O D F Scatter Plot Comparing the Observed C-Section Rate in the 17 Physician Groups With the C-Section Rates Predicted as long as Those Groups by the Decision Tree

Hypothesis: The observed variation in C-section rates as long as physician groups is inherent to the group practice in addition to not due to differences in the patient sub-population. The Population: 22,176 patients (1995-1997). Stratified by provider groups. 17 provider groups. Conclusions: The substantial differences among groups were not predicted by patient risk. Significant variation in the C-section rate of the different provider group sub-populations. Future Work: Evaluate methods as long as machine learning group comparison. Compare decision tree model with a Neural Network model. Best evidence that c-section rate can be lowered without adversely affecting the results comes from countries with lower c-section rates but comparable outcomes. We intend to apply the same techniques to a medical database of one of these countries.

Avila, Sergio KGUN-TV General Assignment Reporter www.phwiki.com

Avila, Sergio General Assignment Reporter

Avila, Sergio is from United States and they belong to KGUN-TV and they are from  Tucson, United States got related to this Particular Journal. and Avila, Sergio deal with the subjects like General Assignment News; Local News

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