Healthcare Analytics Archeron White Paper

8/15/2019 Healthcare Analytics Archeron White Paper

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Predictive analytics in Healthcare

Predictive analytics (PA) uses technology and statistical methods to search through massive

amounts of information, analyzing it to predict outcomes for individual patients. That

information can include data from past treatment outcomes as well as the latest medical

research published in peer-reviewed journals and databases.

In medicine, predictions can range from responses to medications to hospital readmission rates.

Examples are predicting infections from methods of suturing, determining the likelihood of

disease, helping a physician with a diagnosis, and even predicting future wellness.

Prediction modelling uses techniques such as artificial intelligence to create a prediction profile

(algorithm) from past individuals. Experts use predictive analysis in health care primarily to

determine which patients are at risk of developing certain conditions, like diabetes, asthma,

heart disease, and other lifetime illnesses. Additionally, sophisticated clinical decision support

systems incorporate predictive analytics to support medical decision making at the point of

care.

Our programmes built on various predictive models shall follow standards of HL 7. Health

Level-7 or HL7 refers to a set of international standards for transfer of clinical and

administrative data between software applications used by various healthcare providers. These

standards focus on the application layer, which is "layer 7" in the OSI model. The HL7 standards

are produced by the Health Level Seven International, an international standards organization,

and are adopted by other standards issuing bodies such as American National Standards

Institute and International Organization for Standardization. (Source : Wikipedia)

Through the following predictive model, Archeron shall be aiming at envisaging new and

innovated technology using Artificial Intelligence.

1. Linear Regression Model

The focus lies on establishing a mathematical equation as a model to represent the interactions

between the different variables in consideration. The following studies have been undertaken in

the healthcare sector using the Linear Regression Model:

• Analyze medical expenditure of individuals aged 65 years and older w ho qualify for health

care under the Medicare program


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• Building a model that predicts an individual’s cost to an insurer and determining future

healthcare costs using prior costs, demographics

Using the Linear Regression Model we will be innovating programme that will assess the

heartbeat and chest movement and using neural net KNN classifier video camera the chest

movement and the heart beat movement will be visualized to the healthcare practitioners for

better monitoring and ailing.

2. Discrete Choice Model

Discrete choice models allow researchers to analyze and predict how people's choices are

influenced by their personal characteristics and by the alternatives available to them. The

following studies have been undertaken in the healthcare sector using the Discrete Choice

Model:

• Areas as transport modelling, examining consumer responsiveness to advertising

• Population preference over different screening strategies for cancer screening programmes

Using the Discrete Choice Model, we will be innovating a User Choice Bucket wherein User

Behaviour Pattern will be recorded through data sets from Hospitals and respective analytical

tools shall be programmed. The results shall be viewed in graphics which will help the

healthcare clinics serve the populous in a more efficient faishon.

3. Log model

Logistic regression is typically applied to binary outcome variables for which the product

binomial probability distribution can be assumed. The following studies have been undertaken

in the healthcare sector using the Log Model:

• Quantal bioassay experiments in which the model specifies a dose-response relationship and

epidemiologic studies concerned with certain health measures such as the proportion of people

seeking medical care.

• A Logistic Regression Model to Predict High Risk Patients to Fail in Tuberculosis Treatment

Course Completion.

Using the Log Model, we will be innovating a programme that will assess the probability of Re-

Admission of a patient after treatment.

4.

Multinominal Regression


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This model deals with one nominal/ordinal response variable that has more than two

categories, whether nominal or ordinal variable. The following studies have been undertaken in

the healthcare sector using the Multinomial Regression:

• The proposed method is applied to a study on risk factors for heart attack and stroke in 2009

U.S. nationwide Behavioral Risk Factor Surveillance System (BRFSS) data.

• A Multinomial Logistic Regression Analysis to Study the Influence of Residence And Socio -

Economic Status On Breast Cancer Incidences.

Using the Multinominal Regression we will be innovating a programme that will assess the

probability of BP going too high or too low, Heart attacks and Stroke through EEG. This

predictive model shall also be used in establishing correlation within user profiles in hospital so

as to segregating the user choice as per socio economic parameters. Each segregated category

will be curtained with constants for heart attack probability. For instance one of the segregated

category will be ‘Urban male’ with ‘50+’ age and further sub categorized by BMI height to check

user probability of heart attack.

5. Probit Regression

We explore how alternative estimation models perform in our dataset, concluding that, rather

than an ordered probit model, a grouped data regression approach turns out to be empirically

more adequate. The following study has been undertaken in the healthcare sector using the

Probit Regression Model:

• To estimate a model in which socio-economic characteristics, along with health-related

attitudes and behaviour predict levels of drinking

Using Probit Regression model we will be innovating a programme that will manage the

Hospital patient files and assess Behaviour models along with categorizing them as a product of

probability of future negative behaviour.

