Machine Learning in Healthcare

“Science calls for science“; if this had been a proverb, it would have been perfectly applicable between the science of medicine and the science of data; indeed, data science has long been used in the field of medicine and this in different areas such as the study and diagnosis of cases, survival models…etc.…and automatic image recognition and diagnosis. 

Machine learning (ML) and artificial intelligence (AI) models have significantly advanced medicine and the various associated disciplines, such as pharmacy and biostatistics, progress that currently needs to be improved. A dedicated technical organization to collect data, process it, and above all design efficient models and model management in healthcare, it is in this perspective, and this needs that platform exists, Hub model such as Verta, offer options to develop and concept machine learning models, manage them, test them and, above all, optimize them into Life Saving models.  The most common healthcare use cases for machine learning are automating medical billing, clinical decision support, and the development of clinical care guidelines.

Areas of application of Machine Learning in Healthcare 

The use of machine learning models in healthcare and medicine has its origins in predictive models of disease survival and the evolution of pandemics. Many areas use it n ‘have more as a forecasting tool than as a diagnostic tool.

Machine learning and survival forecasting (survival model)

This application of machine learning in medicine may not be very pleasing at first sight because it offers an effective tool for treatment to estimate the probability of survival of a patient suffering from a severe illness. This estimate may, for example, be established using dedicated models (Logit Model, Probit Model, Cox Model …). Still, by going more in the interpretation of parameters, specialists can extract variables or conditions that will have a direct impact on the survival of an individual, and removing these variables, it becomes possible, if medicine allows it, to improve the survival of patients and, why not, to save them.

assorted source codes Photo by Ali Shah Lakhani on Unsplash

Such data science undeniably requires powerful data management tools, machine learning model design, and collaborative work, such as data science platforms.

Machine learning and the evolution of pandemics

It is no secret that statistics and machine learning have been used or even over-exploited in the evaluation of the progression of the Covid-19 pandemic, this was notably manifested in the importance that all the planet and especially the companies and sectors concerned by healthcare have given to the collection of data relating to the number of people newly affected by the Covid-19 virus daily, one wonders why all this enthusiasm?!, and well the answer is simple; specialists and in particular states needed on the one hand to determine whether a current situation meant a pandemic wave, to predict its peak and consequently its end, the interest being to take the operational and organizational measures necessary to reduce the duration of the pandemic and limit as much as possible; thus, each action taken could directly impact the machine learning model established as a result.

Obviously, without an efficient data collection and management platform for the established models and, above all, for testing the model and therefore the life cycle, predicting the evolution of pandemics would have been very difficult to show almost impossible and achieve a maximum precision is not a luxury in such an application, hence the importance of model management in healthcare.

Machine learning as a diagnostic tool

Here is a leading area of the use of machine learning (ML) and deep learning models, the area is that of automated diagnostics by analyzing X-ray or scanner images.

It is clear that such an application helps speed up diagnostics and save lives.

Imagine being a doctor and working in a busy hospital, diagnosing by looking at X-ray images or MRIs would quickly become tiring or even very difficult to perform, this can be done using an automatic system. dedicated to the development of diagnostics, these diagnostics are the result of the use of the machine learning model of pattern recognition, a field in which data science excels.

The fact of connecting radiography or scanner equipment to data management and machine learning platforms will greatly contribute to the design of models and to train them in such a way that the established diagnosis will be more and more efficient and reach an accuracy rate close to 100%.

Is Your Doctor a Robot?

We have already discussed the use of machine learning and deep learning models in medicine for establishing medical diagnoses, but come to think of it why not simply allow a machine to go even more and heal the sick or to perform precise surgery, the challenge of such technology lies in the high precision of the surgical movements that a robot can perform, especially when it is a sensitive organ such as the brain, where the error is not allowed.

As Kuromon Market in Osaka was about to close for the evening I sampled some delicious king crab and did a final lap of the market when I stumbled upon one of the most Japanese scenes I could possibly imagine, a little girl, making friends with a robot. Photo by Andy Kelly on Unsplash

It goes without saying that making a robot for surgery requires enormous learning, using cutting-edge electronic technology and of course very advanced and sophisticated mathematical machine learning models.

Once again, the contribution of data management platforms and machine learning and deep learning models appears in the realization of a project of such a scale, this can be very futuristic, but the platform already exists in the world image of Verta.

Conclusion

Data science will obviously never replace a doctor for the establishment of a diagnosis, but it will make its decisions and its diagnosis more precise and will therefore reduce errors, which will necessarily lead to saving more human lives. and this thanks to the machine learning model.

 

Blog resources:

  1. https://healthitanalytics.com
  2. http://www.sthda.com/english/wiki/survival-analysis-basics
  3. https://www.mayoclinic.org
  4. https://clinical.r-biopharm.com
  5. https://arxiv.org/abs/2104.09325
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