OUR MISSION STATEMENT

Transforming diagnostics with machine learning and artificial intelligence

Machine learning and artificial intelligence have the potential to transform nearly every facet of healthcare, and prognostics/diagnostics for acute care are no exception. When coupled with rapid profiling of patient host response from blood, ML/AI-driven models promise earlier and more accurate detection of the presence and type of infection, timely stratification of patients by the severity of their condition, and characterization of patients into molecular subtypes more amenable to different treatment strategies.

Experience brings novel approaches

At Inflammatix ML, we understand that bringing world-class ID diagnostics to the point of care demands a holistic approach. Our highly interdisciplinary team draws on decades of experience in academic and industrial science with backgrounds ranging from applied statistics, computer science, engineering, bioinformatics, and software development. We pursue both methodological and applied research directions, developing novel models and methods when the need arises but also investigating the feasibility of the ‘tried-and-true’ as well as the ‘latest-and-greatest’.

We prioritize sharing our work and insights with the broader research community in both publications and international conferences. We continue to grow and curate one-of-a-kind patient datasets, leveraging information at multiple scales to guide selection of high-performing biomarkers and diagnostic classifiers. We engage stakeholders across the company and the medical community to help our test achieve leading performance and optimal integration with clinical workflows and decision-making.

Areas of research

Systems fordiagnostic classifierdevelopment

Systems for diagnostic classifier development

Systems fordiagnostic classifierdevelopment

Hyperparameter optimization of diagnostic classifiers

Systems fordiagnostic classifierdevelopment

Fairness in diagnostics

Systems fordiagnostic classifierdevelopment

Systems for diagnostic classifier development

Patient host response, as measured by expression of targeted mRNA biomarkers from blood, can vary from patient to patient. Previous approaches have developed host response signatures and classifiers that tend to generalize poorly, owing to biomarker selection and classifier training and validation on a limited, unrepresentative set of patient observations from a single study or hospital. Pooling of data across multiple studies has proven effective in producing more generalizable host response signatures and classifiers but introduces other methodological challenges.

For example, before being used in standard classifier development, our multi-cohort data must first be co-normalized to minimize spurious variation unrelated to our classification tasks. Also, the manner in which we select our classifiers must move beyond random cross-validation to reflect the structure and heterogeneity in our patient population and to provide more realistic estimates of generalization performance. In addition, our training data have been profiled with multiple assay platforms, none of which are fast enough to enable clinically actionable turnaround times for indications like sepsis.

Our biomarker signatures and classifiers must be able to generalize to measurements obtained on more deployment-ready platforms, possibly without access to such limited data at training time. We addressed these challenges in an important proof-of-concept study (Mayhew et al., 2020a) that helped systematize our process of diagnostic classifier development.

BLOG | Systems for diagnostic classifier development

Lessons learned for generative AI for tabular data

Kirindi Choi, Ljubomir Buturovic & Roland Luethy | August 29, 2023

BLOG | Systems for diagnostic classifier development

Decision Threshold Optimization for Diagnostic Tests using a Genetic Algorithm

Roland Luethy & Ljubomir Buturovic | March 15, 2023

BLOG | Systems for diagnostic classifier development

Searching for the best tabular data classifiers

Nandita Damaraju & Ljubomir Buturovic | June 22, 2022

BLOG | Systems for diagnostic classifier development

Adaptive CV: An approach for faster cross validation and hyperparameter tuning

Nandita Damaraju | February 3, 2022

Systems fordiagnostic classifierdevelopment

Hyperparameter optimization of diagnostic classifiers

An essential step in classifier selection is hyperparameter optimization, or the identification of values for a classifier’s hyperparameters that optimize some objective function (e.g. performance in cross-validation). While conventional methods of grid search and random sampling can still be effective, more sophisticated approaches such as Bayesian optimization (Snoek et al., 2012) and Hyperband (Li et al., 2018) have demonstrated gains in terms of both performance and efficiency (i.e. producing high-performing classifiers with fewer evaluations of candidate hyperparameterizations) on a range of ML benchmarks.

However, these ML studies tended to focus on computer vision tasks and to use large-scale and fairly homogeneous dat