Improve your customer service with specialized chat bots that interact with patients through chat windows. Automate scheduling follow-up appointments with patients. Minimise human error by ensuring they are directed to the appropriate healthcare department, and reduce kpi times.
Build state of the art classification algorithm for diagnosing patients based on mere mobile phone photos. Identify rare diseases with learning algorithms such as functional-gradient boosting (FGB), which self-report behavioural data to allow distinguishing between people with rare and more common chronic illnesses.
Supervised learning will allow physicians to select from more limited sets of diagnoses. An example of this is the estimation of patient risk factors relative to symptoms and genetic information. Such models can be calibrated and trained on micro biosensors and mobile phone applications which will give more sophisticated health data to assess treatment efficacy. Reduce treatment cost and optimize individual patent health.
Machine learning in early-stage drug discovery can be used to estimate the success rate of initial screening of drug compounds relative to biological factors. The application of unsupervised learning (k-nearest neighbor algorithm) to precision medicine has identified mechanisms in multi-factor diseases and created alternative treatments and therapies.
Clinical Trial Research
Selecting and identifying ideal candidates for clinical trails by sampling from a broader range of data to find features that are currently underutilised, an example of this could be social media and number of doctor visits. Use machine learning to improve the safety of the trialists by monitoring their health in real-time remotely.
Epidemic Outbreak Prediction
The monitoring and predicting of epidemic outbreaks have been performed successfully by machine learning technologies for a number of years now. Collecting vast amounts of data from satellites, historical healthcare databases, and social media; one can train support vector machines and deep neural networks potential outbreaks such as malaria and Ebola.
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