Understanding Machine Learning: Introduction
Machine learning (ML) is a form of artificial intelligence (AI) and computer science that uses data and algorithms to mimic how humans learn and improve its accuracy over time. Machine learning is an essential factor in the evolving work in data science.
Using machine learning models, algorithms can be trained to make predictions that can reveal key insights into the collected data. These insights can help drive mindful decision-making in the healthcare system, improving patient outcomes and adding clinical benefit.
The Role of Machine Learning in Personalized Treatment
Personalized medicine is a growing field with the goal of building medical treatments that are personalized to the patient’s unique needs, including their lifestyle, condition, and living environment. Healthcare providers are using machine learning algorithms to analyze data gathered by sensors to help with personalized treatment selection.
The sensors used to gather patient data for ML algorithms can collect vital signs, activity levels, and sleep patterns. They can be implanted within the patient’s body but will typically be worn devices that can consistently monitor the patient’s condition. Using sensors to build a personalized treatment plan will provide patients with more accurate care and lead to improved outcomes.
Machine learning algorithms can analyze more than just data gathered by sensors. ML algorithms can also be trained using the data stored in electronic health records to account for the patient’s medical history. By using both current and historical data, the treatment plan will be able to consider more than the patient’s current condition.
Machine Learning Tools for Precision Medicine
Knowing how machine learning models and algorithms can be applied to build personalized medicine, let’s take a look at the different machine learning tools that can be utilized to improve precision medicine applications.
Classification
Classification is the process of grouping objects into preset categories through the process of recognition and understanding. By categorizing training data, machine learning models can utilize algorithms to classify any future datasets into the correct categories.
Classification is a type of pattern recognition where algorithms are applied to clinical data to identify patterns in future datasets. For example, it could be looking for specific words or numbers in a patient’s history to classify them as high risk for certain conditions.
Regression
Regression is a supervised learning technique that identifies any correlations between the dependent and independent variables. Utilizing fold cross-validation will give developers an error rate prediction, allowing them to make any necessary changes to the model. A regression model can help predict continuous variables, such as a drug response or reaction.
Clustering
Clustering is a type of unsupervised learning method that examines references from datasets based on data without labeled responses. This process is typically used to find structure, inherent groupings, and underlying processes. Clustering can analyze a dataset and identify and group similar cases within the dataset to be further examined.
Challenges of Applying Machine Learning in Personalized Medicine
While using machine learning models to provide patients with personalized medical care sounds ideal, there are some challenges that healthcare providers can face. Let’s take a look at three of the most common challenges that come with machine learning models in personalized medicine.
Data Quality
Since machine learning and artificial intelligence rely so heavily on data, the accuracy and performance of the machine learning model depend on the quality of the training data. The data needs to have a large enough sample size to represent the varied patient population that seeks out clinical care.
Machine learning models that are trained on insufficient data can be riddled with missing information or biases. Using a small sample size can lead to the ML model over-relying on characteristics in the data and losing the ability to perform in a clinical practice accurately.
Ethical Concerns
The increased use of machine learning in personalized medicine has brought along a slew of ethical concerns regarding data privacy. Large datasets can include sensitive patient information that is stored and used, like genetic and historical patient data. To ensure the privacy of individual patients, the data needs to be anonymized, and remove any data that could be used for identifying patients.
Discrimination in machine learning models has been an ongoing concern in its applications, including race, ethnicity, and gender. Biases in artificial intelligence stem from incomplete datasets being used to train models, making them less effective in populations with different circumstances or disadvantages. To reduce the chance of biases in machine learning models, both the development team and datasets should be diverse to identify any underlying biases in the models properly.
Implementing Models in a Clinical Setting
Taking a model and effectively implementing it into clinical practice to improve patient outcomes and provide patients with an optimal treatment response can be a challenge. Before machine learning models can have clinical applications, they must be closely examined and evaluated to measure their potential effectiveness.
A typical challenge to encounter when implementing ML models into clinical practice is when they assign patients with diagnoses that lack effective drugs or treatments. When the intention of using machine learning models in a clinical setting is to offer patients personalized treatment, the results must be accurate and offer healthcare providers insights into potential treatment outcomes.
Improving Personalized Medicine with Machine Learning
Artificial intelligence and machine learning algorithms can be utilized to analyze large datasets and identify patterns that could indicate health issues. Machine learning models can analyze the electronic health records of an individual patient and identify data points that could indicate risk factors or symptoms of diseases. This can help lead healthcare providers to the patient’s diagnosis and build a personalized treatment plan.
Machine learning in personalized medicine can improve diagnostic accuracy since it can analyze a large amount of data quicker than a human, leading to a more timely diagnosis. Since ML models can be scaled to analyze larger datasets than humans, they can typically provide a more comprehensive analysis of the patient’s condition, leading to improved treatment outcomes.
Machine Learning and the Future of Healthcare
The healthcare industry is a constantly evolving field that is becoming increasingly more specialized as new developments and research are uncovered. Emerging technology has long held a place in the healthcare industry, and we will start to see artificial intelligence and machine learning incorporated into healthcare practices as they become more developed.
It’s important to remember that the goal of machine learning models in a clinical setting isn’t to replace healthcare professionals but rather serve as a tool to improve diagnostic speed and accuracy. Machine learning and artificial intelligence can be utilized as tools to support findings and diagnoses, but healthcare providers should always be the ones leading clinical decision-making.
Is your healthcare organization looking to incorporate machine learning models into its practice to support healthcare providers? Reach out to Geneca today to learn more about how we can support your organization and build a well-trained model to offer patients a personalized treatment plan.