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Lessons Learned from Developing ML for Healthcare

Machine learning (ML) methods are not new in medicine — traditional techniques, such as decision trees and logistic regression, were commonly used to derive established clinical decision rules (for example, the TIMI Risk Score for estimating patient risk after a coronary event). In recent years, however, there has been a tremendous surge in leveraging ML for a variety of medical applications, such as predicting adverse events from complex medical records, and improving the accuracy of genomic sequencing. In addition to detecting known diseases, ML models can tease out previously unknown signals, such as cardiovascular risk factors and refractive error from retinal fundus photographs.

Beyond developing these models, it’s important to understand how they can be incorporated into medical workflows. Previous research indicates that doctors assisted by ML models can be more accurate than either doctors or models alone in grading diabetic eye disease and diagnosing metastatic breast cancer. Similarly, doctors are able to leverage ML-based tools in an interactive fashion to search for similar medical images, providing further evidence that doctors can work effectively with ML-based assistive tools.

In an effort to improve guidance for research at the intersection of ML and healthcare, we have written a pair of articles, published in Nature Materials and the Journal of the American Medical Association (JAMA). The first is for ML practitioners to better understand how to develop ML solutions for healthcare, and the other is for doctors who desire a better understanding of whether ML could help improve their clinical work.

How to Develop Machine Learning Models for Healthcare
In “How to develop machine learning models for healthcare” (pdf), published in Nature Materials, we discuss the importance of ensuring that the needs specific to the healthcare environment inform the development of ML models for that setting. This should be done throughout the process of developing technologies for healthcare applications, from problem selection, data collection and ML model development to validation and assessment, deployment and monitoring.

The first consideration is how to identify a healthcare problem for which there is both an urgent clinical need and for which predictions based on ML models will provide actionable insight. For example, ML for detecting diabetic eye disease can help alleviate the screening workload in parts of the world where diabetes is prevalent and the number of medical specialists is insufficient. Once the problem has been identified, one must be careful with data curation to ensure that the ground truth labels, or “reference standard”, applied to the data are reliable and accurate. This can be accomplished by validating labels via comparison to expert interpretation of the same data, such as retinal fundus photographs, or through an orthogonal procedure, such as a biopsy to confirm radiologic findings. This is particularly important since a high-quality reference standard is essential both for training useful models and for accurately measuring model performance. Therefore, it is critical that ML practitioners work closely with clinical experts to ensure the rigor of the reference standard used for training and evaluation.

Validation of model performance is also substantially different in healthcare, because the problem of distributional shift can be pronounced. In contrast to typical ML studies where a single random test split is common, the medical field values validation using multiple independent evaluation datasets, each with different patient populations that may exhibit differences in demographics or disease subtypes. Because the specifics depend on the problem, ML practitioners should work closely with clinical experts to design the study, with particular care in ensuring that the model validation and performance metrics are appropriate for the clinical setting.

Integration of the resulting assistive tools also requires thoughtful design to ensure seamless workflow integration, with consideration for measurement of the impact of these tools on diagnostic accuracy and workflow efficiency. Importantly, there is substantial value in prospective study of these tools in real patient care to better understand their real-world impact.

Finally, even after validation and workflow integration, the journey towards deployment is just beginning: regulatory approval and continued monitoring for unexpected error modes or adverse events in real use remains ahead.

Two examples of the translational process of developing, validating, and implementing ML models for healthcare based on our work in detecting diabetic eye disease and metastatic breast cancer.

Empowering Doctors to Better Understand Machine Learning for Healthcare
In “Users’ Guide to the Medical Literature: How to Read Articles that use Machine Learning,” published in JAMA, we summarize key ML concepts to help doctors evaluate ML studies for suitability of inclusion in their workflow. The goal of this article is to demystify ML, to assist doctors who need to use ML systems to understand their basic functionality, when to trust them, and their potential limitations.

The central questions doctors ask when evaluating any study, whether ML or not, remain: Was the reference standard reliable? Was the evaluation unbiased, such as assessing for both false positives and false negatives, and performing a fair comparison with clinicians? Does the evaluation apply to the patient population that I see? How does the ML model help me in taking care of my patients?

In addition to these questions, ML models should also be scrutinized to determine whether the hyperparameters used in their development were tuned on a dataset independent of that used for final model evaluation. This is particularly important, since inappropriate tuning can lead to substantial overestimation of performance, e.g., a sufficiently sophisticated model can be trained to completely memorize the training dataset and generalize poorly to new data. Ensuring that tuning was done appropriately requires being mindful of ambiguities in dataset naming, and in particular, using the terminology with which the audience is most familiar:

The intersection of two fields: ML and healthcare creates ambiguity in the term “validation dataset”. An ML validation set is typically used to refer to the dataset used for hyperparameter tuning, whereas a “clinical” validation set is typically used for final evaluation. To reduce confusion, we have opted to refer to the (ML) validation set as the “tuning” set.

Future outlook
It is an exciting time to work on AI for healthcare. The “bench-to-bedside” path is a long one that requires researchers and experts from multiple disciplines to work together in this translational process. We hope that these two articles will promote mutual understanding of what is important for ML practitioners developing models for healthcare and what is emphasized by doctors evaluating these models, thus driving further collaborations between the fields and towards eventual positive impact on patient care.

Acknowledgements
Key contributors to these projects include Yun Liu, Po-Hsuan Cameron Chen, Jonathan Krause, and Lily Peng. The authors would like to acknowledge Greg Corrado and Avinash Varadarajan for their advice, and the Google Health team for their support.

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.