Executive Summary

Conventional machine learning has had its application to medical discovery and clinical decision support following the start of the AI revolution. ML methods have found its acceptance in the mainstream medicine. To build upon the conventional ML, the methods of deep learning promise one more ability layer in automating difficult cognitive tasks. From large datasets, people are identified with the use of Beacon API. The reconstruction approach is inferred probabilistically and close relatives are exposed and from a DNA mixture, people are identified with the use of re-identification technique. The strategies that are followed to address the problems of deep learning include data piracy comprising of anonymous data, federated learning, and transfer learning. The next strategy is explainability which provides accurate diagnosis with extraction of more features. Explainability remains a very interesting research area in minimizing the mystery behind the deep learning with giving explanation and making predictions.

It is recommended that ML should be applied so that it is computationally efficient to much greater degree with the provision of more interpretable models, which can prove to have greater accuracy in the end. There is real scientific and clinical benefit of performing a meticulous assessment of the conventional models.

Recommendation ML algorithm is one of the key strategies in achieving the optimal results. There is sometimes confusion regarding the deep learning methods generalization for the analytic technique of catch-all data. Nonetheless, other methods of ML should be applied so that it is computationally efficient to much greater degree with the provision of more interpretable models, which can prove to have greater accuracy in the end. There is real scientific and clinical benefit of performing a meticulous assessment of the conventional models. The deep learning does have usefulness of some specific tasks such as classification of medical images. However, deep learning does not have suitability for the problems of all clinical data. Across a number of clinical problems, off-the-shelf, and conventional ML methods can train faster and better performance overall can be achieved in comparison to the deep natural netwo .

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The confidence and unbridled excitement for deep learning can be leading to inappropriate applications, impracticable expectations, and ignorance of ML tools that are more appropriate. Moreover, unlike other industries, working in isolation is not recommended in healthcare data science. This explicitly has the healthcare practitioners’ cooperation, data scientists, and informatics specialists. The current clinical workflow must have the in-depth knowledge so that the from the relevant data sources the model is pulled. On the relevant patient cohort, training is needed and at a relevant point the application must be there in the time of clinical workflow.