Amazon knows which items people like to buy when and where..

Netflix knows which films and series people prefer to watch..

Google knows which symptoms and conditions people are searching for..

 These data points can be used for very detailed personal profiling, which may be of great value for behavioural understanding but also has potential for predicting healthcare trends. There is great optimism that the application of Artificial Intelligence (AI) can provide substantial improvements in all areas of healthcare ranging from medicine, pharmacy, audiology, psychology, occupational therapy, physical therapy, athletic training..overall from diagnostics to treatment.

In this new series of articles, I will be sharing various applications of Artificial Intelligence across various domains of healthcare. So, let’s start:

Precision Medicine

Precision medicine provides the possibility of tailoring healthcare interventions to individuals or groups of patients based on their disease profile, diagnostic or prognostic information, or their treatment response. The tailor-made treatment opportunity will take into consideration the genomic variations as well as contributing factors of medical treatment such as age, gender, geography, race, family history, immune profile, metabolic profile, microbiome, and environment vulnerability.

The objective of precision medicine is to use individual biology rather than population biology at all stages of a patient’s medical journey. This means collecting data from individuals such as genetic information, physiological monitoring data, or EMR data and tailoring their treatment based on advanced models. Machine learning analysis of precision medicine’s multi-modal data allows for broad analysis of large datasets and ultimately a greater understanding of human health and disease.

Machine learning analysis of precision medicine’s multi-modal data allows for broad analysis of large datasets and ultimately a greater understanding of human health and disease.

Digital health applications

Healthcare apps record and process data added by patients such as food intake, emotional state or activity, and health monitoring data from wearables, mobile sensors, and the likes. Some of these apps fall under precision medicine and use machine learning algorithms to find trends in the data and make better predictions and give personalized treatment advice.

Genetics-based solutions

It is believed that within the next decade a large part of the global population will be offered full genome sequencing either at birth or in adult life. Such genome sequencing is estimated to take up 100–150 GB of data and will allow a great tool for precision medicine. Interfacing the genomic and phenotype information is still ongoing.

Healthtech companies are today looking at identifying patterns in the vast genetic dataset as well as EMRs, in order to link the two with regard to disease markers. This is made possible by using correlations to identify therapeutics targets, either existing therapeutic targets or new therapeutic candidates with the purpose of developing individualised genetic medicines. ML algorithms are used in every step of drug discovery and development process including target discovery, lead optimization, toxicity assessment, and innovative trial design.

Drug discovery and development

Drug discovery and development is an immensely long, costly, and complex process that can often take more than 10 years from identification of molecular targets until a drug product is approved and marketed. Any failure during this process has a large financial impact, and in fact most drug candidates fail sometime during development and never make it onto the market. On top of that are the ever-increasing regulatory obstacles and the difficulties in continuously discovering drug molecules that are substantially better than what is currently marketed. This makes the drug innovation process both challenging and inefficient with a high price tag on any new drug products that make it onto the market.

There has been a substantial increase in the amount of data available assessing drug compound activity and biomedical data in the past few years. This is due to the increasing automation and the introduction of new experimental techniques including hidden Markov model based text to speech synthesis and parallel synthesis.

However, mining of the large-scale chemistry data is needed to efficiently classify potential drug compounds and machine learning techniques have shown great potential. Methods such as support vector machines, neural networks, and random forest have all been used to develop models to aid drug discovery since the 1990s.

More recently, Deep Learning has begun to be implemented due to the increased amount of data and the continuous improvements in computing power. There are various tasks in the drug discovery process where machine learning can be used to streamline the tasks. This includes drug compound property and activity prediction, de novo design of drug compounds, drug–receptor interactions, and drug reaction prediction.

More in next…

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