June 2024 MITE Hot Topic: Introduction to Application of Predictive Modeling in Healthcare

Author: Motahareh (Moti) Tavakoli, MSc
Quality Management Engineer


Predictive modeling is an analytical technique that uses statistical and machine learning algorithms to forecast future events by analyzing historical and real-time data. Its application in healthcare marks a transformative shift towards data-driven decision-making, leveraging advanced analytics to improve health outcomes and optimize patient care. Although not a new concept, the use of predictive modeling has surged recently due to advancements in computing power, data collection technologies, and machine learning algorithms. The process of building a predictive model involves three key steps: data collection and pre-processing, model development (training and parameter optimization), and model validation. The data used in this process can range from structured data (e.g., age, lab results) to unstructured data (e.g., text from clinical notes, images).1,2


Predictive modeling encompasses a variety of tasks, serving specific purposes. Let’s delve into some of these tasks, and examples of their applications in healthcare domain.1,2

  1. Classification: Categorizes data into predefined classes or groups, used when outputs are discrete labels.
    – Disease Diagnosis: Predicting whether a patient has a specific disease (e.g., diabetes, cancer) based on symptoms and test results.
    – Patient Readmission Prediction: Classifying patients based on their risk of readmission after discharge from a hospital.
  2. Risk Prediction: Estimates the likelihood of an event occurring in the future, often quantified as a risk score.
    – Cardiovascular Disease Risk: Predicting the 10-year risk of developing cardiovascular disease based on factors like age, cholesterol levels, and smoking status.
    – Hospital-Acquired Infection Risks: Estimating a patient’s risk of acquiring infections during hospital stays.
  3. Anomaly Detection: Identifies data points that deviate significantly from the majority of the data, indicating potential outliers or unusual patterns.
    – Fraud Detection in Insurance Claims: Identifying fraudulent claims based on anomalies in billing patterns.
    – Detecting Unusual Patient Vitals: Monitoring vital signs to detect unusual patterns indicating acute medical events like sepsis.
  4. Time Series Forecasting: Predicts future values based on previously observed values over time, considering trends, seasonality, and patterns.
    – Epidemic Outbreak Predictions: Forecasting the spread of infectious diseases over time to prepare healthcare responses.
    – Demand Forecasting for Hospitals: Predicting future patient admissions to manage resources effectively.
  5. Regression Analysis: Predicts a continuous outcome variable based on one or more predictor variables.
    – Predicting Patient Length of Stay: Estimating how long a patient will stay in the hospital based on their condition and treatments.
    – Drug Dosage Optimization: Predicting the optimal dosage of medication for patients based on efficacy and side effects.
  6. Survival Analysis: Estimates the time until an event of interest occurs, considering the time dimension and censoring.
    – Patient Survival after Surgery: Estimating survival times for patients following surgical procedures.
    – Effectiveness of Cancer Treatments: Assessing the impact of different treatments on patient survival rates.
  7. Recommendation Systems: Predicts user preferences for a set of items (products, services) based on past interactions.
    – Personalized Treatment Recommendations: Recommending personalized treatment plans based on a patient’s medical history.
    – Healthcare Resource Allocation: Suggesting the allocation of resources (e.g., hospital beds, medical equipment) based on predicted needs.
  8. Sentiment Analysis: Determines the sentiment expressed in text data, categorizing it as positive, negative, or neutral.
    – Patient Feedback Analysis: Analyzing patient reviews and feedback to gauge satisfaction and identify areas for improvement.
    – Social Media Monitoring for Public Health: Monitoring public sentiment on social media regarding health policies, outbreaks, and healthcare services.

A real-life healthcare example

The Emergency Department (ED) is considered the frontline of a hospital. One significant challenge faced by EDs is overcrowding, which hampers their ability to provide immediate care and consequently reduces the overall quality of care. A common cause of this issue is when patients have completed their treatment in the ED and are ready to be transferred and admitted but must wait due to a shortage of staffed beds in the inpatient unit. To address this problem, a research project was undertaken to develop an accurate predictive model for forecasting hospitalizations from the ED. This project utilized data from the Maine Medical Center ED visits, which included attributes such as age, sex, Emergency Severity Index (ESI), means of arrival, and ED chief complaints. By implementing this predictive model, hospital decision-makers can better estimate the need for inpatient beds in advance, allowing them to prepare and initiate the bed coordination process while the patient is still in the ED. Several common machine learning algorithms for binary classification tasks were implemented, and the performance of these models was evaluated using metrics such as accuracy, sensitivity, and specificity. The results indicated that the developed Artificial Neural Network model outperformed the other models, achieving a 72% accuracy rate.


Despite its potential, implementing predictive modeling in healthcare faces challenges, including ensuring data quality and privacy, integrating predictive tools into clinical workflows, and addressing ethical considerations around algorithmic bias and decision-making. However, advancements in artificial intelligence and machine learning, coupled with increasing healthcare data digitization, continue to expand its possibilities.2


  1. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., … & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4).
  2. Toma, M., & Wei, O. C. (2023). Predictive modeling in medicine. Encyclopedia, 3(2), 590-601.

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