AHA Pitch

AHA Pitch

by | Aug 17, 2022 | Analytics, Health and Wellness, Health Plans, News

Altheia Predictive Health Pitch at AHA – Empower to Serve MN – FAN FAVORITE 2022 WINNER

Aug 9, 2022 | News

Jolly Nanda, the visionary behind Altheia Predictive Health was selected as one of 4 companies to compete for grant funding at the American Heart Association  EmPOWERED to serve Business Accelerator in Minnesota. This Business Accelerator looks for a diverse pool of social and digital health entrepreneurs and organizations who are driving change through health justice in their communities. Finalists participate in a six-week virtual business training and have a chance at grant funding. The event was held at the General Mills Headquarters and sponsored by Cheerios.

Analyzing the Covid-19 Vaccine Distribution Method and Experience

Analyzing the Covid-19 Vaccine Distribution Method and Experience

by | Jan 15, 2021 | Covid

Analyzing the Covid-19 Vaccine Distribution Method and Experience

Introduction

As the Covid-19 vaccine begins to roll out for those who qualify for the initial distribution, the country as a whole is still seeing spikes and surges of cases, as well as high occupancy levels at hospitals and ICUs. The LA Times reported that “one hospital in southwest Los Angeles, Memorial Hospital of Gardena, is at 320% occupancy [and that] the 172-bed medical center has been in various levels of “internal disaster status” since March.”  That said, as the vaccine becomes more available to the general public, it is important to understand what type of analytics can help us bring these shocking statistics down and how we can understand if distribution plans are effectively working. 

Discussion

In evaluating the best way to roll out the vaccine, Accenture put together a list of relevant information to be collected for analysis. So what are the relevant data and statistics that go into a successful roll out? The first step in planning is forecasting and this is done by collecting data on a given population, this can be conducted at the county, city, state or regional level; relevant data in terms of the vaccine will include age and level of disease present in that population. From that information, distributors can decide where to locate dispensing sites and the supply necessary at each dispensing site. Once distribution starts, the next step will be adjustments which can be aided through artificial intelligence and machine learning. This would simply analyze the forecasting method used in the initial step and improve upon it so that new distribution centers are better prepared than the first ones. The final step is to evaluate the success of distribution which will vary based on the distribution leader, however, because the goal is to vaccinate as many people as possible and as quickly as possible, this will look at tiers that had the most waste in terms of vaccines and tiers that did not have enough vaccines to meet their demand. 

One of the main concerns when it comes to vaccine distribution, on every level including the country level, is equality in distribution. An interesting point of data is that “as of 15 November 2020, several countries have made premarket purchase commitments totaling 7.48 billion doses, or 3.76 billion courses, of Covid-19 vaccines from 13 vaccine manufacturers. Just over half (51%) of these doses will go to high income countries, which represent 14% of the world’s population.” This brings up questions of income disparity as it relates to population health and is something that can also be tracked through analytics. 

Conclusion 

As accessibility to the Covid-19 vaccine increases, we highly encourage you to consider getting vaccinated. The policies and practices in place in this country make the vaccinations very safe, in fact,“the US has the best post-licensure surveillance system in the world making vaccines extremely safe and not getting vaccinated puts yourself and others at risk.”  There is a lot of hesitation and concern when it comes to the vaccine which will make people less likely to get it on their own. To encourage you to get it as soon as it is made available to you, we are sharing the experience of Adiba Mobin, a UT Southwestern employee who has received both doses of the vaccine:

“I received the first dose of the Pfizer COVID-19 vaccine on December 23, 2020, just 9 days after the initial vaccine rollout in Texas. I did not take any medication (Ibuprofen, Acetaminophen, etc) before getting the vaccine. A few hours after receiving the first dose my arm became sore at the injection site and the soreness soon spread to my entire upper arm (shoulder to elbow) so I took some Tylenol for the pain which helped with the pain. Within the next 24 hours the soreness had vanished and I had absolutely no other side effects. 

