Revolutionizing healthcare: an invitation to clinical professionals everywhere

Breast cancer

Breast cancer is one of many common diseases that can be fought more successfully by clinicians using machine learning at all stages of patient-clinician interaction, from screening through to treatment courses.

Screening: from “one-size-fits-all” to personalized

For diseases like breast cancer, screening often represents the start of the patient journey. Time is of the essence when diagnosing and treating breast cancer, since earlier diagnoses yield higher likelihood of survival. Yet adolescent and young adult female breast cancer patients are significantly less likely than older adults to be diagnosed with early-stage disease—a fact that decreases the likelihood of survival of thousands of women each year. Younger women are unlikely to be screened because current screening policies are guided by clinical practice guidelines that take a “one size-fits-all” approach designed to work well on average for an entire population.

Since the risks and benefits of screening tests are functions of each patient’s features and characteristics, personalized screening policies tailored to the features of individuals would yield a significantly more accurate and efficient result. Several machine learning approaches to this problem have been developed by our own lab. One example of such a tool is ConfidentCare, a computer-aided clinical decision support system that provides personalized screening policies based on healthcare data. This is done by identifying clusters of patients with similar features, then learning the “best” screening procedure for each cluster, while ensuring that the learned screening policy satisfies a predefined accuracy requirement with a high level of confidence for every cluster. We have already demonstrated that, when applied to real-world data, ConfidentCare outperforms current clinical practice guidelines in terms of cost-efficiency and false positive rates.

Personalized screening policies will sometimes lead to recommendations to screen more often, or to use more accurate but perhaps more invasive or more expensive screening procedures. They will also sometimes be exactly the opposite, because many patients who are low risk do not need to be screened as often and do not need costly or invasive screening procedures. Less frequent screening of patients at low risk frees up scarce resources to provide more frequent screening for patients at high risk. In addition, machine learning can learn the value of screening and of screening over time for the particular patient at hand. And it’s not only the timing of screening that should be individualized: machine learning can learn the relative value of various types of information (and the value of such information over time) and hence learn that some patients require a particular method of screening or follow-up while others do not.

Ensuring timely diagnosis by understanding decision-making

Providing personalized screening policies, as described above, is an important first step to identifying patients who may already have undiagnosed diseases or conditions, such as breast cancer. Clinicians still, however, need to make a sequence of diagnostic decisions: if a patient has been flagged for screening or presents with a set of symptoms, the clinician must decide which diagnostic tests should be prescribed, in which order they should be taken, and when an official diagnosis should be declared.

No two patients or clinicians are alike, which may explain why diagnosis is an area in which regional and institutional variation in clinical practice is commonplace. This variation has been shown to contribute substantially to healthcare costs.

This is an issue that can be addressed and mitigated by quantifying the different decision-making processes of clinicians and observing their demonstrated behavior. By observing the sequences of diagnostic tests prescribed, we can learn about the tradeoffs implicitly encoded into the patterns of diagnostic decision making. This applies, in particular, to the timing and motivators of decisions made regarding the cost-benefit balance of acquiring information—for example, when running diagnostic tests that may potentially be costly.

Our own lab is beginning to conduct research in this area. We have developed and validated a number of tools that could be used to audit and improve decision-making behavior. In the case of a breast cancer patient, for example, such tools could enable us to identify a patient whose diagnosis might have been delayed as a result of a belated decision to run a test. More generally, we could quantify and compare the priorities of decision-makers such as clinicians, or detect hidden biases. We could learn, for example, which diseases clinicians consider more important to diagnose correctly, or which tests tend to be over-prescribed. Such learnings can be used to improve consistency and quality of practice.

Diagnosis: saving lives by catching cancer earlier

The sections above demonstrate how machine learning can help identify and flag at-risk patients, while also understanding and improving decision-making regarding the appropriate course of action for diagnosis. The diagnosis process itself can also benefit from the application of machine learning: while this capability is extremely well-documented (often to the exclusion of other notable achievements), it is still worth mentioning as part of a larger whole.

Machine learning algorithms have demonstrated a clear ability to aid radiologists in accurately identifying images that are problematic (i.e. those featuring areas that appear suspicious), and numerous studies have shown that the performance of radiologists is significantly improved when using a deep learning computer system as decision support in breast cancer diagnosis. This directly supports early diagnosis of breast cancer, thereby increasing the variety of available treatments and likelihood of survival.

We have explored the potential of such tools in a variety of studies, perhaps the most notable of which led to the development of a triaging system called MVMT, which has the potential to save countless hours for overworked radiologists and provide them more time to focus on the difficult and complex cases that warrant additional scrutiny.

Results obtained on a private dataset show that MVMT reduced the number of radiologist readings required by 43% while improving the overall diagnostic accuracy in comparison to readings done by radiologists alone.

