“Science calls for science“—if this had been a proverb, it would have been perfectly applicable to the science of medicine and the science of data; indeed, data science has long been used in medicine and in different areas such as the study and diagnosis of cases, survival models… etc..…and automatic image recognition and diagnosis. Now’s the time for machine learning in healthcare.
Machine learning (ML) and artificial intelligence (AI) models have significantly advanced medicine and the various associated disciplines, such as pharmacy and biostatistics, and progress has been slow to improve. To apply machine learning algorithms, a dedicated technical organization needs to collect data, process it, and above all, design efficient models and model management in healthcare, it is in this perspective that this needs that platform exists; Hub models 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 originates in predictive disease survival models and pandemics’ evolution. Many areas use machine learning as a forecasting tool over 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 into the interpretation of parameters, specialists can extract variables or conditions that will have a direct impact on the survival of an individual, and by removing these variables, it becomes possible, if medicine allows it, to improve the survival of patients and, why not, to save them.
Machine Learning Photo by Ali Shah Lakhani on Unsplash
Data scientists undeniably require powerful data management tools, such as data science platforms, to configure machine learning models for collaborative work.
Supervised 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 to 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 for the use of machine learning (ML) and deep learning models: automated diagnostics by analyzing X-ray or scanner images.
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. 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.
Connecting radiography or scanner equipment to data management and machine learning platforms will greatly contribute to the design of models and their training, making the established diagnosis more efficient and reaching 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 why not simply allow a machine to go even further and heal the sick or 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 error is not allowed.
Photo by Andy Kelly on Unsplash
Making a robot for surgery requires enormous learning, cutting-edge electronic technology, and 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 realizing a project of such a scale. This can be futuristic, but the platform already exists in Verta’s world image.
Conclusion
Data science will obviously never replace a doctor in establishing a diagnosis, but it will make its decisions and diagnoses more precise and, therefore, reduce errors. This will necessarily lead to saving more human lives, and this is thanks to the machine learning model.
Updated: See how ReferralMD uses machine learning to speed up and automate the patient intake process for health care practices.
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