Predictive Modeling in Healthcare: Steps for Accurate Forecasting
Now, let’s move on directly to the predictive modeling process. One does not simply make predictions without appropriate and thorough preparation, especially if you work with big data. Variables must be found, processed, and validated to be suitable for further usage.
Here’s a brief overview of the stages we go through before we leverage our healthcare data for forecasting.
1. Data Sources Definition
Depending on the goal we pursue when using predictive analytics in healthcare, we determine the necessary sources from which we’ll extract our data. For example, if we need to predict the level of flu cases for the next season, we may require a register containing information on the incidence of the population in past years. This contains general information on incidence, not just flu, EHRs, as well as metrics from medical equipment analyses.
2. Data Modeling
At this stage, we are finalizing the requirements for the ETL process and the prediction itself. In other words, we thoroughly work out the selected sources, choose the columns we’ll work with, and identify only the data that we need. In this particular case, we select only information on flu incidence and the results of examinations of people with this diagnosis.
It’s important to understand that modeling is an iterative process, and we can revisit it at any subsequent stage. This could be triggered by the emergence of new data or the discovery of inaccurately provided data.
Read about the Top Data Modeling Techniques
3. Extract, Transform, Load
During the ETL stage, we proceed directly to raw health data extraction from the required sources, processing and filtering, and loading into our chosen storage for subsequent prediction-building. The quality of the process and the built data architecture will determine the system’s forecasting capabilities. That’s why this step is of paramount importance.
4. Data Validation
This stage involves verifying the data already loaded into the storage. We check the quality of the transformed data, its consistency, and whether it corresponds to the intervals of acceptable values.
5. Data Enrichment
At the enrichment stage, we have the opportunity to expand our dataset by adding extra columns. This becomes possible through the use of special tools, such as LLM (Large Language Model). For example, a doctor leaves notes after a patient’s visit. An LLM model is capable of analyzing handwritten text and providing an assessment of the patient’s condition in the range of 0-10, and this value can be used in the prediction itself.
6. Testing
The validation stage entails only checking the data format, whereas testing helps verify the entire flow. For example, we added new data to the ETL process and thereby slightly altered it. In such a case, tests that previously had worked successfully may have failed, which indicated that something went awry in the flow itself.
ML Model Training & Prediction Building
After we’ve prepared our healthcare data and made sure that they are clean, consistent, and suitable for further usage, we may start to select the machine learning model, train it on them, and build predictions. Below, there is a table of major steps for prediction building.
|
Step
|
Description
|
| ML Model Selection |
Choose the right machine learning model based on the data type, desired outcome, and complexity. Options include Linear Regression for simple predictions, Decision Trees for non-linear data, and Neural Networks for complex patterns like image recognition. |
| Data Preprocessing |
Prepare data by normalizing numeric inputs, imputing missing values, and encoding categorical data to ensure it is in a format suitable for modeling. |
| Training the ML Model |
Adjust model parameters through cross-validation and regularization to prevent overfitting and ensure it performs well on new data. |
| Prediction Building |
Generate predictions using the trained model on new data, apply appropriate thresholds for binary outcomes, and evaluate probabilistic outputs for decision-making under uncertainty. |
| Model Evaluation |
Use accuracy, precision, recall, ROC-AUC, and F1 Score to evaluate the model’s performance and ensure its reliability for healthcare applications. |
| Continuous Learning and Model Updating |
Incorporate new data through online learning or apply transfer learning to keep the model updated and relevant for current medical challenges. |
Forecast Precision in Danger: Main Risks of Predictive Analytics in Healthcare
As always, it’s easier said than done. All the steps of predictive analytics in healthcare mentioned above sound quite simple, but it’s just in words. There are so many factors that may affect the accuracy of predictions, starting from the engineer’s ineptitude and ending with business rules alterations.
And while the future of predictive analytics tends to be quite promising in healthcare, this opportunity can only be realized by being prepared to address potential critical issues. Below, let’s take a look at some of the most common pitfalls and the best possible solutions to overcome them.
Poor Raw Data Quality & Heterogeneity
Problem
Let’s explore the example of medical equipment for blood testing. A sensor that detects hemoglobin levels had malfunctioned, which was not immediately detected by the laboratory staff. Consequently, due to the faulty equipment, we receive incorrect results, which are then loaded into the database. Thus, since our source data are of low quality, it’s quite naive to expect accurate predictions on their basis.
Solution
If the equipment returns extreme values, for example, 0 or 1000, which is completely unacceptable when we are talking about hemoglobin levels, then we can easily detect such anomalies at the validation stage and conduct filtering immediately.
Of course, it’s a bit more complicated if the equipment returns results within the normal range. For example, over the past week, the hemoglobin levels of all patients were 139 and 140, which are in the normal range, but the equipment did not return any other values at all. In this case, we can use Artificial Intelligence to power an anomaly detector, which will help identify points that don’t fall into the statistical norm range for this sample.
Alterations in Initial Sources
Problem
Let’s continue discussing the hemoglobin indicator with the following example. Say, in the database containing equipment analysis results, the firmware was updated. As a result, the column that was previously called hemoglobin was transformed into Hemoglobin, meaning that the lowercase letter was changed to uppercase. The ETL, expecting the column name “hemoglobin”, cannot find the necessary column in the source and logically fails to process this data further.
Solution
To detect such discrepancies promptly, it’s necessary to set up alerts that will notify us if an error occurs. In our ETL pipeline, we will also need to change the column name so that the process continues smoothly and without a glitch.
Data Privacy and Security
Problem
Given the huge datasets healthcare organizations process, they are a tempting target for cybercriminals. Overall, clinics collect these variables from multiple sources, such as appointment and scheduling systems, electronic health records, and wearable devices, which makes them particularly vulnerable.
Solution
The best way for providers to protect their systems is to employ strong cybersecurity measures like data encryption, strong authentication, and role-based access. Plus, conduct regular penetration tests to spot any possible vulnerabilities.
Additionally, it is worth leveraging a blockchain-based intrusion detection system to double down on data protection. You see, blockchain provides tamper-proof security logs and decentralized verification, making it possible to identify suspicious activities.
Find more compelling reasons making Blockchain a Treasure Trove for Healthcare
