Machine (learning)-based techniques have made substantial advances recently, and there is a general suggestion that they will drive major changes in health care within a few years. Yet, we all suffer from the lack of precise comparative studies on the accuracy of machine-based interpretations of medical data. To fill this gap, in this paper we investigate on the efficacy of using an automated mood analysis methodology to understand how patients react to the prescription to take different kinds of prenatal diagnostic tests (invasive vs non-invasive) and to the corresponding outcomes, based on conversations developed on Reddit. Our study essentially provides answers to research questions concerning: i) the popularity of prenatal diagnosis, ii) the patients' sentiment about different prenatal tests, iii) the existence of a cause-effect relationship between prenatal testing and patients' mood, and iv) the type of dialogues held by patients and physicians on this topic. Nonetheless, a general result emerging from our research is that a machine-based decision loop for now still needs human involvement, at least to alleviate the tension between empirical data and their correct medical interpretation.
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