Imagine a situation where hundreds of medical scans, tests and images are screened by AI, and only the ones with abnormalities are presented to the experts. Other than the increased speed in diagnosis, medical experts will be able to take on more work and focus specialized abilities on high value, decision-making work.
Hospital performance, both operational and clinical, can be dramatically increased when data are used to detect areas of performance bottlenecks. Simulation of new management strategies will result in precise decision making and policy setting. Guesswork in hospital management will be a thing of the past.
Managing for optimal health in situations of limited resources is what Public Health is mostly about. Being able to quantify and predict the impact of public health policies where large sums of funds are being allocated would serve as a powerful tool for public health administrators and healthcare payers of any size of population.
Multiple factors impact the outcome of certain medical interventions ranging from behavioral change to medication to surgery, including individual patient differences. Machine learning on the effects of different treatment options matching key patient characteristics and attributes could lead to the desired state of Personalized Medicine.
We listen to your problems, your concerns, how you operate, and what matters most to you and your organization.
We take a good look at your data. Quality and quantity of your data, including how it is stored, are important in telling us how to address your issues.
We, and preferably with you, think of desired outcomes, measurements and applicable solutions. Formulating the question is a key part of success.
We train the machine. The machine will absorb all the knowledge from your organization from the data fed to it. It will see patterns. It becomes intelligent.
We, after satisfactory testing, help you deploy it in real-world situation, measure if it reaches the desired objective, tweak it for increased performance, and more tweaking until you are happy.
AUC is a useful metric when comparing between machine learning models but it does not address how effective a model is for screening.
We use precentage of image screend with radiologist-level sensitivity of 92.6% (%saved) as our performance metric.
|Accuracy||2/10 = 20%|
|Sensitivity||2/3 = 66%|
|%saved||5/10 = 50%|
|Positive Predictive Value||2/5 = 40%|
|Optimizations||Architecture||Public Data||Local Data||%saved||AUC|
We use embedding layers to extract features from catagorical data, combine them with numerical data, and model them through deep neural networks.
|Target Disease Groups||Mean Absolute Error (Days)|