6. Time series model

A time series is a sequence of observations made over time. The approach usually involves

constructing a time series of population-level rates for a particular quality improvement focus.

The following studies have been undertaken in the healthcare sector using the Time Series

Model:

• Comparing one time period to another time period – for example, evaluating the impact of asmoking cessation programme by comparing smoking rates before and after the event


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• Comparing one geographical area or population to another

• Making future projections – for example to aid the planning of healthcare services by

estimating likely resource requirements

• Forecasting individual hospital waiting lists, as well as treatments/surgery specific waiting

lists

Using Time Series Model we will be innovating a program that will monitor a patient past

healing from heart attack by assessing heart beats through bio sensor data (HomeSkan is a

product of a leading Healthcare equipment Manufacturing firm from India, Skanray. This

product shall be collaborated with this technology.)

Through this model, a time series band boundary shall be programmed for each user with thepast history of BP. So that if any day the BP of a patient is outside 15% of past 6 months data, an

auto red flag will be highlighted to the doctor.

This model shall also be utilized to predict hospital stay of admitted patients and 2nd time heart

attack probability for patients.

7. Survival or Duration Analysis

It is a statistical methods for analyzing longitudinal data on the occurrence of events. The

following study has been undertaken in the healthcare sector using the Survival or Duration

Analysis Model:

• To assess the relationship of co-variables to time-to-event, such as: does weight, insulin

resistance, or cholesterol influence survival time of patients

• Estimate time-to-event for a group of individuals, such as time until second heart-attack for a

group of patients

Using Survival or Duration Analysis we will be innovating a programme that manages the data

input and scans frequency of doctor home visit and then predicts force a week or month or bi-

weekly visit for a particular patient.

8. Decision Trees

Decision analysis utilizes mathematical models to quantitatively compare multiple decisions

accounting for both the monetary cost and the effect on quality of life. The strength of decision

analysis is that the process offers an explicit and systematic approach to decision making based


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on the premise of rationality. The following study has been undertaken in the healthcare sector

using the Decision Tree Model:

• Should a health care worker who has a needle stick injury be given HIV prophylaxis treatment

Using the Decision Tree Model we will be innovating a programme to predict healthcare cost

based on past admission data for the patients.

9. Multivariate Adaptive Regression Splines

MARS is a nonparametric regression procedure that makes no assumption about the underlying

functional relationship between the dependent and independent variables. Instead, MARS

constructs this relation from a set of coefficients and basic functions that are entirely "driven"

from the regression data. The following studies have been undertaken in the healthcare sectorusing the MARS Model:

• Estimate number of accidents caused by various external factors like drinking and driving,

poor weather conditions and bad condition of roads

• Predict healthcare costs for a patient by categories (ex: total costs, pharmacy cost etc.) with

indicating presence of certain health conditions

Using MARS we will be innovating a programme that will form a disease cluster identification

based on HL – 7 and ISD – 10. This model along with KNN shall also address the Socio Eco Class

and Geography based

10. MLT

Machine learning techniques emulate human cognition and learn from training examples to

predict future events. The following studies have been undertaken in the healthcare sector

using the MLT Model:

• Predicting Hospital Readmissions

• Predicting Strokes, Seizures and Identifying Heart Failure

• Predicting Emergency Room Wait Times

• Automating Employee Access Control

Using the MLT Model we will be innovating a programme which will predict future events based

on past data of medical history. These predictions will envisage cardiac events and with the


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Skanray technology it will be very useful for medical practitioners to give treatment

accordingly.

11. Neural Network

Neural networks are nonlinear sophisticated modeling techniques that are able to model

complex functions. They can be applied to problems of prediction, classification or control in a

wide spectrum of fields. The following studies have been undertaken in the healthcare sector

using the Neural Network Model:

• Identify signs of precancerous and cancerous changes in body

• Synthesizing, optimising and analysing neural networks to an Electrocardiogram (ECG)

Patient Monitoring task

• Predicts a drug's mechanism of action from its pattern of activity in the body

• Enable health care providers to individualize a care management plan based on the possibility

of a patient not completing quarterly visits.

• Diagnose diabetes

• Diagnose of malignancy of Thyroid Nodules

Using both MLT and Neural Net models of predictive analysis we will be devising a

recommendation engine like our proprietary CapitaWorld.

12. Radial Based

A radial basis function (RBF) is a function which has built into it a distance criterion with

respect to a centre which can be used very efficiently for interpolation and for smoothing of

data. The following study has been undertaken in the healthcare sector using the Decision Tree

Model:

• Arrhythmia Detection and Classification

Using the Radial Based Model we will be innovating a programme that will perform cluster

analysis on medical historical data, filter it and then compare the results with KNN and Filter B

results so as to provide meaningful insights about the individual patient.

