2.1
R&D
Drug discovery and development (e.g. genome sequencing, precision medicine)
Machine learning approaches can help in all stages of drug discovery: target validation, identification of prognostic biomarkers, and analysis of digital pathology data in clinical trials. Adopting data-driven strategies speeds up processes and reduces failures.
Planning of clinical trials (e.g. candidate selection)
Machine learning allows, at its core, to recognize patterns and to find logical rules. Using this tool in the medical field can improve the selection of candidates, form subgroups based on different phases of disease progression, improve the interpretation of data or even speed up the time of approval by the drug administrations.
Drug & device comparative effectiveness
Comparative effectiveness research (CER) has two main goals: to increase the effectiveness of treatments and to reduce their costs. Many AI techniques, including clustering algorithms, can be used to compile lists of similar treatments or medical devices, derive statistics and extract rankings.
2.2
Production
Predictive maintenance
Advanced machine learning models can recommend optimal times to conduct maintenance by analyzing root causes and identifying drivers of machine downtime. Implementing these recommendations will help prevent future asset failures and unplanned downtimes, hence minimizing production losses while maximizing OEE (overall equipment effectiveness).
Demand forecasting
Business planning, budgeting, and goal-setting depend on the demand, which is based on complex factors and hence becomes hard to model manually. The foresight on the demand is, thus, a key strategic point. Using deep-learning-based forecasting techniques can help to improve the procurement strategy, business robustness, inventory management, and resources allocation.
Production capacity optimization (e.g. throughput and yield)
Using data streams from production lines, machine learning can detect bottlenecks as well as improving yield by adjusting parameters at critical stages of the assembly process.
Inventory management
Inventory management is intrinsically linked to demand forecasting. Better managing your inventories through time and anticipating peaks and valleys due to machine learning forecasting techniques, reduces cost and improves operating efficiency.
2.3
Sales & Distribution
Predictive forecasting (e.g. magnitude of disease outbreaks)
It is possible to predict epidemics behaviors, seasonal or not. By using multiple data sources such as clinical studies, population density, or climate information, combined with news stream analysis, it is possible to model and attempt predicting the outbreak, magnitude, and evolution of an epidemic, and thus to adapt strategies.
Drug recommendation engine
By training models based on hundreds of existing diseases and associated treatments, it is possible, from a few keywords about the symptoms of an individual, to determine the cause of the sickness and recommend the most optimal treatment.
Marketing budget allocation
Advanced analytics helps with optimal marketing budget allocation to maximize the return on marketing invest (ROMI). This is done by predicting market development and segment-specific conversion rates of marketing measures.
Sales Force steering (e.g. lead scoring)
Predictive and prescriptive machine-learning algorithms can help organizations find patterns in the way customers respond to different touchpoints, hence determining which actions are more likely to lead to conversion. This set of techniques is referred to as “Next Best Action” (NBA) and will increase the effectiveness of your salesforce.
2.4
Diagnostics & Treatment
Disease identification/ diagnosis
By training models based on hundreds of diseases and associated symptoms, it is possible to diagnose many diseases at high certainty based on a short description of the symptoms. Similarly, machine learning models can learn from thousands of data points from previous patients, anticipate potential side effects, and propose an adequate diagnosis.
Remote patient monitoring
When a patient suffers from a chronic disease, it is sometimes better for well-being purposes to follow him or her remotely. The interactions, done textually or verbally, lead to a data flow that can be processed to extract as much information as possible on the evolution of the disease, thus assisting the medical task force in the follow-up of the patient and extracting the most critical information. Machine Learning boosted chatbot solutions can also be considered, thus entirely freeing up the medical staff until an alert is raised by the chatbot.
Personalized medicine (e.g. generation of unique treatment plans)
Precision medicine requires a detailed patient profile. Based on the patient’s data, various personal risks, and medical conditions, deep learning or classification models can match tailored treatments to patients. The stratification and personalisation processes are both speeded up, and the risk of human errors is highly decreased.
Medical text analysis (e.g. patient records)
The analysis of medical text files can be automated by using machine learning techniques such as Natural Language Processing or Document Capture Technologies. Such techniques can make patient records files faster to analyse, induce information based on what is written on them, group them or even add complementary information on the diseases and conditions quoted.
