New Medical Breakthrough Using Machine Learning Model to Predict ALS Survival Odds

New Medical Breakthrough Using Machine Learning Model in Predicting ALS Survival Odds

New Medical Breakthrough Using Machine Learning Model to Predict ALS Survival Odds

Machine learning Model and intelligent data analysis have now become the ultimate buzzwords in the field of medical sciences. It has helped mine out potential solutions to medical conditions and sustenance and optimization of treatment procedures in leaps and bounds. This being said, the aid of algorithms and artificial intelligence although promising is still a perspective that requires more meticulous work to gain commercial and reliable experience. Research in this aspect is being carried out extensively. Disease management is being tackled using this analytical method with the aim of gaining a substantial success rate.

Machine Learning Model for ALS survival prediction

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ALS is a condition that is highly degenerative, with no complete cure but the scope for maintenance. This motor neuron disease deteriorates conditions to the extent of eventual muscle weakening leading to fatal repercussions like difficulty in breathing. Hence determining the survival rate of this condition along with the potential factors conducive to the same helps ease up the process of disease management immensely. This condition can be genetic and has no known cure till date hence making it a condition that is in dire need of techniques and aids that help sustain the patient’s life.

The team of researchers at the University of Michigan has reported a life-changing model in the form of a new hazard ranking algorithm that is highly promising in boosting the survival rate of ALS patients.

The Need for a New Model

The exigent Cox proportional hazards model that is commonly used can lead to the eventuality of incorrect forecasts if not applied correctly.

 This gave rise to the need for spawning variations to this model or whole new statistical models for more accurate forecasts and more precise predictions.

Factors previously considered for predictions of survival

  • Patient’s age at the onset of the disease and the uric acid levels- Significant variations in these factors made them candidates for becoming the components of the model
  • The only available medication called Rilutek- Rejected as a factor because of failure at distinguishing long-term and short-term therapy users from available patient data

A succinct breakdown of the brand-new model

GuanRank– a hazard ranking algorithm helps convert the compiled patient data into hazard ranks which helps cultivate potentially more accurate than the conventional methods and models that are currently used.

The components of this research model have helped realize that optimizing respiratory treatments can become the solution for ALS disease management and survival.

This model was successfully tested on the available PRO-ACT Database of ALS clinical trials to help boost ALS survival ranking.

Researchers now plan to use this state of the art predictive power tool on other databases inclusive of other diseases.

This path-breaking research has further cemented the era of artificial intelligence and machine learning as the go-to solution to the issues rampant in medical science.

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