Healthcare and pharmaceutical research, like any other industry, is driven by the power of vector, table and matrix processing. This underpins key research work around statistical, computational biology, process modeling and simulation.

In particular, machine and deep learning, both training and trained, and key data-oriented factor analysis, like feature extraction and model calibration. Learn how  Syneos Health built a data timehouse to accelerate AI innovation with time-series queries running 100X faster at 1/10th of the cost of a traditional data warehouse or lake, and accelerate workflows with its 100k concurrent run, billion virtual patient AI simulator.

Use Python and low code approaches to accelerate research, incorporate production-grade test data-sets and, when ready, export into production infrastructures seamlessly, to enhance your research strategies in the lab, at the hospital and in the research center.

Construct relevant databases from multiple streams – operational data, trial observations, simulated data and machine and sensor data.

With KX, data engineers can use multi modal capabilities to group relevant information with diverse data structures together, bringing together pharmaceutical claims, patient diagnoses, and daily enrolment data in a kdb database indexed by time. This allows relevant data to be quickly identified for deeper trend and cohort analysis to improve clinical trial selection all at the speed of thought.

Reduce downtime, improve fault detection, and improve visibility of operational equipment efficiency –

Use KX to assess operator performance by assessing key indicators, developing alerts, and enforcing remedial actions where needed. Share productivity and performance data throughout the business – 60% faster than before deployment in the case of one medical equipment manufacturer. Deploy real-time insights to help management with faster oversight, and an ability to drill down into particular issues quickly, future forecasting and strategic digitization initiatives.

Edge connectivity accelerates insights from millions of sensors across machines and production lines, providing visibility of component variances under different operating conditions and get to root failure issues more quickly and definitively.

Systems can be stress-tested before failure causes an expensive stoppage, empowering change from reactive repair to proactive maintenance. With faults identified down to individual sensors, task workers can prioritize their response resource appropriately to better manage medical device production lines.

Our previous means was fine for running a few channels of data, but with the explosion of data we now collect from sensors across every system, the math was a minute-and-a-half behind. That’s too late to intervene and prevent failure. Now we take advantage of end-to-end monitoring in real-time, on KX, to improve performance and reliability.

Biomedical signal processing involves acquiring, pre-processing and modeling physiological signals to identify patterns and signals.

Sources can include neural activity, cardiac rhythm, muscle movement, and other physiological activities, for example via an electrocardiogram (ECG), an electroencephalogram (EEG), and electromyography (EMG) used for diagnosis and as indicators of overall health.

KX facilitates a workflow including signal acquisition, visualization and annotation, with pre-processing and feature extraction driving into classification and predictive models and/or used directly for diagnosis.

In either case, there is easy interoperability with production Python code or research-oriented Jupyter Notebooks for training and calibrating predictive models that, once trained, validated, and exported as inferred models, can perform live diagnosis in real-time.

Monitoring, and proactive prevention of environmental and epidemiological breaches within processes can significantly reduce regulatory penalties and reputational damage.

Being able to leverage real-time insight for better use of resources, extending the effective equipment lifespan, detecting breaches, reducing waste, and reducing re-work all combine to represent a measurable and credible sustainability initiative. In addition, use 10X to 100X more efficient kdb infrastructure when compared to traditional warehouse, lake and lakehouse infrastructures for common analytics queries.

In our testing, KX presents – immediately – which components are more prone to deviation affecting performance and reliability. This used to require significant time and effort to spot. We now get to the issue right away.