I received the 2nd dose on January 12, 2021, exactly 21 days later. This time I went prepared with a painkiller 30 minutes before getting vaccinated. The nurse administering my vaccine encouraged me to stay ahead of the possible side effects by taking Aleve or Tylenol specifically, as Ibuprofen might not be as effective. Once again, about 2-3 hours after receiving the second dose I had arm soreness at the site and at 5 hours post vaccination I could feel a headache coming on. At 8 hours post I took 1 extra strength Tylenol. The following day at 24 hours post-vaccination there was still arm soreness and a lingering headache but I did not take another Tylenol until 32 hours post vaccine, to help with some very minor muscle aches and arm soreness. All of my side effects were extremely manageable and nothing that took me away from my work for the day.” 

 

Putting Data Behind Healthy Decisions

Putting Data Behind Healthy Decisions

by | Jan 1, 2021 | Health and Wellness

Putting Data Behind Healthy Decisions

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

As we ring in 2021, we also ring in an abundance of New Year’s resolutions and the most common resolutions include health related goals such as improved eating habits and weight loss. In all of our articles that quantify diseases, we include tips for improved health. Many of them include eating a healthy, balanced diet and incorporating exercise in your lifestyle, as well as more specific tips. In today’s article, we will put some numbers behind some of these tips to encourage you to incorporate them into your New Year’s goals and to generally increase your knowledge and add perspective to these lifestyle choices. 

Discussion 

Though exercise is a highly recommended part of a healthy lifestyle, “less than 5% of adults participate in 30 minutes of physical activity each day; only one in three adults receive the recommended amount of physical activity each week.” This is a statistic that is highly concerning because exercise is a known preventative method for diseases such as heart disease, diabetes and more. In fact, “a 2013 noted that higher levels of physical activity were associated with a 21 percent reduction in coronary heart disease events for men and a 29 percent reduction…in women.” On the other side of things, lack of physical activity is “estimated to be the primary cause of approximately 21-25% of breast and colon cancers, 27% of diabetes and approximately 30% of ischaemic heart disease” globally. From this, we can see, through hard data, the importance of including physical activity in our lifestyles. 

According to the CDC, only “12.2% of adults in the USA meet the daily fruit intake recommendations [and] less than 10% of US adults adopt and stick to the recommended vegetable guidelines.” These statistics are made believable when paired with the fact that “117 million adults suffer from one or more chronic diseases due to improper nutrition.” There is a definite connection between our nutritional decisions and the status of our health. However, a healthy diet can mean different things to different people so consider consulting your physician or a nutritionist to see how you can improve your health through your diet.

Conclusion

Clearly, there is hard data behind the reasoning and importance of including physical activity and a nutritious diet into your lifestyle. If you are just starting to make changes in your lifestyle, simple steps such as going for a 30 minute walk or incorporating fruits and vegetables into your diet can be a great first step. If you are looking to improve your health and fitness, you can set goals for yourself such as incorporating a new type of workout into your routine or taking a look at and improving your macronutrient counts. 

 

https://www.hhs.gov/fitness/resource-center/facts-and-statistics/index.html

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3879796/

https://www.euro.who.int/en/health-topics/disease-prevention/physical-activity/data-and-statistics

Recommended Tools for Data Scientists in the Medical Field

Recommended Tools for Data Scientists in the Medical Field

by | Dec 11, 2020 | Analytics, Industry Trends, Technology

Recommended Tools for Data Scientists in the Medical Field

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

Data science is a field that is becoming increasingly applicable to nearly every aspect of our lives. From our online shopping habits to sports analytics, there are vast applications of this field. One application is in healthcare and, more specifically, in disease prediction and management. Each field that data science is applied to has specific tools that are best suited for the desired outcome – in this article we will explore data science tools that are especially relevant to the field of medicine.

Discussion 

One incredibly useful function for medical analytics is Support Vector Machines. SVM’s are algorithms with high accuracy levels that are used for classification and analysis; they are incredibly useful in the processing and learning of images which means they are useful tools when it comes to processing medical images such as x-rays, MRI’s and CT scans. 

Natural Language Processing is also a great tool to have in your toolkit. NLP is a field of artificial intelligence that seeks to decode and understand the human language and patterns within it. This is particularly useful in analyzing a physician’s digitized notes and picking up on commonalities used in their phrasing that can highlight trends in diseases, especially in the early stages of diseases. Natural Language processing is best conducted in Python and that is another great tool to have under your belt as a data scientist in the medical field. 

