Identifying and Addressing the Challenges in the Diagnosis of Sepsis

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Sepsis is the leading cause of death in the Intensive Care Unit, and it’s responsible for 1 in 3 hospital deaths. Each hour without treatment increases a patient’s risk of death by 4-8%. Thus, early detection of sepsis is crucial for improving survival.

With the inclusion of advanced data preprocessing and machine learning, our research has been able to better predict which patients will get sepsis during their hospital stay.

In our study, we sought to develop a robust sepsis prediction model using physiological data (vital signs and lab results) from the 2019 PhysioNet Challenge. In the first phase of our analysis, we trained a recurrent neural network using long short term memory (LSTM). While the LSTM parameters themselves can be optimized in well-understood ways to produce a more accurate classifier, the impact of pre-processing parameters on sepsis prediction performance remain largely unknown.

Now entering into our second phase, we are applying Augusta™ on a patient data set consisting of ~40,000 patients with the intent of more systematically considering the impact of upstream decisions made in processing the data before training a model.

Initial results look promising.

‘Contingent AI’, What is it?

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Machine Learning with ContingentAI
What is Contingent AI? 

In any data science pipeline there are a number of options that are selected for data processing (e.g. contrast settings for medical images, data imputation approaches, bandpass filter cut-offs for ecg signals). Typically, these options are selected manually based on previous experience of the Data Scientist or recommendations from previous, similar studies. In contingent AI, any “settable” parameter for data processing, data integration, or feature selection is permuted and the corresponding effects on the downstream predictive model measured. This process is similar to hyperparameter tuning in machine learning, however instead of optimizing only the machine learning model, the entire data science pipeline (including model selection) is subject to optimization.

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Dr. Calum MacRae Joins as Strategic Advisor

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Dr. Calum MacRaeAdvocate for healthcare technology innovation joins BioSymetrics in a mission to empower the application of AI within biomedicine.

NEW YORK, February 7, 2019 (GLOBE NEWSWIRE) – BioSymetrics, an artificial intelligence and machine learning SaaS company, announces Dr. Calum MacRae as Senior Advisor.  Dr. MacRae is an innovative pioneer in genetics, biology, and drug discovery.   With his deep knowledge and expertise, Dr. MacRae will assist BioSymetrics in the design and application of the company’s software for use in drug discovery, precision medicine and health systems.

“The introduction of AI into medicine is challenged by a fundamental defect of information content and standards,” said Dr. Calum MacRae. “BioSymetrics is leading the promise that we are entering an era in which precision medicine and AI finally attain real world applications; overcoming a number of AI challenges across a wide spectrum of biomedicine.” Read More