Analyzing Alzheimer’s

by | Oct 23, 2020 | Analytics, Industry Trends

Analyzing Alzheimer’s Disease

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

 

Introduction 

Alzheimer’s disease is a progressive disease that sees cells and cell connections die resulting in the loss of memories and mental function. It is a condition that affects 5 million Americans, with projections to affect 14 million people by 2050 and while there are medications to help treat symptoms, there is no cure. When trying to battle diseases without a cure, the best thing to do is to look at prevention and applying data science to that process can improve our understanding of how mitigating factors can benefit us individually. It can also help scientists create targeted gene therapies to help those affected by Alzheimer’s.   

Discussion 

Currently, a lot of the research being done in regards to Alzheimer’s disease revolves around genetics because of the genetic expressions and patterns that seem to be consistent in many of those that have the disease. To apply analytics to this knowledge, researchers at Icahn School of Medicine at Mount Sinai and Emory University formed a joint ventures with other research institutions to perform deep data analysis on mined DNA, RNA, protein, and clinical data. With this data they hoped to identify regulators and predictors of the disease in order to create targeted gene therapy treatments. What they found was that, though there are correlations between genetic expressions and Alzheimer’s, they were not strong enough to identify a singular cause. However, they did find that a protein called VGF plays an “plays an important role in protecting the brain against the onset and progression of Alzheimer’s disease.” Once they identified this connection, scientists could create a testing environment in which they “ramp[ed] up levels of the gene or protein in mice” and saw that those mice had a significantly lower risk of having Alzheimer’s or saw the progression of their disease slow down. Another gene therapy study out of Stanford University made a similar connection with the ApoE4 gene variant which is present in more than half of Alzheimer’s patients. Again, they did not find this to be a direct cause of the disease but prevalent enough that increased expressions of that gene increases the risk of Alzheimer’s. Studies like these are extremely important because understanding our genetic risk factors can help us understand how dedicated we should be to focusing on mitigating factors.

Conclusion 

Alzheimer’s is a difficult disease to live with and, though genetic risk factors are unavoidable, living a healthy lifestyle can help prevent Alzheimer’s by lowering blood pressure and cholesterol, as well as lowering the risk of contracting diabetes, all of which have been connected to Alzheimer’s. Incorporating healthy foods and exercise into your lifestyle can help support these goals. Additionally, staying mentally active by continuing to learn and keeping up social connections has been shown to decrease the risk of Alzheimer’s as well. Finally, avoiding head trauma by taking precautions such as wearing a seatbelt, wearing a helmet and avoiding falls are all important steps you should take. Though we are discussing these prevention steps in terms of Alzheimer’s, they are all important steps to mitigating many other conditions. 

How Analytics and Technology Can Enable and Improve Patient Engagement

by | Oct 9, 2020 | Analytics, Health and Wellness, Health Plans, Technology

How Analytics and Technology Can Enable and Improve Patient Engagement 

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

When it comes to our medical data, many people simply go to their annual checkups and hear feedback from their doctors; this is often a very one sided experience and we know that for people to be healthy, they must take an active role in their health. Engaging in our health can mean different things for different people – for those who are very health, engaging in our health could mean tracking nutrition and dietary decisions or exercise routines and for those suffering from chronic conditions, such as diabetes, this can mean using apps that track health metrics to improve the understanding of your own body. In this article we will take a look at a few companies who are seeking to improve patient engagement to improve the health of its users. 

Body 

In this day and age, nearly everyone has a smartphone and from that phone they conduct almost all of their communication, check the weather, secure their homes, do banking and more. It is only natural then that they could also utilize this device to enhance their health. Apps are a great way to increase patient engagement because of their accessibility and many providers have realized this. MyChart is widely used by providers to communicate test results and ranges, appointment summaries and other relevant health information. This is an important tool for patients to have because it enables them to always have their medical records on hand as either reminders for themselves or supplemental information for nutritionists or trainers. MyChart also enables patients to have a direct line of communication with their doctors to ask any pressing or important questions. Another great app available to consumers to mySugr; this app was created to help diabetics track, understand and control their blood sugar levels. The app lets users log their blood glucose and insulin levels, their medication list and dosages, as well as meals from which they derive an estimated carb intake. All of these factors are key to keeping diabetic patients in good health and mySugr utilizes these inputs to create detailed reports and health analysis for patients, as well as takeaways to provide your physician. A great app for those suffering from cardiovascular issues is Kardia – Kardia is integratable with health devices such as EKG and blood pressure devices to analyze and log your EKG results. With continued time and use, the app will learn your body’s normal readings and note when abnormalities show up, as well as when those abnormalities seem serious enough to contact a physician. The app also creates concise reports to share with your providers. 