Another great tool for data science is SQL. SQL is a programming language that is used for relational database management systems. In overlapping the field of medicine and data science, SQL can be a powerful tool in its ability to store, process and execute functions on genomic data. To work with SQL, a researcher might create a database with genetic information and then map data from that database to predict the effect of a drug and isolate a specific gene that causes problems in issuing that drug therapy. They might also use it to isolate certain genes when studying a disease or to make connections between a person’s overall health profile and the health issues they may be facing. 

Conclusion 

Similar to every other application of data science, there are specific tools that are best utilized when working with medical and biological data. The tools we discussed in our article today are tools that are optimized to process, clean and understand data in a medical setting and are great skills for any entrepreneur working in this field to have. To support learning endeavors in this field, we’ve created a short list of resources to help gain traction in picking up these skills: 

Support Vector Machines Explanation and Functions: https://scikit-learn.org/stable/modules/svm.html

Natural Language Processing Explanation: https://becominghuman.ai/a-simple-introduction-to-natural-language-processing-ea66a1747b32?gi=257c3ee25a16 

Python Crash Course: https://www.youtube.com/watch?v=JJmcL1N2KQs 

SQL Crash Course: https://www.codecademy.com/learn/learn-sql

 

New Developments in How Data Science is Helping in the Battle Against Covid-19

New Developments in How Data Science is Helping in the Battle Against Covid-19

by | Dec 4, 2020 | Analytics, Covid, Industry Trends, Technology

 

New Developments in How Data Science is Helping in the Battle Against Covid-19

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

 

Introduction

As we move into the winter months, we are seeing many states hit new and unprecedented records of Covid-19 cases. This is something that has been expected since lockdowns began in March, however, that does not make it any less concerning to healthcare professionals and researchers. In an effort to continue to prevent deaths and long-term side effects of Covid-19, researchers are always looking for new ways to apply artificial intelligence and machine learning to different areas. In today’s article we will take a look at some of these upcoming applications of data science to the battle against Covid-19. 

Discussion 

In a previous article, we detailed the ways in which machine learning can be applied to medical imaging. Due to the fact that machine learning has a great capacity for learning and understanding images and anomalies within them, the application of data science to imaging has potential to help identify Covid-19 cases in x-rays. Researchers at Northwestern Medicine Bluhm Cardiovascular Institute have been working with a machine learning algorithm that was trained by over 17,000 chest x-rays and found that the algorithm could “detect COVID-19 in x-ray images about ten times faster and one to six percent more accurately than specialized thoracic radiologists.” 

Though the algorithm is still in the research phase, it has great potential to be a significant tool and is an open source resource which means that other researchers can contribute and build on top of the existing algorithm.

Another application of data science in the battle against Covid-19 is actually in the development of a vaccine. The pharmaceutical giant, Pfizer, was recently approved in the U.K. to distribute an emergency vaccine and, with a 90% efficacy rate, the United States is likely not far behind. However, researchers at MIT’s Computer Science and Artificial Intelligence Lab have found results in their data that “suggest that the vaccines may not have the same impact among all patient populations” and may leave minority groups, specifically those of Black and Asian descent. This is incredibly important because it affects how minority groups will experience a “post-Covid” world in that they may have to continue to take precautions until a fully effective vaccine has been developed. As artificial intelligence algorithms continue to study these vaccines, they may find that these minority groups are more likely to have a gene or gene sequence that prevents the vaccine from being effective for everyone; if found, researchers can begin to create targeted gene therapies that allow the vaccine to perform its functions effectively. 

Conclusion 

As we continue to try to prevent the spread of Covid-19 across the United States, machine learning and artificial intelligence play an important role in improving our understanding of the virus and its effects on our bodies and society. 

 

Applying Data Science to Nutrition

Applying Data Science to Nutrition

by | Nov 18, 2020 | Health and Wellness, Technology

Applying Data Science to Nutrition

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

 

Introduction 

In all of our articles regarding the application of data science to various diseases, we always include tips for prevention. One tip we always include is to make smart decisions when it comes to nutrition and the foods you ingest. This means avoiding processed when possible and opting for choices that provide your body with necessary and beneficial nutrients. The benefits of a healthy diet are well documented and in this week’s article we will take a look at how data science can improve our understanding of our dietary choices, as well improve our health. 

