Abstract
Healthcare intelligence is derived from human-centric solutions (predictive and analytical) that deal with diagnosis and treatment based on the patient’s information. In an attempt to embed computational accuracy, MYCIN (a rule-based system) was developed in the 1970s, to diagnose the blood-borne bacterial infections. Pharmacogenomics, the study of individualized medicine and lifesaving treatments, aims to identify the effect of genes in response to drugs and treatments. It is emerging and offering collaborative solutions of pharmacology (the science of drugs), genomics (study of genes), and machine intelligence (AI technologies). Machine learning and natural language processing are being used by IBM Watson to advance precision medicine, particularly diagnosis and treatment of cancer. But the above systems were not adopted for clinical practices, they demonstrated promise for accurate diagnoses and treatments. In the past, healthcare decisions were almost entirely made by people and integrating smart intelligent devices and models into the process raises questions about accountability, transparency, consent, and privacy. Healthcare decisions are shifting from exclusive human-centric to semi- or fully smart intelligent machines. This entails bias and ethical concerns. In the literal sense, the computation and cognitive processes of such bias effects are manifest in explicit preconceived ideas (consciously) and assumptions or stereotypes (unconsciously) as well as skewed data insights for a particular segment of class (inadvertently). The objective of the rest of discussion is to perform an analytical investigation for future healthcare informatics.
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Panda, G.K., Sahu, I.K., Sahu, D. (2022). Effect of Computation and Cognitive Bias in Healthcare Intelligence and Pharmacogenomics. In: Tripathy, B.K., Lingras, P., Kar, A.K., Chowdhary, C.L. (eds) Next Generation Healthcare Informatics. Studies in Computational Intelligence, vol 1039. Springer, Singapore. https://doi.org/10.1007/978-981-19-2416-3_4
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