Noah  Rowe

Noah Rowe

1596812100

Predictive Modeling in Healthcare Analytics

TESTING! Most of us have heard this word regularly in recent times. Inadequate testing of COVID-19 patients has resulted in under-reporting of cases across the world. This also puts the healthy population at risk of getting infected. Now, what if we can predict the chances of a person being infected with a disease using previous clinical data? This is where predictive modeling comes into the picture.

Predictive modeling is a process of modeling historic data for predicting future events. For example, by using the Electronic Health Record (EHR) of patients we can create a model that predicts the patients having a risk of heart failure sooner. In this article, I will try and explain how we can develop a good predictive model using EHR quickly.

According to CMS.gov, An Electronic Health Record (EHR) is an electronic version of a patient’s medical history.

What makes Predictive modeling challenging?

We have millions of patients, their diagnosis information, clinical data, and so on. All data combined creates a big challenge. Another challenge in predictive modeling is the abundance of models to be built. Every step in the predictive modeling pipeline has many different options. It is important to choose the right one!

Image for post

Designed using Canva.


Predictive Modelling Pipeline

Predictive Modelling is not a single algorithm, but a computational pipeline that involves multiple steps. First, we decide the prediction target, for example, whether a patient will develop heart failure in the next few years. Second, we construct the cohort(a group of people with a shared characteristic) of relevant patients for the study. Third, we define all the potentially related features for the study. Fourth, we select the features which are relevant only for the prediction of the target. Fifth, we compute the predictive model and next, evaluate the predictive model. We iterate this process several times until we are satisfied with the result.

Image for post

Designed using Canva.


1) Prediction Target

There are often many targets, investigators want to predict using the data they have. However, only a subset of them is possible. So we should choose a prediction target that addresses the primary question which is both interesting to the investigator and possible to answer with the data available. In this article, we will be investigating the onset of heart failure which is both an interesting and potentially possible target.

#data-science #artificial-intelligence #health #predictive-analytics #machine-learning #data analytic

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Predictive Modeling in Healthcare Analytics
Ian  Robinson

Ian Robinson

1623223443

Predictive Modeling in Data Science

Predictive modeling is an integral tool used in the data science world — learn the five primary predictive models and how to use them properly.

Predictive modeling in data science is used to answer the question “What is going to happen in the future, based on known past behaviors?” Modeling is an essential part of data science, and it is mainly divided into predictive and preventive modeling. Predictive modeling, also known as predictive analytics, is the process of using data and statistical algorithms to predict outcomes with data models. Anything from sports outcomes, television ratings to technological advances, and corporate economies can be predicted using these models.

Top 5 Predictive Models

  1. Classification Model: It is the simplest of all predictive analytics models. It puts data in categories based on its historical data. Classification models are best to answer “yes or no” types of questions.
  2. Clustering Model: This model groups data points into separate groups, based on similar behavior.
  3. **Forecast Model: **One of the most widely used predictive analytics models. It deals with metric value prediction, and this model can be applied wherever historical numerical data is available.
  4. Outliers Model: This model, as the name suggests, is oriented around exceptional data entries within a dataset. It can identify exceptional figures either by themselves or in concurrence with other numbers and categories.
  5. Time Series Model: This predictive model consists of a series of data points captured, using time as the input limit. It uses the data from previous years to develop a numerical metric and predicts the next three to six weeks of data using that metric.

#big data #data science #predictive analytics #predictive analysis #predictive modeling #predictive models

Alayna  Rippin

Alayna Rippin

1597622400

The Trending Healthcare App Features for 2020

The recent technological trend in the healthcare industry has brought a virtual doctor into many of our pockets. Be it a serious health condition or a need to track our fitness level, there are thousands of mHealth apps for most of the healthcare use cases.

In 2020, the health-tech industry offers many mHealth apps ranging from heart rate monitoring to nutrition and fitness apps. Undoubtedly, there will be variation in the app functionality according to the target market, customer base and the purpose.

Use-Case specific app features

There are thousands of healthcare mobile apps available in the market and depending on the use-cases, they will incorporate specific features that help them serve the purpose. Following are some of the most trending app features.

Digital Prescription and Reports

Almost every hospital or pharmacy-related healthcare app should have the functionality to handle digital prescriptions and reporting. Misplacing or losing medical prescriptions and reports is very common for the patients. It even becomes a hassle for the healthcare institutions or doctors to manage and access the patient files. This is where the digital prescription feature comes handy for both the patients and doctors.

This feature should also incorporate the functionality to download the reports and prescription information in a documented format. It allows the patients to manage their lifetime medical history in one place. This feature is very crucial to speed up the medical treatment process.

Wearable Connectivity

Wearables are the most trending discussion in the health tech space. Up until now, this feature is most commonly used by fitness tracking apps. But as the healthcare industry has now paced up the technology adoption, healthcare providers and medical practitioners have started to trust the wearable technologies to monitor their patient health continuously.

In 2020, wearables do not just mean some gadgets like Fitbits. There are many clinical-grade IoMT (Internet of Medical Things) devices used by the healthcare industry that are used in a form of belts, chest straps etc.

The wearable connectivity feature allows the healthcare apps to record the user’s data, which can be shared with the doctors. The doctors can provide a better consultancy if they have access to their patient’s all-time health status.

On-Demand Medicine

Similar to amazon for x apps, this on-demand app feature is focused on making the medicines accessible anytime, anywhere. Using this feature, the patients will be able to refill their stock of medicine without having to visit the pharmacy.