Discussion 

When it comes to looking at how nutrition affects our health, we are usually looking specifically at the field of nutrigenomics which is essentially the study of the biological processes that take place after the ingestion of a certain food or combination of foods. To conduct tests in this space, a researcher will usually take bodily measurements such as height, weight, health conditions, drug intake and dosage, blood pressure, glucose levels and more. Then, similar to how studies are performed for diabetes or hypertension studies, data sets collected are processed by a range of data science tools, such as “cluster detection, memory-based reasoning, genetic algorithms, link analysis, decision trees, and neural networks.”  

One of the biggest struggles in regards to understanding the results of these studies is that no two bodies are exactly the same so there is a lot of variation when it comes to how certain foods affect our bodies. For this reason, large scale studies must be the norm in this field, especially when it comes to understanding how certain foods affect those with a specific condition.  

One company leading the way in making nutrition a data science related field of study is Nutrino. Nutrino leverages AI and machine learning to understand how measurable nutrition decisions affect user inputs such as “allergies, physical activity, sleep, mood, glucose, and insulin level.” It also takes in user information in regards to preexisting conditions to analyze how dietary decisions affect those users more accurately, as well as to help them manage their chronic conditions. 

Conclusion

The application of the information discovered through the application of data science to nutrition can be extremely impactful for those with chronic conditions by figuring out which foods can best support their health goals. However, this can also be very helpful to the general population and those without chronic conditions by simply helping us better understand how our nutrition decisions impact our lives and can help prevent diseases. As the health and functional foods industry continues to grow faster than ever before, there is certainly market demand for further research in this space. 

 

 

Using Data Science to Increase the Usability of Prosthetics

by | Nov 12, 2020 | Analytics, Industry Trends, Technology

Using Data Science to Increase the Usability of Prosthetics

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction 

Prior to its merge with the field of technology, most prosthetics existed for the purpose of aesthetics and balance but were not functional. However, utilizing new developments in computer engineering and data science has dramatically changed the abilities of prosthetics. Now, prosthetic limbs can help those who need them improve their walking patterns or even hold items. We currently see around one million new amputees daily and that, combined with a proven lack of disability accommodation globally, shows us that there is an important need to continue developing improvements for prosthetics. 

Discussion 

As we have mentioned in previous articles, artificial intelligence and machine learning have demonstrated immense capabilities for image recognition. The codes behind the logic that helps these tools distinguish between certain objects is vital to the usability of prosthetic hands and arms. At Newcastle University further research is being performed to see how to improve this process but the technology is already proven – prosthetic hands/arms can be implemented with technology that helps distinguish between objects in order to help the prosthetic hold or handle the object better. For example, one would hold a teacup differently than a brick so knowing how to, not only tell them apart, but how to handle them differently can make a huge difference to those who need support in that area. 

For those with lower body amputations, the biggest struggle is often in how to walk. Without the ability to walk, many amputees see themselves struggling with several new health issues – lack of exercise can lead to complications like diabetes and heart disease, which also leads to decreased quality of life. Thus, it is highly important for amputees to be able to walk but walking with a struggling gait or unevenly distributed weight can lead to muscle and nerve issues that need to be avoided. Companies like ReWalk are coming out with smart prosthetics that analyze walk patterns and adjust the prosthetics accordingly to improve the impact and smoothness of how its user walks. 

Conclusion 

The fact that this technology exists is amazing in its potential to positively impact millions of people. However, the next step is to make this technology as widely available as possible which means that it needs to clear clinical and regulatory steps so that it can be approved to use by insurance providers. As we look at the legislatures and processes involved in approving new medical technology, it is important to remember that technology is moving at a faster pace than ever and, in general, a lot faster than government processes often happen. Ensuring that technology like this can be available to consumers as soon as possible though, is vital to improving the quality of life for those who need these tools. 

 

 

Data Science in Drug Discovery

Data Science in Drug Discovery

by | Nov 6, 2020 | Analytics, Industry Trends, Technology

Data Science in Drug Discovery

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

Creating new drugs is often a painstaking process. First, the molecular structure of a disease has to be determined, along with its development and processes; then, researchers have to find a target gene or protein that heavily influences the actions of the disease in order to begin creating targeted gene/protein therapy before drug development begins. Next, research regarding absorption, interactions, effects, effectiveness and dosage measuring begins before moving into clinical trials which has several phases. Finally, FDA review and approval must come through before a new drug can go to market. The issue that many people have with drug development is the stage that involves clinical trials and deciding when to move into that stage and this is where data science can be a huge asset in drug discovery. 

