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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

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?

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.