A data-driven approach to pre-operative evaluation of lung cancer patients

Abstract

Many early stage lung cancer patients have resectable tumors, however, their cardiopulmonary function needs to be properly evaluated before they are deemed operative candidates. Such patients are typically asked to undergo standard pulmonary function tests, including cardiopulmonary exercise tests (CPET) or stair climbs. The standard tests are conducted only at selected healthcare provider locations, and are labor intensive. In addition, they are sometimes ineffective due to patient co-morbidities, such as limited mobility, which limits patient participation. To address these shortcomings, we envision that cardiopulmonary function can be evaluated in the patient's environment using an inexpensive wearable device during routine physical activities. We present a cloud-connected mask that is fitted with CO 2 , O 2 , flow volume, and accelerometer sensors. The data collected from the device is transmitted to a cloud service, which facilitates utilization of various data mining algorithms for extraction of insights from the data. As a necessary first step toward cardiopulmonary function evaluation, we study automatic recognition of the user's physical activity from mask sensors data via an empirical analysis of several data representation and classification algorithms. The results demonstrate accurate activity recognition using mask sensors, and underscore the potential of our approach for cardiopulmonary function evaluation.