Getting the best results from the referral process

Once a patient has been screened and diagnosed, a number of key decisions need to be made regarding their options for care. In the case of a disease such as breast cancer, it is often the case that numerous specialists need to be appointed. The referral process, however, is far from simple, and variation is commonplace. In current clinical practice, patients are referred to experts in an ad-hoc manner based on signs and symptoms of the patient, patient’s or primary care physician’s preference, insurance plan, and availability of the physician. As a result, these decisions can be made in a somewhat arbitrary and potentially inefficient manner, with the result being that patients may not end up seeing the right person.

A few years ago, we introduced a machine learning technique to optimize clinical workflows by personalizing the process of matching new patient cases with appropriate diagnostic expertise from a range of domain experts who specialize in similar types of cases. While our algorithm is general and can be applied in numerous medical scenarios, we illustrated its functionality and performance by applying it to a real-world breast cancer diagnosis dataset.

Such an approach also lends itself quite naturally to the task of composing a team of diverse experts (for example, an oncologist, a radiologist, and general practitioner) to treat patients suffering from complex diseases such as breast cancer.

Operation of the proposed system for clinic i. In this example, the diagnostic decision for the patient is made by a human expert from clinic j. Then, after some time the true health state of the patient is revealed. Based on this, clinic i updates the diagnostic accuracy of clinic j for that context, while clinic j updates the diagnostic accuracy of its expert f.

When implemented in a clinical context, this kind of system could feature safeguards: for example, referral recommendations made by the system will be examined by a clinician before the final prediction is made. For any patient, it would therefore remain the clinician’s discretion whether to rely on or to disregard the recommendation of the algorithm. Similarly, recommendations could take into account the patient load of individual clinicians, ensuring that workloads remain as manageable as possible.

Treatment effect estimation: developing effective and personalized treatment plans

Once a patient has been diagnosed with a disease such as breast cancer and referred to a specialist (or a group of specialists), a treatment plan for that patient needs to be developed. This involves identifying when to give treatments, and how to select among multiple treatments over time. These are important and complex problems with few existing solutions. Choosing the best treatment for a particular patient requires estimating the effects of a variety of treatments for that particular patient. To accomplish this, we need to understand personalized treatment effects and not just average treatment effects. Unfortunately, this is essentially still beyond the capabilities of state-of-the-art medical knowledge.

Recent advances in machine learning, however, have led to tools that are capable of learning personalized treatment effects by beginning where clinical trials leave off: learning from observational data captured in clinical practice.

The impact of accurate prediction of personalized treatment effects is hard to overestimate, because it provides a basis for the choice of treatment according to the features of the particular patient at hand. This leads to improved outcomes for patients, who receive the best treatment, and to improved use of resources (i.e. not wasting scarce resources on patients who will receive little or no additional benefit from them). Such methods can produce predictions that come with statistically valid confidence intervals, which are invaluable in a clinical context.

When applied to breast cancer, we can use machine learning to draw upon observational data to devise and predict the likely outcome of multiple treatment courses. This includes determination of which specific treatments will likely need to be given at various points in time. This allows clinicians to “mix and match” combinations of treatments, such as chemotherapy and radiotherapy, to maximize benefit and minimize risk.

Survival analysis with competing risks

Treatment decisions of the nature outlined immediately above, as well as ongoing patient management, need to factor in the evolving likelihood of survival over time, and must also include the possibility of complications arising from competing risks. The problem of survival analysis with competing risks has recently gained significant attention in the medical community due to the realization that many chronic diseases possess a shared biology. For example, cancer and cardiovascular disease (CVD) possess various similarities and possible interactions, including a number of similar risk factors. In addition, many existing cancer therapies increase a patient’s risk for CVD. At the same time, conventional methods for survival analysis, such as the Kaplan-Meier method and standard Cox proportional hazards regression, are not equipped to handle competing risks.

It is obviously important that patients who are at risk of both cancer and CVD be provided with a joint prognosis of mortality due to the two competing diseases in order to properly manage therapeutic interventions. This is a challenging problem, since CVD patient cohorts are very heterogeneous; CVD exhibits complex phenotypes for which mortality rates can vary as much as 10-fold among patients in the same phenotype.

Machine learning, however, can accurately model survival of patients in such a highly heterogeneous cohort, while treating CVD and cancer as competing risks. We have demonstrated this our lab’s own survival models for competing risks, DeepHit and Dynamic-DeepHit, which offer personalized actionable prognoses that clinicians can use to design personalized treatment plans. Experiments on real-world data have demonstrated that our models outperform state-of-the-art survival models.

We have also designed survival analysis tools that can provide predictions are the cumulative product of many different models, but which are “weighted” to prioritize the best-performing models, thereby yielding the best possible result. This kind of versatility (being able to compare and choose between many tools at once to get the best prediction) adaptability is a particular strength of machine learning.