DPSCREEN: Dynamic Personalized 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. 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.
The objective of the paper is to find the best policy for patient screening given the pertinent information. To provide the policy, a disease should be modeled as a finite state stochastic process. The policy is trying to minimize screening costs and delays. The authors propose an approximate solution that is computationally efficient. The experiments on simulated data related to breast cancer indicate that the proposed algorithm could reduce delays while keeping the same cost when compared to a trivial baseline
  Positives:
+ personalized screening is an important topic of research
+ the proposed algorithm is reasonable and well-developed
+ the results are promising
+ the paper is well written
  Negatives:
- the proposed algorithm is purely an academic exercise. It assumes that the disease model of a given patient is known, which could be never assumed in practice. The main obstacle in personalized screening is not coming up with a policy when the model is known, but inferring the model from data. 
- the methodological novelty is not large
- the experiments only compare the proposed algorithm to a baseline doing annual screening. Given the previous point about the disease model, an important question that should be studies experimentally is the robustness of the proposed method to inaccuracies in the disease model. In other words, how sensitive is the policy to uncertainties in the disease model.
  Overall, the topic is interesting and the proposed algorithm is reasonable. However, the methodological novelty is not large and the paper would need to do a better work to convince readers that this work is practically relevant.