Screening is important for the diagnosis and treatment of a wide variety of diseases. A good screening policy should be personalized to the features of the patient and to the dynamic history of the patient (including the history of screening).

Screening is important for the diagnosis and treatment of a wide variety of diseases. A good screening policy should be personalized to the features of the patient and to the dynamic history of the patient (including the history of screening). The growth of electronic health records data has led to the development of many models to predict the onset and progression of different diseases. However, there has been limited work to address the personalized screening for these different diseases. In this work, we develop the first framework to construct screening policies for a large class of disease models.

While mammograms are currently the gold standard in breast cancer screening, swirls of controversy exist regarding when and how often they should be administered. On the one hand, advocates argue for the ability to save lives: Women aged 60-69 who receive mammograms, for example, have a 33 percent lower risk of dying compared to those who don't get mammograms. Meanwhile, others argue about costly and potentially traumatic false positives: A meta-analysis of three randomized trials found a 19 percent over-diagnosis rate from mammography. Even with some saved lives, and some overtreatment and overscreening, current guidelines are still a catch-all: Women aged 45 to 54 should get mammograms every year. While personalized screening has long been thought of as the answer, tools that can leverage the troves of data to do this lag behind.

While mammograms are currently the gold standard in breast cancer screening, swirls of controversy exist: advocates argue for the ability to save lives, (women aged 60 to 69 had a 33 percent lower risk of dying compared to those who didn't get mammograms), and another camp argues about costly and potentially traumatic false positives (a meta-analysis of three randomized trials found a 19 percent over-diagnosis rate from mammography). Even with some saved lives, and some overtreatment and overscreening, current guidelines are still a catch all: women aged 45 to 54 should get mammograms every year. While personalized screening has long been thought of as the answer, tools that can leverage the troves of data to do this lag behind. This led scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Jameel Clinic for Machine Learning and Health to ask: Can we use machine learning to provide personalized screening? Out of this came Tempo, a technology for creating risk-based screening guidelines.

Screening is important for the diagnosis and treatment of a wide variety of diseases. A good screening policy should be personalized to the disease, to the features of the patient and to the dynamic history of the patient (including the history of screening). The growth of electronic health records data has led to the development of many models to predict the onset and progression of different diseases. However, there has been limited work to address the personalized screening for these different diseases. In this work, we develop the first framework to construct screening policies for a large class of disease models. The disease is modeled as a finite state stochastic process with an absorbing disease state. The patient observes an external information process (for instance, self-examinations, discovering comorbidities, etc.) which can trigger the patient to arrive at the clinician earlier than scheduled screenings. The clinician carries out the tests; based on the test results and the external information it schedules the next arrival. Computing the exactly optimal screening policy that balances the delay in the detection against the frequency of screenings is computationally intractable; this paper provides a computationally tractable construction of an approximately optimal policy. As an illustration, we make use of a large breast cancer data set. The constructed policy screens patients more or less often according to their initial risk -- it is personalized to the features of the patient -- and according to the results of previous screens – it is personalized to the history of the patient. In comparison with existing clinical policies, the constructed policy leads to large reductions (28-68 %) in the number of screens performed while achieving the same expected delays in disease detection.