All healthcare organizations have a large amount of patient data at their disposal. Healthcare providers can use this data to analyze risk factors for many diseases. Risk patterns are metrics that help compare the chances of disease development in one group with a certain characteristic to a control group without the characteristic.
According to this paper, relative risk patterns are "patterns whose local support and relative risk are higher than the user-specified minimum local support and relative risk thresholds, respectively."
Let’s review how medical data mining helps identify these patterns and improve disease treatment.
Mining Risk Patterns: Challenges and Opportunities
Let’s start with the definition of data mining. Data mining involves data processing and analysis of large data sets to discover a risk pattern that can be used to forecast future events. Data mining can identify future symptoms and diseases and detect fraud and abuse. Also, it improves customer relationship management and healthcare services. Al Adamsen of the Talent Strategy Institute noted that the only way to open the potential of data is to implement focused data mining:
“It can’t be a part-time job. Do I have the right development and recruiting strategies in place? With data, we can take a focused, systematic approach. ”
— Al Adamsen, Founder and CEO at Talent Strategy Institute
Usually, data mining consists of two elements:
- The data itself
- Computing power capable of dealing with the data source
Here is what the process of medical data mining looks like:
- Create a target data set from the original data that will be analyzed
- Clean the data from missing data fields
- Find invariant aspects of the data
- Mine the data and classify the data, find new patterns, and predict variables based on the database characteristics
- Interpret the data results and classify the findings of the survival analysis
It’s important to remember that data mining results are only as accurate as the data obtained. That’s why data mining has a lot of challenges in medical application.
- It’s difficult to find reliable and exact data; since hospitals and HCFA are independent agencies, there are no common standards that regulate the data used.
- Organizations are unwilling to share patient data due to privacy concerns.
- Data mining works well to explore risk patterns in large databases. However, it’s important to keep in mind that the sensibility and specificity of the data mining tools can impact the predictive value of gathered information.
How to Define Optimal Pattern Sets
Let’s review the example of defining complicated patterns in a collection of patient records. Each record has several discrete attributes with one target attribute among them. This target attribute has two values: abnormal for the patients with a disease or risk and non-abnormal for other records.
Many of the risk patterns carry no meaning for the user. This happens when the optimal risk pattern set is introduced to exclude superfluous patterns. A risk pattern set is optimal if it includes all risk patterns except those whose relative risks are less than or equal to one of their subpatterns. It’s important to mention that every record in a data set will be covered by a pattern with the highest relative risk. So, the optimal pattern set does not include all patterns but only those with the highest relative risk. Let’s review the example of the discovery of risk patterns in medical data:
- Define the optimal risk pattern set to exclude complicated patterns with lower relative risk than their corresponding simpler form patterns
- Create an anti-monotone property that supports an efficient mining algorithm
- Create an efficient algorithm for mining optimal risk pattern sets and pattern prediction based on this property
- Identify cohorts of patients that are vulnerable to a risk outcome from a large data set
Algorithms Building in Healthcare: How They Work
It is estimated that the healthcare data industry will experience a compound annual growth rate (CAGR) of 36 percent through 2025. Market research has shown that global big data in the healthcare market is expected to reach $34.27 billion by 2022 at a CAGR of 22.07%.
Soon, complex algorithms will help clinicians make incredibly accurate determinations about our health from large amounts of data and correlations between these data. For example, this study shows that it’s possible to de-identify data on hundreds of thousands of patients using a series of machine learning algorithms powered by Google’s massive computing resources. As a result, these algorithms make it possible to predict and diagnose diseases from cardiovascular illnesses to cancer, predict the likelihood of death with 90% accuracy, predict the length of a hospital stay, and risk patterns medical.
One example of AI software that uses algorithms is Buoy—an AI-based symptom and cure checker that uses algorithms to diagnose and treat illnesses. The patient shares the symptoms with a chatbot, gets clinical insight into what’s going on, and is then provided with the best care option.
“We recently developed Triage, a custom software solution for healthcare equipped with an AI-powered voice attendant. The patient is asked to answer a series of questions with the voice prompts' help. Triage supports multiple languages. And its voice-fingerprinting technology can automatically identify patients and locate their records.”
— Vlad Medvedovsky, Founder and CEO at Proxet (ex - Rails Reactor), a software development services company
The key in defining risk patterns in medical data is to constantly monitor how they are used and talk to the health care providers, patients, and administrators to determine if they find any misleading results.
“Software reflects the people who write it, so it’s important to have these fairness issues in mind. At every step of the way, we should check if the algorithm is going to lead to an unfair result. Which bad things unintentionally could happen, and how can we proactively bake in ways to benefit everyone? And that requires careful attention to each step: picking the data, developing the formula, and then deploying the algorithm and monitoring how it is used.”
— Marshall Chin, the Richard Parrillo Family Professor of Healthcare Ethics at the University of Chicago Medicine
Real-world Usage of Medical Algorithms in Healthcare
Let’s review the most common medical algorithms used in healthcare.
Support Vector Machines
This algorithm uses a supervised learning model for classification, regression, and detection of outliers and predicts the medication adherence of heart patients. Support vector machines is also used for protein classification, image segregation, and text categorization.
Bayes Theorem describes the probability of an event based on prior knowledge of conditions that might be related to the event. It is one of the most efficient machine learning algorithms globally, and used for medical data clarification and disease prediction.
This algorithm predicts the current scenario of the categorical dependent variable through the use of predictor variables. It is used to classify and predict the probability of disease risk management to help doctors make more result-oriented decisions. It also helps medical organizations define and target patients with more risk and create corresponding health plans to improve their daily health habits.
With the help of special algorithms, scoring systems—like Apgar for evaluating a newborn’s condition at birth or APACHE for determining the severity of patients in intensive care—help physicians monitor and predict the state of the patient by analyzing metrics such as heart rate, oxygen levels, and neurological reflexes.
Read more about the algorithms in healthcare in our article, “Machine learning unleashes new healthcare opportunities”
Proxet has a team of professionals, which can equip your company with the power of risk prediction for healthcare. We've completed several projects for clients in healthcare, some of which utilized voice recognition. Our team possesses all the necessary skills and experience to satisfy your business needs quickly and efficiently.
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