By Bart Copeland
Open source is powering significant innovation in machine learning (ML). And many are choosing Python for their ML initiatives. Let’s look at three use cases for Python-based ML in healthcare.
ML and Python in healthcare
ML was first applied to tailoring antibiotic dosages for patients in the 1970s. But with the increased volume of Electronic Health Records (EHR) and the explosion in genetic sequencing data, healthcare’s interest in ML is now at an all-time high.
According to McKinsey Research, big data and machine learning in pharma and medicine could generate a value of up to $100 billion annually. That’s based on better decision-making, optimized innovation, improved efficiency of research and clinical trials, and the creation of new tools for physicians, consumers, insurers and regulators.
How does Python fit into this picture? It’s the go-to language for many developers, ranking as one of the most popular programming languages. And it’s used widely across various tech disciplines, from data engineers to web programmers. Python’s rising popularity includes data science and ML. And the emergence of open source language automation presents tremendous opportunities in healthcare for Python-based ML. And, by using open source language automation, Python language builds can be built in minutes with specific ML packages and be vetted for compliance with security and license criteria.
Python now features the bulk of all open source ML and data engineering tools. Developers can use the language to efficiently build innovative solutions while ensuring that code is secure throughout the life-cycle of the applications.
Use Case 1: Predicting Disease Prognosis
Predicting how diseases will progress is more guesswork than science. Existing solutions help improve patient treatment by better predicting disease prognosis. But these solutions are either too costly or too time-consuming for widespread use. What’s needed is a solution that provides better predictions, more cheaply and more quickly.
Enter predictive prognosis, Python-based MLused for solutions such as predicting the mortality of a patient within 12 months of a given date based on their existing EHR data.
Use Case 2: Diagnostics
The National Academy of Sciences found that up to 10% of all patient deaths and between 6% and 17% of all hospital complications are due to diagnostic errors. ML is one potential solution, particularly when applied to image recognition in oncology and pathology. In addition, ML has also been shown to provide diagnostic insights when examining EHRs.
ML successfully analyzes medical images about 92% of the time, compared to senior clinicians at 96%. However, when ML diagnoses are vetted by pathologists, a 99.5% accuracy rate is achieved. And even more promising is the use of ML to provide diagnoses based on multiple images, such as Computerized Tomography , Magnetic Resonance Imaging and Diffusion Tensor Imaging scans. The human brain has a hard time integrating these different views into a whole, but ML solutions were better be able to process each unique piece of information into a single diagnostic outcome.
Use Case 3: Hospital and Patient Care Management
Doctors need to identify patients who are not following their treatment protocol. Patients undergoing surgery need skilled staff to care for them, sometimes around the clock. And cost control has become critical to sustainability due to the budget and personnel constraints of hospitals and clinics.
The solution? Use Python to create a Deep Neural Network (DNN) using Pytorch and Scikit-Learn in order to predict death dates for patients with terminal illnesses. Each patient’s EHR is put into the DNN, including current diagnosis, medical procedures and prescriptions. The DNN provides results that allow doctors to bring in palliative care teams in a timelier manner.
From improved triage for emergency departments to patient surgery and care to predictive inventory management, ML has a role to play. That’s why industry analysts at Accenture estimate that by 2026, the ML health market could potentially save the U.S. healthcare economy $150 billion in annual savings.
The billions of dollars that can be saved and significant improvements to care achievable through ML propel the healthcare field to turn to ML, and do so via the Python programming language. Python, an open source language, is considered by many to be best suited for ML initiatives.
To optimize Python for ML in healthcare, considering how to use, monitor and secure the language code should consider open source language automation. It provides the solution which maximizes Python power for ML in healthcare.
About Bart Copeland:
Bart Copeland is the CEO and president of ActiveState, which is reinventing Build Engineering with an enterprise platform that lets developers build, certify and resolve any open source language for any platform and any environment. ActiveState helps enterprises scale securely with open source languages and gives developers the kinds of tools they love to use. Bart holds a Master of Business Administration in technology management from the University of Phoenix and a mechanical engineering degree from the University of British Columbia.
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