Medical treatment using machine learning

Medical treatment uses tools for working with randomized controlled trial data, where we only observe what happens to a patient if they've got a treatment, and we want to know what would happen to the patient if they had not received the treatment and vice versa.

Machine learning allows us to build models that associate a broad range of variables with the effect of a treatment on individual patients.

The data science and machine learning platform Neural Designer brings together different data types into a single model to better diagnose diseases.


  1. Objectives.
  2. Benefits.
  3. Approach.
  4. Conclusions.


The aim of medical treatment is to assess which tools better fits each patient in order to survive to the disease. Besides, the objective is to determine one or more treatment, whether we want to avoid acquiring a disease or condition, are suffering from symptoms, have caught a cold or the flu, have developed diabetes or cancer, or have injured yourself in an accident or a fall. All of these require treatments.

Thankfully, a large amount of data is currently available to clinicians, ranging from details of clinical symptoms to various types of biochemical assays and outputs of imaging devices.

We put up a model to predict the best type of treatment. These are some examples of types of data that could be useful to make an accurate medical treatment using machine learning:


Treatments fall into four categories, based on their potential outcomes. Some benefits of leveraging machine learning in medicine are the following:


Avoiding a wealth problem before it starts, using vaccines and presonalized medicines to prevent from genetic diseases.


Curing, healing or repairing after being diagnosed with a disease or condition, to return to 100% of our health. For this, we can use different antibiotics or antihistamines, a surgical treatment attempts to repair a problem and physical therapy for mucles and other parts that have been strained and damaged.


Many diseases and conditiones cannot be cured by existing medical treatments, then the goal is to manage to make the patient's longevity and quality of life are maximized by managing the problem.


Palliative care is provided to patients who have severe, debilitating, and life-ending diseases. Its goal is to relieve symptoms such as pain and make a patient comfortable, with little or no attempt to cure or manage the disease or condition that causes the discomfort.


Neural networks are the most important technique for machine learning and artificial intelligence. The following flow chart shows how to build and use a neural network for medical prognosis.

The first step is to create a data set by collecting all the personal, environmental, disease, and comorbidity data about a cohort, as well as the outcome of those patients.

Then, we need to build a neural network that will forecast the outcome of new patients.

The next graph illustrates a neural network for medical prognosis.

A training strategy is applied to the neural network to discover the underlying relationships in the data set.

To improve the predictive capabilities of the model, we can also apply model selection by trying combinations of variables and choosing those with more impact on the outcome.

Then, the resulting model undergoes an exhaustive testing analysis.

Finally, after model deployment, the neural network is used to predict the future health of new patients.

The data science and machine learning platform Neural Designer guides you through this process so that you focus on the medical aspects and not on the details behind machine learning.


Machine learning enables us to build predictive models that help doctors predict the outcome of a disease and choose the best possible treatment for each patient.

As we want to help in this mission to improve healthcare and push researchers to get the best results they can, we are offering special prices for academic programs.

Related solutions:

Related examples:


Subscribe To Our Newsletter