Conclusion

As technology continues to improve the world around us, it is amazing that it can also improve the functions that happen within us. Analytics and apps make improved health easy to access for many people and, in many cases, at no additional cost which means that everyone should consider incorporating such apps into their lives.

 

Analyzing Air Pollution and its Effects on Our Health

Analyzing Air Pollution and its Effects on Our Health

by | Sep 30, 2020 | Analytics, Health and Wellness

Analyzing Air Pollution and its Effects on Our Health

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction 

As wildfires in California continue their path of destruction through the Golden State, Americans across the country are feeling the effects of their destruction as smoke makes its way up to 2,500 miles across the country. The widespread effects of smoke pollution are well documented as being dangerous to health, however, it is only recently that the power of data analytics has given us more insight into the issue. 

Body 

China is a country with a huge carbon footprint and the impact of its economic decisions has created thick smog in its most populated cities that have led citizens to wear protective masks for years. A study out of Shenzhen has created a stepping stone for further research in this area by creating a proposed model that looks at: 

(i) estimating high resolution concentrations of air pollution with big data analytics based on enormous structured and unstructured data

(ii) quantifying the health effects of both single pollutant and pollutant mixtures

(iii) designing the personalized health advisory model based on individual characteristics and exposure information

 Another study out of Amity Institute of Information Technology led to a unique model workflow:

 

The takeaways from these models are that the data extraction methods for this type of research are twofold – these models would focus on both spatiotemporal and medical data inputs to create relationships between these points, at which point machine learning can be applied to understand how certain environmental events can impact both the environment and human health. 

The biggest roadblocks that occur when creating these types of models is that no two environmental effects are ever the same and there are many different factors that make that the case. Wildfires in Arizona are very different from wildfires in California and while we can hope that machine learning will differentiate and adapt to them for us, there are variables that cause these differences that may be left out. For example, humidity can exacerbate natural disasters, as can the terrain of the area in which they happened. The number of houses and cars, types of materials present in the area and so many other factors can influence these models such that it is very possible for researchers to not be able to consider them all. 

Conclusion

The most impactful conclusion we can draw from the California wildfires is that their detrimental effects on our health and planet would have been drastically lower had precautions been taken in terms of climate change. Climate change has created an environment that helps these wildfires thrive and makes it significantly more difficult to quell their flames. Precautions we can take for our own health include investing in air purifiers, staying indoors when possible and wearing protective masks when outside. Precautions we can take to prevent further damage to our planet include investing in alternative energy sources and lowering our carbon footprint through making more sustainable decisions such as shopping locally, recycling and carpooling; more information on lowering your carbon footprint can be found here.

 

Using Big Data to Improve the Lives of Liver Disease Patients

by | Sep 23, 2020 | Analytics, Industry Trends, Technology

Using Big Data to Improve the Lives of Liver Disease Patients

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

 

Introduction 

The liver is an extremely important organ in our bodies and is responsible for several tasks; of its many functions the most important tasks is its filtration and detoxification functions. The liver also helps regulate blood sugar, breakdown fat, produces and recycles blood at certain points in our lives, and many, many other supporting functions. Clearly, the liver performs key actions to keep our bodies healthy and functioning so anything that decreases its capabilities, such as liver disease, is important to look at. Liver disease is a very broad term and refers to any issue that affects the liver’s ability to function. Liver issues are classified as a disease when 75% or more of the liver tissue is affected which is when decrease in function begins. It can be caused by one or many factors – infections such as hepatitis, autoimmune diseases, certain cancers, genetics, alcohol abuse and increased fat accumulation can all play a role in the onset of liver disease. The use of data to analyze these factors, as well as metabolic factors, is very key to improving the diagnosis and management of liver disease. 