The feature would require the functionality to allow admin to list out the pharmacies in the locality, so that the users can order from the nearest one. To make this feature more effective, you also would need to add an online payment feature within the app.

#healthcare #health-tech #health-tech-and-cyber-security #healthcare-apps #healthcare-application #healthcare-mobile-apps #healthcare-trends-in-2020 #top-healthcare-trends

Noah  Rowe

Noah Rowe

1596812100

Predictive Modeling in Healthcare Analytics

TESTING! Most of us have heard this word regularly in recent times. Inadequate testing of COVID-19 patients has resulted in under-reporting of cases across the world. This also puts the healthy population at risk of getting infected. Now, what if we can predict the chances of a person being infected with a disease using previous clinical data? This is where predictive modeling comes into the picture.

Predictive modeling is a process of modeling historic data for predicting future events. For example, by using the Electronic Health Record (EHR) of patients we can create a model that predicts the patients having a risk of heart failure sooner. In this article, I will try and explain how we can develop a good predictive model using EHR quickly.

According to CMS.gov, An Electronic Health Record (EHR) is an electronic version of a patient’s medical history.

What makes Predictive modeling challenging?

We have millions of patients, their diagnosis information, clinical data, and so on. All data combined creates a big challenge. Another challenge in predictive modeling is the abundance of models to be built. Every step in the predictive modeling pipeline has many different options. It is important to choose the right one!

Image for post

Designed using Canva.


Predictive Modelling Pipeline

Predictive Modelling is not a single algorithm, but a computational pipeline that involves multiple steps. First, we decide the prediction target, for example, whether a patient will develop heart failure in the next few years. Second, we construct the cohort(a group of people with a shared characteristic) of relevant patients for the study. Third, we define all the potentially related features for the study. Fourth, we select the features which are relevant only for the prediction of the target. Fifth, we compute the predictive model and next, evaluate the predictive model. We iterate this process several times until we are satisfied with the result.

Image for post

Designed using Canva.


1) Prediction Target

There are often many targets, investigators want to predict using the data they have. However, only a subset of them is possible. So we should choose a prediction target that addresses the primary question which is both interesting to the investigator and possible to answer with the data available. In this article, we will be investigating the onset of heart failure which is both an interesting and potentially possible target.

#data-science #artificial-intelligence #health #predictive-analytics #machine-learning #data analytic

Gerhard  Brink

Gerhard Brink

1624054260

Big Data Analytics in Healthcare: Possibilities and Challenges

In the transformation of healthcare practices and science, the rapidly evolving field of big data analytics has begun to play a pivotal role.

Big Data analytics lets businesses leverage their knowledge and use it to discover new possibilities. This in turn leads to smarter business movements, smoother processes, higher revenues, and more satisfied clients.

In the transformation of medical practices and science, the rapidly evolving field of big data analytics in healthcare has begun to play a pivotal role. It has provided instruments for the accumulation, management, analysis, and assimilation of large amounts of diverse, structured, and unstructured data generated by existing healthcare systems.

In order to enhance diagnosis, the data is extremely useful and can help analyze a whole variety of problems, involving symptoms, pharmaceuticals, and dosage. It would be much more difficult for medical professionals to come to the right conclusions without this knowledge.

Some of the Benefits of Big Data in Healthcare are:

**• **Improved performance for operations

**• **Advance Care and Treatment for Patients

**• **The Right Treatment for Diseases Discovery

**• **Personalized and Integrated Communication

**• **Strengthened access to key information

The barriers to big data analytics in healthcare lie beyond the possibilities. Big Data in healthcare has its own characteristics, including heterogeneity, inadequacy, promptness and durability, anonymity, and management. In order to facilitate health-related science, these features introduce a number of challenges to data storage, mining, and sharing.

Some of the Challenges of Big Data in Healthcare are:

**• **Due to lack of effective data governance procedures, capturing data is one of the biggest obstacles for healthcare organizations. To use data more efficient, it must be clean, precise, correctly formatted so that it can be used across various healthcare systems.

**• **Most patient records are kept for fast and easy access in a centralized database these days, but the real problem lies when this information that needs to be shared with outside healthcare professionals.

**• **For most healthcare providers, data security is one of the top issues with constant hacking and security violations that need to be handled on a continuous basis.

**• **When dealing with highly sensitive data and even patient data, which is important, the healthcare industry must be very cautious. Not only can leakage of details prove costly to healthcare companies, but it is also unethical to disclose it without prior authorization.

#big data #healthcare #latest news #big data analytics in healthcare: possibilities and challenges #big data analytics in healthcare #possibilities

Ray  Patel

Ray Patel

1623251700

Beating the Averages With Predictive Data Analytics

Predictive analytics can help narrow the chasm between data analytics professionals and the business people who benefit from their activities.

“Nanoeconomics” may sound like a college course that one may expunge from their minds as soon as they wrap up their final exam the last day of the semester, but it’s a force that may supercharge the insights data and technology professionals are delivering to their business decision-makers. At its heart, data analytics is an economic activity, expected to add some incremental value to corporate revenues.

But there’s been a yawning chasm between the activities of data analytics professionals and the businesspeople who are supposed to see the benefits of those activities. Namely, analytics insights are typically based on statistical averages, versus directly focusing on the problems at hand.

#analytics #big data #big data analysis tools #business strategies #decision management #real-time decisions #trending now #predictive analytics #prescriptive analytics