A guide to anomaly detection in health care with machine learning

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Imagine a patient in the ICU where their vitals are carefully monitored. Occasionally, subtle heart rate or oxygen levels may occur—small shifts that could be easily overlooked. However, an advanced machine learning model detects these changes and anomalies and promptly alerts the medical team, enabling timely intervention and support.

This isn’t science fiction anymore; it’s happening today. Anomaly detection is revolutionizing health care, particularly in areas where timely intervention can mean the difference between life and death. Machine learning (ML) is at the core of this transformation, allowing us to detect early signs of such medical emergencies by analyzing complex health care data in real time.

What is anomaly detection in health care?

Anomaly detection in healthcare involves spotting unusual patterns in patients’ medical data that could indicate a health issue or potential disease outbreak. Machine learning can quickly analyze large amounts of data and pick up on even the tiniest anomaly.

With wearable devices now widely adopted, vast amounts of data are continuously generated—from heart rate to oxygen levels to body balance statistics. Detecting anomalies in this data can lead to timely interventions, preventing critical conditions like strokes or cardiac arrests.

Is traditional health care not enough?#

Traditional health care relies on human experts to interpret lab results, sensor data, and imaging scans. But as you can tell, the huge volume of such data, its variety, and complexity soon become a challenge. It’s hard to detect anomalies manually because, for the human eye, the patterns are often subtle and buried under thousands of readings. Therefore, traditional health care demands experts in large numbers, making it costly and logistically challenging.

Machine learning is the way to anomaly detection#

Machine learning changes the game by automatically learning from data without requiring explicit programming every single time. Moreover, ML can process vast amounts of complex data, identify subtle changes, and adapt over time. Whether detecting an outlier in blood pressure or identifying a rare condition based on symptoms, ML can work at scale and speed.

What is the best algorithm for anomaly detection?#

Various ML algorithms are used for anomaly detection, each with strengths and weaknesses. There is no single best algorithm; it depends on the dataset and the problem you’re solving. Let’s look at the different parameters that affect the selection of the best algorithm for health care anomaly detection.

Determining which algorithm to use is overwhelming, especially when the stakes are high. But don’t worry—we’ll guide you by breaking things down so you can feel confident selecting the right algorithm for anomaly detection in your work.

The role of dataset type and complexity#

Any machine learning model is highly impacted by the data it is trained on. Therefore, it is necessary that the data is of high quality and highly relevant to the problem domain to ensure accurate and reliable results.

There are several commonly encountered data-related scenarios. Let’s discuss these scenarios and how to handle them.

1. Small and well-structured dataset:#

Small, clean datasets often result in overfitting, as the model might memorize patterns rather than generalize. Simpler algorithms like k-means clustering or isolation forest are well-suited for such datasets, especially for vital sign data like pulse or respiration rate.

2. Complex and unstructured datasets:#

Large datasets, such as medical imaging or genomic data, require significant resources. Advanced methods like convolutional neural networks (CNNs) can identify subtle patterns and handle high-dimensional data in these cases.

For example, detecting cellular level abnormalities in medical images with machine learning requires more complex supervised models that can learn from millions of labeled images.

3. Real-time datasets:#

Real-time datasets probe the challenge of handling and processing large amounts of continuous data, thus requiring efficient resources for quick processing. If the dataset consists of real-time data streams (like continuous patient monitoring in the ICU), algorithms that can operate efficiently in a time-series context, such as recurrent neural networks (RNNs) or long short-term memory (LSTM) networks, are ideal. These are highly complex datasets where temporal relationships need to be learned.

4. Highly imbalanced datasets:#

A highly imbalanced health care dataset can result in a biased machine learning model that might fail to predict or identify critical and rare cases. Health care datasets are usually highly imbalanced, meaning that normal data points far outnumber the anomalies (e.g., normal heartbeats vs. irregular heartbeats). In such cases, effective data preprocessing or methods like SMOTE (Synthetic minority oversampling technique) can handle the complexity by learning to deal with the rare class (anomalies).

Real-life examples of detecting health care anomalies with ML#

With the appropriate ML model chosen based on data structure and complexity, let’s see how these techniques apply in real health care scenarios.

Example: Predict patient falls#

Take, for example, an anomaly detection system used to predict patient falls in a hospital. Using sensor data and machine learning, the system identifies subtle changes in a patient’s balance, alerting caregivers before a fall happens.

To create a model for this, follow these steps:

1. Data preparation: Clean and normalize your sensor data to ensure it is ready for training.

2. Data splitting: Split your data into training, validation, and test sets.

3. Model selection: Based on the problem statement and the complexity of the data, choose anomaly detection models like isolation forests, autoencoders, recurrent neural networks (RNNs), etc., using scikit-learn, TensorFlow, or Keras.

4. Model training: Train your model on the training set.

5. Model evaluation: Evaluate using precision, recall, F1-score, and ROC-AUC metrics on the validation set.

6. Hyperparameter tuning: Adjust hyperparameters like learning rate and number of layers to improve performance.

7. Testing: Test the final model on the test set to assess its performance on unseen data.

And voila! Your model is ready to be deployed.

Example: Detect irregular heartbeat#

Imagine building a system to spot heart arrhythmias now that you are a pro. By training a machine learning model on thousands of EKG readings, you could catch irregular heartbeats early and alert doctors to potential risks, like heart attacks. This kind of detection can save lives by providing early warnings. Building a real-time model involves streaming data from wearable devices and using edge computing to process data efficiently.

The future of anomaly detection in health care with ML#

The future of health care lies in predictive and personalized medicine. Anomaly detection can evolve into proactive care, identifying potential health issues before they occur. For example, machine learning models could analyze patient data and predict conditions based on historical trends, providing personalized treatments based on detected anomalies. Explainable AI (XAI) is also something worth exploring. It ensures that these models are interpretable and provide insights into the root causes of detected anomalies.

Overall, the potential of machine learning in health care goes beyond just code and data. It’s about making a real difference in someone’s life; knowing what we’re building today could help save a life tomorrow.