Discussion 

There are a few quantifiable factors that are related to liver disease; those factors include: Age,Gender, Total Bilirubin, Direct Bilirubin, Alkaline Phosphatase, Alanine Aminotransferase, Aspartate Aminotransferase, Total Proteins, Albumin and Albumin and Globulin Ratio. All of these variables have been positively correlated to the presence of liver disease and would be important factors for any algorithm that looks at liver disease or other diseases that compromise liver function. The Global Journal of Computer Science and Technology published a report in 2010 that looked at how to apply statistical modeling and machine learning to the study of liver disease. They used three different supervised algorithms – Naive Bayes, KStar and FT Trees – to predict the accuracy of liver disease diagnoses and found that FT Trees provided the highest level of accuracy at 97.10%. Such a high accuracy rate provides a solid foundation for other researchers to add in new variables and factors to further improve that rate. Also in this area of research is a project led by Harvard Medical School, Massachusetts General Hospital and Georgia Institute of Technology that has set out to further understand the effects of alcohol in relation to liver issue related deaths. The project begins by modeling drinking patterns against alcohol based liver issues in patients born from 1900-2012 and then utilized different intervention scenarios to see if reducing alcohol consumption also lowered chances of liver issue related deaths. The findings, in line with most practical medical advice, found that liver function decreased with alcohol consumption and its functionality improved more with each intervention that happened sooner than later.  

Prevention

The most important thing you can do to keep your liver healthy is to eat healthy foods and live a healthy lifestyle, including reduction of alcohol consumption. There are several genetic factors that, unfortunately, cannot be mitigated, however, having genetic tests done so that you are aware of any increased risk you may have is critical and can help you determine how much you may need to adjust your lifestyle to accommodate those factors. 

Conclusion 

Similar to many other diseases, liver disease is quantifiable in many ways. There are already several promising studies that have created a solid foundation for further research in the area of machine learning applications to this field of study; additionally, there are also promising studies that show the importance and influence of lifestyle interventions in changing the success rate of patients suffering from liver disease.

 

 

Putting Data Behind Parkinson’s Disease

by | Sep 18, 2020 | Analytics, Health and Wellness

Putting Data Behind Parkinson’s Disease

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction 

Over one million people living in the United States suffer from Parkinson’s disease – more than the number of people suffering from multiple sclerosis, muscular dystrophy, and amyotrophic lateral sclerosis combined. Parkinson’s disease is a progressive disease affecting the nervous system that leads to stiffness and slowing of movement due to nerve cells in the brain breaking off or loss of neurons. Symptoms in the early stages of the disease can include changing facial expressions, slurred or soft speech and other minor changes in the person’s ability to move normally and easily. As the disease worsens, one can develop a tremor, suffer from rigid muscles and impaired balance, and loss of automatic movements. The cause of Parkinson’s disease is unknown but early research suggests certain gene variations can increase one’s risk of having Parkinson’s, as well as the possibility that certain toxins can trigger the onset of Parkinson’s. Treatment for Parkinson’s disease is also fairly costly, and its side effects, such as decreased cognitive abilities, can often further decrease a patient’s ability to shoulder the associated costs. Clearly, there is huge importance to furthering the study of Parkinson’s disease and one of the ways to improve such studies is to use analytics tools in disease analysis.

Discussion

There are several ways in which analytics can be used to benefit those suffering from Parkinson’s disease; because the nature of the symptoms and effects of Parkinson’s is mainly physical, a lot of the metrics used in caring for patients revolve around movement. For example, activity trackers used for Parkinson’s patients include algorithms that detect abnormalities in walking patterns such as “tremor, dyskinesia, asymmetry, festination, and freezing.” These algorithms can also study the level of activity and then use this information to understand how a patient’s walking patterns and habits are changing. Tremors are a common symptom of Parkinson’s and can be observed and measured using spectral analysis; measuring Parkinson’s tremors can be helpful because such “episodes are correlated to medication intake events” and doctors can adjust medication consumption as necessary based on the observed data. Furthermore, as technology continues to evolve rapidly, patients may be able to understand and adjust their medication accordingly on their own.  Finally, because many people with Parkinson’s experiences sleep disturbances such as “insomnia, periodic limb movement disorder and REM-sleep disorder,” combining a generic sleep study and fait pattern studies and applying data science tools can provide a more accurate analysis of sleeping habits for Parkinson’s patients. 

