As healthcare and pharma professionals, we deal with vast amounts of data on a daily basis. From patient records to clinical trials, data analysis plays a crucial role in our decision-making process.
Two important concepts in the field of data analysis are statistical inference and machine learning. While both involve the use of statistical methods to analyze data, there are some key differences between the two.
Statistical inference is a process of drawing conclusions about a population based on a sample of data. It involves using statistical methods to estimate population parameters, such as the mean or standard deviation, based on sample data. Statistical inference is often used in scientific research to test hypotheses and make predictions about future events.
Machine learning, on the other hand, is a type of artificial intelligence that involves the use of algorithms to learn patterns in data. Machine learning algorithms are designed to automatically improve their performance over time by learning from new data.
One of the key differences between statistical inference and machine learning is the focus of analysis. Statistical inference is primarily concerned with understanding the underlying population from which a sample is drawn, particularly as part of the scientific research community, while machine learning is focused on making predictions or identifying patterns within large volumes of data.
Another difference is the level of human involvement required. Statistical inference typically requires a human analyst to design the study, collect the data, and interpret the results. Machine learning, on the other hand, can be fully automated, with algorithms learning from data without any human intervention.
Finally, the types of data that are analyzed also differ between statistical inference and machine learning. Statistical inference typically involves analyzing structured data, such as numerical data collected through surveys or experiments. Machine learning, on the other hand, can analyze both structured and unstructured data, such as text or images.
So, what really are the key differences?
These are some opinions from the scientific community:
Prediction is at the heart of almost every scientific discipline, and the study of generalization (prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, while statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal: prediction from data. (1)
In this point of view, the difference is just cultural. See the comparison table from Professor Rob Tibshirani (2)
Others find them complementary:
Statistics requires us to choose a model that incorporates our knowledge of the system, and ML requires us to choose a predictive algorithm by relying on its empirical capabilities. Justification for an inference model typically rests on whether we feel it adequately captures the essence of the system. The choice of pattern-learning algorithms often depends on measures of past performance in similar scenarios. Inference and ML are complementary in pointing us to meaningful conclusions. (3)
And here are some examples of the most common uses of both concepts in Pharma and Healthcare:
Statistical Inference Examples
In Pharma and Life Sciences
A clinical trial is conducted to determine the effectiveness of a new drug in treating a specific disease. Statistical inference can be used to analyze the data and draw conclusions about the drug's efficacy.
Hypothesis testing: Statistical inference can be used to test hypotheses about the effectiveness of a new drug or treatment. For example, a clinical trial may test whether a new drug is more effective than an existing drug in treating a particular condition.
Sample size determination: Statistical inference can be used to determine the appropriate sample size for a clinical trial. This can help ensure that the trial has enough statistical power to detect meaningful differences between treatment groups.
Randomization: Statistical inference can be used to randomize participants into treatment groups in a clinical trial. This can help ensure that the treatment groups are comparable and that any differences in outcomes are due to the treatment and not other factors.
Subgroup analysis: Statistical inference can be used to analyze subgroups of participants in a clinical trial. This can help researchers determine whether the treatment is more effective for certain groups of patients, such as those with a particular genetic profile or disease severity.
In Clinical/Healthcare Services
A hospital wants to determine if there is a significant difference in the length of stay for patients with different types of insurance. Statistical inference can be used to analyze the data and determine if there is a significant difference.
Disease surveillance: Statistical inference is used to monitor the spread of diseases and predict future outbreaks. Public health officials use statistical models to analyze data on disease incidence and prevalence to identify trends and patterns.
Risk assessment: Statistical inference is used to assess the risk of developing certain diseases or conditions. For example, researchers may use statistical models to analyze data on lifestyle factors and genetic predisposition to determine an individual's risk of developing heart disease.
Machine Learning Examples
In Pharma and Life Sciences
Patient Selection: Machine learning algorithms can be used to analyze patient data and identify the most suitable candidates for clinical trials. This can help to improve the efficiency of the trial and reduce the number of patients needed to achieve statistically significant results.
Predictive Modeling: Machine learning can be used to develop predictive models that can help to identify patients who are at risk of developing adverse events during the trial. This can help to improve patient safety and reduce the risk of trial failure.
Drug Development: Machine learning can be used to analyze molecular data and identify potential drug targets. This can help to accelerate the drug development process and improve the success rate of clinical trials.
Patient Monitoring: Machine learning algorithms can be used to monitor patient data in real-time during clinical trials. This can help to identify early warning signs of adverse events and allow for prompt intervention to improve patient outcomes.
In Clinical/Healthcare Services
Predicting patient readmission rates based on their medical history and demographic information. Machine learning algorithms can be used to analyze large amounts of data and identify patterns that can be used to predict readmission rates.
Identifying patients at risk of developing a specific disease based on their genetic information. Machine learning algorithms can be used to analyze genetic data and identify patterns that can be used to predict disease risk.
Predictive analytics: Machine learning algorithms can be used to analyze patient data and predict the likelihood of certain health conditions or diseases. This can help healthcare providers to identify high-risk patients and provide early interventions to prevent or manage the condition.
Medical imaging analysis: Machine learning algorithms can be used to analyze medical images such as X-rays, CT scans, and MRI scans. This can help healthcare providers to identify abnormalities and diagnose conditions more accurately.
Personalized medicine: Machine learning algorithms can be used to analyze patient data and develop personalized treatment plans based on individual patient characteristics. This can help healthcare providers to provide more effective and targeted treatments.
Drug discovery: Machine learning algorithms can be used to analyze large datasets and identify potential drug candidates. This can help pharmaceutical companies to develop new drugs more quickly and efficiently.
Patient monitoring: Machine learning algorithms can be used to monitor patient data in real-time and alert healthcare providers to potential issues. This can help providers to intervene quickly and prevent complications.
The Bottom Line: understanding the differences between statistical inference and machine learning can help pharma and healthcare professionals select the most appropriate method for their specific needs and ensure the most accurate outcomes.
At Equilibrium Point, we combine the best of both worlds to improve our understanding of healthcare data and make more informed decisions for our customers and their patients.
Next up on the EQP blog: And what about the uses of the shiny prompt-based AI (ChatGPT) in healthcare?
References:
(1)Appearing in Proceedings of the 29th International Conference on Machine Learning, Edinburgh, Scotland, UK, 2012. Copyright 2012 by the author(s)/owner(s).