Predictive Modeling in Healthcare

I have made a programming presentation with codes (python language) how to prepare data for predictive modeling please check it out after you have learnt about the theory behind predictive modeling in healthcare. We will start by knowing why predictive analysis? And also understand how the predictive models work. Before we continue, take an imagination on how change can take on this world, when you actually get medications only for those illnesses you suffering from in the moment? And how wonderful would it be when you receive information only for those relevant health products? And most important ask yourself about the quality of life a humankind would gain only by predicting the most dangerous diseases just by looking at the patterns within the medical records, current symptoms and health historical data. Alright, all these doctors can do, but how efficient do you think they could be?

Daniel Faggella (2018) Machine learning in the healthcare enables mining of high-quality data which can be deep and more accurate, by the use of computers that can learn based on experience, thus bringing potential uses of data in healthcare setting to a more high and really new level. Capabilities of an algorithm to recognize patterns that even the very best doctors in the world would not easily notice, drawing out previously unrevealed correlations which in turn improve the whole practice in medicine and surgery. The algorithms can identify correlations between different sutures used on specific patient injuries and also the likelihood of an infection. These patterns-recognition communicate potential health and medical problems at an individual level amongst patients with reference to before the actual occurrence and manifestation of the problem.

Simple definition: Predictive Modelling is a strategy that makes utilization of mathematical and computational methods to foresee an occasion or result. A mathematical approach utilizes a condition based model that depicts the phenomenon of under thought. The model is utilized to figure a result at some future state or time in view of changes to the model data sources. The model parameters help clarify how model inputs impact the result. Use cases for healthcare predictive analytics

Predict chronic diseases and maintain population health

The use of predictive modeling to proactively identify patients who are at highest risk of poor health outcomes and will benefit most from intervention is one solution believed to improve risk management for providers transitioning to value-based payment. Learn about a 9 layer deep convolution neural network (CNN) that was developed to monitor the activity of the heart.

Health systems and hospital incur high costs and insufficient resources due to unplanned returns of patients. By improving transitions of care and deploying care coordination strategies, with predictive analytics care providers receive a warning about an event where a patient’s risk factors indicate a high likelihood for readmission of a particular patient within a window of 30-days. predicting patients traits that may produce a high impact on the likelihood of readmission, these are can quickly be identified and can give care providers an extra indication especially on when to focus resources towards follow-up and how to design discharge planning protocols to prevent speedy returns to the hospital.

Preventing suicide and predicting patient self-harm

An early identification of patients or individuals that are likely to cause a harm to their life could really ensure quick alerts and held these patients get the required mental healthcare or medical attention needed as soon as possible avoiding serious events to happen, including self harm or suicide incidents.

Prediction analysis process in healthcare

Steps to predictive Modeling: Before actually we develop any model a lot of important things have to be put into consideration in order to successfully implement the model, lets look at this process and capture a clear understanding:

Analyze Results

Prepare data for Analysis

Define objectives

Monitor performance

Deploy Models

Develop models

Here we determine if the objectives are met. Have knowledge of the clinical or medicine project and set the specific goals. Load the dataset, explore the data, clean it, transform data and visualize data click for more info about visualization. Choose which machine learning models to be used. Determine which tools are optimal. Type of models would include linear models, decision tree neural networks. Apply the models and train the model on the selected datasets. Monitor the performance of the models. For example how the models perform on the during training and also when applied to test datasets or on real world data how predictive modeling works in a data science project. If some steps are not properly done, this may affect the functionality of our model and as well as predictions or we may not find out the interesting patterns as expected.

First we need to clearly understand our business, clinical or any medicine project objectives by asking the right questions. The business or project managers always want to answer all these important questions and make the right decisions basing on the data. Then we need to translate these business or project objective(s) into real analytical goals basing on our business or project questions we are trying to answer at this moment.

Diagrammatic Process preview of predictive modeling

Explanation: Taking an example of a population dataset, first we divide our dataset into two that is Training and Test datasets this will allow us to train our model to learn from our data in order to look up insights and the test sample will help us to test our models with a different dataset in order to find out and determine the accuracy of our model when used on totally different dataset, this will help us to find out if our model is over-fit or under-fit which is very important when it comes to model validations, a model might work very well on training data but might actually under perform when it happens to be tested on a different dataset. Note: Test dataset is always at least one row less than the training dataset refer to the percentages on the diagram above how to divide the dataset. Try out different models and make sure to do a cross-validation and choose the most performing model with high accuracy and lower error counts.

Model complexities: You should be able to identify some of the model complexities as they can very much affect the effectiveness of your model predictions, that is to say, take count of when a model is over-fitting, under-fitting, generalization errors and also the validation for model selection. Under-fitting occurs when the model is of a low dimension, heavily regularized and also in case of a bad modeling assumption may lead your model to under-fit. Over-fitting on the other hand occur in presence of a high dimensional or a non parametric model, weakly regularized, not enough modeling assumptions or even not enough data.