Another promising step in the way of technology and analytics supporting the lives of people suffering with Parkinson’s disease can be seen in the use of wearable technology to evaluate symptoms of Parkinson’s. Intel Corporation, in partnership with the Michael J. Fox Foundation, proposed a program in 2014 to develop a wearable tracking watch that could conveniently collect and record patient information. From these records, machine learning techniques could be applied to understand and assess the progression of a patient’s symptoms and help providers adjust care management methods or medication dosages. 

Conclusion 

Parkinson’s disease is a condition that affects many Americans and many more across the globe. While no treatment to cure Parkinson’s exists, there are care management options available and applying data science tools to these options can significantly improve a patient’s quality of life. Additionally, the creation of tools such as smart watches can further improve the quality and quantity of data available to perform these studies.

 

 

The Most Personalized and Precise Form of Healthcare: a Discussion of the Genome

The Most Personalized and Precise Form of Healthcare: a Discussion of the Genome

by | Sep 3, 2020 | Analytics, Health and Wellness, Technology

The Most Personalized and Precise Form of Healthcare: a Discussion of the Genome

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction 

Our genomes are essentially a personalized index or library of everything we are – they are the combination of genes and DNA that hold all of our genetic information. The field of genomics is relatively new with most studies citing its roots in the 1980’s. In fact, The Human Genome Project only began development in 1990 and was declared complete in 2003. Though new, the field of genomics, like many other fields of study touched by technology, has evolved rapidly. For context, processing a human genome would have cost $20-25 million in 2006 compared to a cost of well below $1,000 today and its market has seen growth from $1 billion to $4.5 billion in the last 8 years alone. Furthermore, the first time a human genome was sequenced took 3 years of processing power while today a human genome can be processed in less than 3 days. The increased accessibility to genomic information is an incredibly important development in terms of preventative care and can be a life-saving step for many people.

Discussion

The Precision Medicine Initiative “is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person [whose] approach will allow doctors and researchers to predict more accurately which treatment and prevention strategies for a particular disease will work in which groups of people.” This is where genomics finds its highest level of applicability and importance. Genomics and the power that we now have in machine learning can provide incredible insights into which genes are relevant to certain diseases. With that insight, patients can adjust their lifestyle to their risk factor and have a much better understanding of where they stand in terms of their health. Doctor’s also have a much clearer idea of what tests may need to be run a lot earlier in a patient’s life and what tests may never need to be run unless an event occurs prompting questions outside of the norm. Overall, genomics can save a lot of time and money for both providers and patients alike.

One of the struggles with sequencing an  individual’s entire genomic profile is the sheer processing and holding power needed to execute algorithms on an entire sequence – massive database space is necessary to perform these types of analytics. However, one consideration that can be factored into account to make genomics even more accessible is isolated sequencing. For example, if someone already knows their family has a high risk of a certain disease, they may choose to only sequence parts of their DNA, such as the BRCA1 and BRCA2 genes sequenced for those individuals with a higher risk of breast cancer. This methodology can be applied to any genetically passed disease. However, the ultimate hope and goal for many is that genome sequencing becomes accessible enough so anyone can sequence their entire genome. This could then be utilized by healthcare providers who can provide a much more personalized approach to a patient’s diagnosis and care plans with that information. 

Conclusion 

In comparison to many other fields of study, genomics is very new, however, that hasn’t stopped it from catching up (and even outrunning and outshining) many other fields in terms of accessibility. When we look at communities, whether that be by location, ethnicity, age or gender, we get a much clearer picture of how the health of a population is influenced. As accessibility to the technology used to support genomics increases for patients, we can expect that picture to get even clearer and to see an even more personalized approach to healthcare.