Jude Savarraj, PhD

“One of the biggest challenges that is also an opportunity is to have large data repositories with common data elements, and then have an open-source data distribution model that would allow for computer scientists to use data to solve some really important problems.”

 on prognostic machine learning (ML) models suggest that these models may be used to predict delayed cerebral ischemia (DCI) as well as functional outcomes in subarachnoid hemorrhage (SAH) care. In the study, the ML models significantly outperformed standard models (SMs). 

Conducted by Jude Savarraj, PhD, research scientist, department of neurosurgery, University of Texas Medical School at Houston, and colleagues, the results show that the ML model outperformed the SM in area under the curve (AUC) by 0.20 (95% CI; -0.02 to 0.4) for DCI, 0.07 (standard deviation [SD], 0.03; 95% CI, 0.0018–0.14) for discharge outcomes. Additionally, the ML model bested the SM model by 0.14 (95% CI, 0.03–0.24) for 3-month outcomes, and matched physician performance in predicting 3-month outcomes.

Initially only using routine variables included in a patients’ electronic medical record (EMR), Savarraj and colleagues noticed that the ML models performed even better when also including clinician-determined Hunt-Hess Scale score in addition to standard EMR variables (AUC, 0.85; SD, 0.05; 95% CI, 0.75–0.92). Only using EMR data yielded an AUC of 0.81 (SD, 0.05; 95% CI, 0.71–0.89; P <.05).

 spoke with Savarraj to learn more about the use of machine learning in healthcare and the opportunities for different healthcare systems to pool data for machine learning models.

Savarraj JP, Hergenroeder GW, Zhu L, Chang T, et al. Machine learning to predict delayed cerebral ischemia and outcomes in subarachnoid hemorrhage. Neurology. Published online November 12, 2020. doi: 10.1212/WNL.0000000000011211

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The research scientist from the University of Texas Medical School discussed some of the challenges and opportunities that machine learning presents in patient care. 

Jude Savarraj, PhD: Applying Machine Learning to Healthcare

“If your brain is in a state of increased pressure for a prolonged time that worsens the outcome. So, if you have a computer algorithm that can predict when this patient is going to develop an increased intracranial pressure crisis, then such a model would be useful for prophylactic treatment as well.”

Jude Savarraj, PhD, research scientist, department of neurosurgery, University of Texas Medical School at Houston, and colleagues 

 that suggests machine learning (ML) models used to predict delayed cerebral ischemia (DCI) and functional outcomes significantly outperformed standard models (SMs) when used in subarachnoid hemorrhage (SAH) care.

The ML model outperformed the SM in area under the curve (AUC) by 0.20 (95% CI; -0.02 to 0.4) for DCI, 0.07 (standard deviation [SD], 0.03; 95% CI, 0.0018–0.14) for discharge outcomes, and by 0.14 (95% CI, 0.03–0.24) for 3-month outcomes. Additionally, ML models matched physician performance in predicting 3-month outcomes.

The ML models initially only used routine variables included in a patients’ electronic medical record (EMR), but Savarraj and colleagues found that the ML models performed even better when they also assessed clinician-determined Hunt-Hess Scale score in addition to standard EMR variables (AUC, 0.85; SD, 0.05; 95% CI, 0.75–0.92). Only using EMR data yielded an AUC of 0.81 (SD, 0.05; 95% CI, 0.71–0.89; P <.05).

 spoke with Savarraj to learn more about the use of machine learning and other opportunities the technology could be applied to in order to improve the care of patients.

The research scientist from the University of Texas Medical School discussed how he would like to use machine learning to aid in prediction and prophylactic treatment. 

Jude Savarraj, PhD: Applying Machine Learning to Predict Outcomes

“When we combined [human intuition and machine learning], we were able to get better scores. That suggests that, in fact, humans and computer models have to work together instead of trying to replace one with another.”

 that suggests machine learning (ML) models used to predict delayed cerebral ischemia (DCI) and functional outcomes significantly outperformed standard models (SMs) when used in subarachnoid hemorrhage (SAH) care. 

 spoke with Savarraj to learn more about the use of machine learning and synergistic opportunities to combine human and computer skill.

The research scientist from the University of Texas Medical School discussed how machine learning was most effective in predicting delayed cerebral ischemia when it took human-derived variables into account. 

Jude Savarraj, PhD: Combination of Human Intuition and Machine Learning to Predict DCI

“There's been efforts to predict DCI sooner, so there could be some prophylactic intervention. Machine learning is really good at predicting stuff before it happens. So that's when the idea struck me. Maybe we should be trying to use machine learning algorithms to see if we can predict this complication before symptoms arise.”

Jude Savarraj, PhD, research scientist, department of neurosurgery, University of Texas Medical School at Houston, and colleagues recently published a study that suggests machine learning (ML) models used to predict delayed cerebral ischemia (DCI) and functional outcomes significantly outperformed standard models (SMs) when used in subarachnoid hemorrhage (SAH) care. 

The ML models initially only used routine variables included in a patients’ electronic medical record (EMR), but Savarraj and colleagues found that the ML models performed even better when they also assessed clinician-determined Hunt-Hess Scale score in addition to standard EMR variables (AUC, 0.85; SD, 0.05; 95% CI, 0.75–0.92). Only using EMR data yielded an AUC of 0.81 (SD, 0.05; 95% CI, 0.71–0.89; P < .05).

Savarraj JP, Hergenroeder GW, Zhu L, Chang T, et al. Machine learning to predict delayed cerebral ischemia and outcomes in subarachnoid hemorrhage. 

. Published online November 12, 2020. doi: 10.1212/WNL.0000000000011211

The research scientist from the University of Texas Medical School at Houston discussed the potential of machine learning to predict outcomes before symptoms arise. 

Jude Savarraj, PhD

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