Smarter Big Data for a Healthy Pennsylvania: Changing the Paradigm of Healthcare

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Marilyn Jenkins
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1 Smarter Big Data for a Healthy Pennsylvania: Changing the Paradigm of Healthcare By: Alejandro Borgonovo Mentor: Dr. Amol Navathe

2 Outline Project Overview Project Significance Objectives Methods About the Data Machine Learning Techniques Results Limitations

3 Project Overview There are three different aims in this project all with the goal of improving the health of Pennsylvanians In hospital At home In the community

4 Aim 1: In Hospital To develop prediction models for the dynamic and timely prediction of in-hospital post-surgical complications There are surgical risk calculators out there already that predict risk of patients with certain preoperative characteristics The difference is that this prediction model will add prospective examination of vital sign, radiology, medication and lab trends to the static models

5 Project Significance The current healthcare system is focused on treating clinical events after they occur Rise in amount of data collected Most risk prediction models for hospital readmission perform poorly and do not provide timely and actionable information Opportunity to shift the healthcare system away from being reactive to predicting the onset of adverse clinical events

6 ACS NSQIP Surgical Risk Calculator American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) Developed a universal risk calculator based on clinical data it collects from U.S. hospitals Predicts probability that patient will develop a complication within 30 days of the operation Intended to aid communication between surgeons and patients about specific risks of the intended surgery

7 ACS NSQIP Surgical Risk Calculator

8 Objectives Replicate the NSQIP surgical risk calculator using Penn EHR and claims data Create a model that will accurately predict risk of patient developing a complication Find importance of variables with most predictive power Set the stage for next steps--incorporating real time data from EMR for dynamic risk prediction

9 Methods: Conceptual Approach Hypothesis: Available UPHS EMR data can be modeled on NSQIP calculator to predict postoperative complications with low misclassification rate Data coding and analysis performed using Python libraries Pandas (database manipulation) and scikitlearn (machine learning)

10 About the Data Data was extracted from Penn Data Store (PDS) Data is stored in many different systems and is plagued by differences in data type, measurement, labeling, etc Pulls data from multiple systems and consolidates it into a single queryable model Key categories include patient demographics, medical history, coded procedures, medication administration, microbiology, pathology, surgery, lab results, vital signs, etc.

11 About the Data continued Setting: Hospital of the University of Pennsylvania (HUP), Pennsylvania Hospital (PAH), and Penn Presbyterian Medical Center (PMC) Sample: 7860 inpatients who either had colorectal or gastrointestinal (GI) surgery identified through ICD-9 or CPT codes. Must have been older than 18 years old at time of surgery Data: ranges from 2008 to 2016, although ASA class is only availabe from 2013 onwards (1877)

12 Data Cleaning & Creating Input Variables Converted data into binary input variables Each variable had a yes or no answer For Age, BMI, and ASA class we created categories to accommodate yes or no answers Used cross-validation to determine accuracy of methods

13 Key Terms Cross Validation: the data set is split before hand into a training set and test set. Model is then estimated using the training set and its accuracy is measured by how well it predicts the output values of the test set Supervised: The learner receives a set of labeled examples ( Correct answers) as training data and creates a function to map new examples. Used for classification, regression, and ranking problems Unsupervised: The data that the learner receives is not labeled, instead it gathers/clusters the data Used for clustering problems

14 K Nearest Neighbors (KNN) A non-parametric method used for classification and regression Identifies K amount of points in the data set that are closest to a test observation KNN then classifies the test observation according to the classification of its nearest neighbors Regression used when output is quantitative

16 Logistic Regression Models the probability that a response belongs to a particular category Typically used for classification that has a binary response although it is possible to have greater than two responses Can calculate probability with multiple predictors NSQIP used this method with feature selection

17 Results Outcome variable was whether patient had any complication Variables Used Method Used Number of patients Accuracy Score All variables Logistic Regression (Emergency, Diabetes, ASA 5) Logistic Regression with feature selection (same as above, with ASA 1 and 2) Logistic Regression with feature selection All variables (without ASA) Logistic Regression (Emergency, Diabetes, Heart Failure) Logistic Regression with feature selection (Emergency, Heart Failure, Diabetes, types of procedures Logistic Regression with feature selection ( same as above, with ascites, COPD) Logistic Regression with feature selection

18 Limitations Time constraint to develop a model Recreating certain variables specific to NSQIP such as Dyspnea and Functional Status Determining whether variable was a complication of a surgery or whether it developed before surgery Kidney failure, Sepsis, Ventilator dependence Measuring how accurate model is in predicting relative risk of individuals

19 Conclusions Feature selection may improve accuracy scoreà reduces overfitting, especially with small amount of data that was available Big opportunity for improvement Identifying variables with the help of labs Larger data set will be available Addition of vital signs, medical and lab trends may greatly increase prediction accuracy

20 References James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning: With Applications in R. N.p.: n.p., n.d. Web. Kansagara, Devan, Honora Englander, Amanda Salanitro, David Kagen, Cecelia Theobald, Michele Freeman, and Sunil Kripalani. "Risk Prediction Models for Hospital Readmission." Jama (2011): Web. Mohri, Mehryar, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of Machine Learning. Cambridge, MA: MIT, Web. Raschka, Sebastian, and Randal S. Olson. Python Machine Learning: Unlock Deeper Insights into Machine Learning with This Vital Guide to Cutting-edge Predictive Analytics. Birmingham: Packt, Print.

21 Questions?

The PREDICT Trial. Prediction using a Randomized Evaluation of Data collection Integrated through Connected Technologies

The PREDICT Trial. Prediction using a Randomized Evaluation of Data collection Integrated through Connected Technologies The PREDICT Trial Prediction using a Randomized Evaluation of Data collection Integrated through Connected Technologies Ogochukwu Ufio Advisors: Mitesh Patel MD, MBA, MS Caroline Carney, BA Table of Contents