Machine Learning and Statistical Techniques to Predict Sepsis: Unifying
Previous Work
Nathaniel D. Bastian, Paul M. Grffin, Shravan
Kethireddy, Joseph J. Klobusicky, Alexander Murph,
Alexander Robinson, Nathan C. Ryan

Introduction
Sepsis is a syndrome of dysregulated
inflammation caused by infection. In 2010 it wasthat 11th leading cause of death in the UnitedStates [1] and, in 2011, it was the single mostexpensive diagnosis treated in hospitals in theUnited States [2]. Septic shock is a subset ofsepsis in which the complications induced by thepresence of sepsis lead to an increase in themortality of the patient.

Methods
Finding a Septic Shock Diagnosis
Following a method developed by Ho et. al. [3]for determining Septic Shock, we classified
periods of hypotension with a given patient as
any period of time where hypostolic bloodpressure was below 90. Using the center time ofeach of these periods of hypotension, we
summed up all the fluids given to the patient
between this time and an hour before. If theamount of fluids in this timespan exceeded
600ml, the patient was considered to have
hypotension despite fluid intake. As such, if thepatient was also determined to have Sepsis, welabeled the patient as positive for Septic Shock.
The start time of that patient’s period ofhypotension was used as the onset time.

Data

Results (Continued)

Two datasets were used for comparative
analysis. First, we used the publically available
Multiparameter Intelligent Monitoring in Intensive
Care II (MIMIC-II) database, which was gatheredfrom Boston’s Beth Israel Deaconess Medical
Center [4]. Secondly, we analyzed medical datacollected at the Geisinger Health System (GHS).

Support Vector Machine

Dataset:Data amount# of ICU
Length of Stay

MIMIC-II>30,00027,542
3 – 18 days

GMC>18,000
17,864
1 – 7 days

Resp. Average
Samples/ Frequency

257.47 / 20 - 58 min

44.64 / 3 – 4 min

GMC AUC: .775, MIMIC AUC: .6656

HR Average Samples/Frequency

264.28 /27 - 67 min

42.26 /2.5 – 3.5 min

Artificial Neural Network

BP Average Samples/ 121.81 /61- 125 min
Frequency

45.68 /2.6 – 3 min

Temp Average
Samples/ Frequency

62.69 /120-280 min

25.29 /5.8 – 6 min

SpO2 Average
Samples/ Frequency

259.6 /28-68 min

38.73 /3.3 – 3.6 min

WBC Average
Samples/ Frequency

13.52/490-134 3min 4.37 /33.4 – 37.5 min

Results
Each model yielded comparable results for thetwo data sets. To measure the performance ofour models, we created for each a Receiver
Operating Characteristic (ROC) curve.

Logistic Regression
To find a patient’s time of Septic Shock onset, tochoose data points closest to this time, and tomerge data from multiple sources into a single
dataset, we used an object-oriented approach inPython. After the proper data was collected andwritten to a csv file, the cleaning, processing, andmodeling-making was done in R.

Predicting Septic ShockTo do our predictive analysis, we used fourexisting mathematical models: a LogisticRegression model, a Decision Tree model, aSupport Vector Machine, and an Artificial Neural
Network. For each model, we used the following
features for prediction: Heart Rate, Respiratory
Rate, SOFA and SAPS scores, White Blood
Cell count, Temperature, and Blood Pressure.

GMC AUC: .875, MIMIC AUC: .7451

Decision Tree (RPART)

Python “Hospital” Object UML

GMC AUC: .75, MIMIC AUC: .8408

GMC AUC: .8325, MIMIC AUC: .7385

Conclusion
We replicated models in the literature on thesame publicly available data set on which theywere developed. After getting these baseline
results, which were similar to those in theliterature, we applied the same models (withthe same features and definitions) to privately
held data. For a fixed model, its performanceon the publicly available data and the privately
held data were comparable. The quality ofperformance was especially good consideringthat the models were developed on
demographic features of the individuals andonly seven additional features. In some cases,
our models were trained on larger data sets
and also outperformed models in the literature.
The greatest challenge in the development ofthese models is the determination of which
individuals have the right kind and right amountof data to be included in the models. In
particular, identifying which patients had sepsis
and when they had it (and septic shock ifapplicable) was essential for our experiments.
The fact that in some cases we improved on
existing results we think can be attributed to thechoice that we made to identify patients as
having sepsis in a way that is not based on theICD-9 the patients have had.
For our next step, we will be examining how wemight examine the paths of how patients movethrough the hospital, and use predictive
techniques like Markov Chains on the GMC
data.

References[1] L Hoyert, et al. Nat. vil. 21 (2012)

Acknowledgements
We would like to thank the Bucknell-Geisinger
Research Initiative for making this project
possible, and Bucknell’s Provost Office forproviding funding for this trip.

[2] C Torio, et al. Nat. inp. 160 (2013)[3] J Ho, et al. Sep. sho. 1 (2014)[4] M Saeed, et al. Mult. intel. 5 (2011)