Discussion

On average, “it costs up to $2.6 billion and takes 12 years to bring a drug to market.” This number is upsetting to both drug developers and to patients alike who may not survive the wait for the drug or whose lives would be significantly improved if development was sped up. Ultimately, the clinical trial phase could use some modernization. Artificial intelligence and machine learning techniques have already proven their immense capabilities in being able to mimic the processes of the human body which can be leveraged to analyze and understand how a drug interacts with a generally healthy person’s body as well as understanding how it interacts with the body of someone who has certain conditions, such as diabetes or hypertension. This addition means that by the time a drug goes to clinical trials, it has actually already been tested against the human body in some ways. This lowers risk for those participating in clinical trials significantly. Another significant development in this process is that AI can imitate the aging process of a patient as well which means that drugs can be released in small batches to those who absolutely need it while long term trials are still on going.  Mark Ramsey, Chief Data Officer at GSK, says that he hopes this type of mapping can expedite the process from over a decade to less than two years.  Additionally, analysts at McKinsey have estimated that merging AI with drug discovery could “create a value of up to $100 billion.” 

Conclusion

This type of technology can be highly impactful for patients suffering from diseases that could be cured or at least have symptoms eased by drugs still in production. Furthermore, this technology can also be leveraged in the fight against Covid-19 by utilizing test cases for treatment plans and vaccines. 

 

 

What Role Can Analytics Play in Imaging?

What Role Can Analytics Play in Imaging?

by | Oct 29, 2020 | Analytics, Industry Trends, Technology

What Role Can Analytics Play in Imaging?

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction 

X-rays are a key part of many treatment plans because of the valuable information they provide. However, they do come with a small increased risk of certain cancers. While this is not a worrisome point for most people who are having diagnostic imaging performed, it can be of concern for those patients that are monitoring symptoms and need more frequent imaging performed. This creates a demand for more efficient image reading techniques. Artificial intelligence methods have been very successful in image recognition and have become an important and useful tool in improving x-ray readings. Today we will look at the methodologies used in this process, as well as one of the companies leading the way in these endeavors. 

Discussion

How: Applying AI and Machine Learning to imaging happens through intensive training of models. Engineers have to create incredibly specific parameters within their algorithms that tell models how to identify pixelated or 3-dimensional characteristics of abnormalities, such as tumors. Their algorithms are generally focused on finding flagged biomarkers and the methodology is generally supported by support vector machines and random forest. Learning architectures can also be supported by convolutional neural networks that map images and focus on the extraction of key figures/points. All of these methods increase the quality and sensitivity of image readings such that they are more accurate when being processed through an algorithm than they are when read by the human eye, i.e. a radiologist. 

Who: CheXNeXt is an algorithm created by researchers out of Stanford University who sought to increase the accuracy of diagnoses of chest conditions by applying artificial intelligence and machine learning techniques to the imaging process. CheXNeXt works by training its dataset which consists of “112,120 frontal-view chest radiographs of 30,805 unique patients [and] using… automatic extraction method on radiology reports” before training its data. The training process “consists of 2 consecutive stages to account for the partially incorrect labels in the ChestX-ray14 dataset. First, an ensemble of networks is trained on the training set to predict the probability that each of the 14 pathologies is present in the image. The predictions of this ensemble are used to relabel the training and tuning sets. A new ensemble of networks are finally trained on this relabeled training set. Without any additional supervision, CheXNeXt produces heat maps that identify locations in the chest radiograph that contribute most to the network’s classification using class activation mappings (CAMs).” With these datasets CheXNeXt is able to accurately diagnose 14 chest-related diseases with more accuracy than a radiologist. 

Conclusion

Artificial intelligence techniques have not yet made their way into the mainstream. However, initial research and testing suggests that the application of AI and machine learning can have an important impact on the diagnosis of many conditions picked up by x-rays. CheXNeXt is one of a few companies that is leading the way on this initiative and, hopefully, as time goes on we will see applications of this technology to x-rays in search of conditions such as bone cancer, digestive tract issues, osteoporosis and arthritis. Additionally, this is a hopeful step that researchers can reduce the need for repetitive x-rays by making diagnoses happen in a more efficient manner – one in which artificial intelligence supports a radiologist. 

 

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.