13. SVM


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Support Vector Machines (SVM) are used to detect and exploit complex patterns in data by

clustering, classifying and ranking the data. They are learning machines that are used to

perform binary classifications and regression estimations. The following studies have been

undertaken in the healthcare sector using the SVM Model:

• Prediction of common diseases: the case of diabetes and pre-diabetes

• Classify persons with and without common diseases

• Classify persons for Automatic Medical Diagnosis

• Prediction of Medication Adherence in Heart Failure Patients

• Identification of Breast Cancer

Using the SVM Model we will be classifying the patients into buckets of category that will

support the hospitals maintain data more efficiently

14. Naive Bayer

Naïve Bayes based on Bayes conditional probability rule is used for performing classification

tasks. It is best employed when faced with the problem of ‘curse of dimensionality’ i.e. when the

number of predictors is very high. The following studies have been undertaken in the healthcare

sector using the Naive Bayer Model:

• Prediction System for Heart Disease

• Medical Data Classification

• Prediction of Swine Flu Disease

• Prediction of Different Dermatological Conditions

• Web Based Health Care Detection

• Decision Support in Heart Disease Prediction System

• Analysing time to time event data

Using the Naive Bayes Model we will be innovating a programme that shall predict heart attack

and web based detection of an individual’s health. So for instance the heart rate of a stock

market trader shall be analyzed through an automated faishon and recorded when the stock

market prices are in recession.


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15. KNN Classifier

The nearest neighbour algorithm (KNN) belongs to the class of pattern recognition statistical

methods. The method does not impose a priori any assumptions about the distribution from

which the modeling sample is drawn. It involves a training set with both positive and negative

values. A new sample is classified by calculating the distance to the nearest neighbouring

training case. The sign of that point will determine the classification of the sample. The

following studies have been undertaken in the healthcare sector using the KNN Classifier Model:

• Designing an automatic, data-driven, machine-learning algorithm in clinical decision making

• Diagnosing Heart Disease Patients

• Medical Data Mining and assessing for insights

• Diagnosing Heart Disease Patients

Using KNN Model we will be innovating a programme that will cluster diseases and perform

image clean up.

16. Group method of data handling

The use of such self-organizing networks leads to successful application in a broad range of

areas. The following study has been undertaken in the healthcare sector using the Group

method of data handling Model:

• Construct gene expressions program

Using Group method of data handling Model we will be innovating a programme that shall

calculate and assess financial distress production.

17. Random forests

Random forests is a notion of the general technique of random decision forests that are an

ensemble learning method for classification, regression and other tasks, that operate by

constructing a multitude of decision trees at training time and outputting the class that is the

mode of the classes (classification) or mean prediction (regression) of the individual trees.

Random decision forests correct for decision trees' habit of over fitting to their training set. The

following studies have been undertaken in the healthcare sector using the Random Forests

Model:


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• Utilizing Healthcare Cost and Utilization Project (HCUP) dataset for predicting disease risk of

individuals based on their medical diagnosis history

• Diabetic Retinopathy Classification Analyses

• Detecting cardiac arrhythmias using ECG data

• Customer Retention Predictive Modeling for HealthCare Insurance Industry

Using the Random Forests model we will innovating a programme that will assist insurance

companies, Hospitals and individual patients estimate the costing of the treatment which

includes each and every cost in the long run.

18. CART

CART presents a sophisticated snapshot of the relationship of variables in the data and can be

used as a first step in constructing an informative model or a final visualization of important

associations. The following study has been undertaken in the healthcare sector using the CART

Model:

• Predict pulmonary tuberculosis in hospitalized patients

Using the CART Model, we will be innovating a programme that will support through predicting

the risk of heart attack.

Final Word

Our Healthcare Analytics programmes built on various predictive models shall follow standards

of HL 7. Health Level-7 or HL7 refers to a set of international standards for transfer of clinical

and administrative data between software applications used by various healthcare providers.

The programmes promise of supporting a wide range of medical and healthcare functions,

including among others clinical decision support, disease surveillance, and population health

management.

Potential benefits of Healthcare Analytics include detecting diseases at earlier stages when they

can be treated more easily and effectively; managing specific individual and population health

and detecting health care fraud more quickly and efficiently. All of the stakeholders with

Healthcare will benefit with the programmes:

• Predictive analytics can provide employers and hospitals with predictions

concerning insurance product costs


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• Pharmaceutical companies can use predictive analytics to best meet the needs of

the public for medications.

• Patients have the potential benefit of better outcomes due to predictive

analytics.

• Predictive analytics provides physicians with answers they are seeking for

individual patients.

• Predictive analytics will help preventive medicine and public health.

• Predictive analytics increase the accuracy of diagnoses.

Healthcare Analytics Archeron White Paper

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