“People in standard finance are rational. People in behavioural finance are normal.“Meir Statman, Ph.D. Machine Learning, Santa Clara University
revolutionary work of psychologists Daniel Kahneman and Amos Tversky in the
1970s-1980s, and their research conducted over the last three decades have revealed
striking insights into the intricate ways the human mind operates. This research
identifies prevalent, deep-seeded, subconscious biases and heuristics present
in the human decision-making process, and reveals an entirely new perspective
on why we behave the way we do. This body of work, and subsequent work by other
researchers, represents an entirely new field of endeavour, referred to as
behavioral finance and economics. In this series of blogs, I aim to shed light
on the various investor biases in behavioral finance and explain how Machine
Learning techniques can help to deal with them, effectively.
Continue reading “Understanding Behavioural Finance: A Machine Learning perspective”
Economics incorporates psychological assumptions into the analyses of economic
decision-making. The field of classical economics considers decision-making to
be based on cold logic and hard facts – which human decision-making behavior
does not always adhere to. Behavioral economics allows us to consider
irrational behavior in decision-making and investigates the underlying reasons
for these irrational choices.
Continue reading “Personalized Nudges”
Personalization is disrupting products and services in every sector – be it retail, e-commerce or the quick-service industry. Therefore, it makes good sense to consider personalization in the context of healthcare delivery and services as well. The WHO has promulgated that a 1:1000 doctor-population ratio is desirable. Yet 45% of the 194 member states of the WHO do not meet this criterion; they have less than 1 physician for a population of 1000. With this abysmal doctor-patient ratio, particularly in over-populated and developing third-world countries, healthcare personalization has the potential to bring about real impact and wide-spread health benefits to patients.
Continue reading “Personalization in Healthcare: Diagnosis and Treatment”
Whether it was Ebola yesterday or the coronavirus today, continuous
monitoring and containment of epidemic diseases has been an on-going global challenge.
These are moments when the global healthcare organizations and professionals
actively collaborate to fight the threat to human life.
Continue reading “Combining AI, NLP and Big Data to Monitor and Control the Spread of Epidemic Diseases”
planning, an important aspect of every industry, is the process of determining
the optimal quantity and timing of an organisation’s production and delivery
chain in order to ensure that an organization is able to produce and provide
its goods and services without resulting in unmet demands due to shortage, or
losses due to overstocking. Inventory planning has a different set of
challenges in the healthcare and the pharma sector; often characterized by uncertain
demand and service times. This problem of resource allocation becomes more
complicated due to epidemic diffusion.
Continue reading “Resource Allocation in Epidemic Diffusion”
Historically, the field of
Artificial Intelligence (AI) has gone through several cycles of initial
excitement, intense hype, optimism, and promises of revolution – dubbed as AI
summers; only to be followed by periods of disappointments, aptly named AI winters,
in which expectations failed to materialise, and government and research funds
slowly moved on to other prospects (Chauvet, 2018).
Continue reading “A Deep Learning Approach to Drug Discovery: Small-Molecule Design and Optimization”
Pharmacovigilance (PV) is the “science
and activities relating to the detection, assessment, understanding, and
prevention of adverse effects or any other possible drug-related problems”
(WHO, 2015). PV practices for most cases depend on analysing clinical trials,
biomedical writing, observational examinations, Electronic Health Records
(EHRs), social media and Spontaneous Reporting (SR). Pharmacovigilance plays a
vital role in monitoring the Adverse Drug Reaction (ADR) caused due to single
drug intake, combined dose as well as prolonged administration. ADR has led to
an increase in the mortality rate by 1.8% throughout the world.
Continue reading “Effective Pharmacovigilance Using NLP”