What are the uses of Python programming language in medicine? How can programming with Python help to quickly and optimally diagnose diseases? What features and facilities does the Python language have that other programming languages do not have? Does it have special libraries that are used in medical sciences? Or is the reason for Python’s applications in medicine important and decisive technologies such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) that Python and its libraries played a key role in their development?
The emergence of new developments and technologies in one field spreads to other fields and fields. This is exactly what happened with artificial intelligence and machine learning. A look at the history of the evolution and developments of artificial intelligence shows that scientists were looking for ways to build a machine that simulates the functions of the human brain. In the beginning, they were not intended to contribute to medical science or a specific industry.
But when they succeeded, the knowledge and technologies of artificial intelligence and tools developed with artificial intelligence entered all fields, fields, and industries in a very short time. The invention of the Python programming language was influential and helpful in its success and the expansion of artificial intelligence applications in various fields.
Medical science is one of the most important fields that have been transformed by the introduction of artificial intelligence and artificial intelligence-based tools. In this article, I explain the uses of the Python programming language, which is the most useful and popular programming language in the development of artificial intelligence and machine learning, and I answer the questions posed at the beginning.
It is necessary to pay attention to the fact that this article is not specialized and medical, and in it, general information about the applications of the Python programming language in medicine has been prepared and written for the readers’ familiarity.
Table of Contents
Python in medicine
Information about artificial intelligence the tools that are made with it and the changes it brings in different fields and industries are at the top of the news in the world of technology. Maybe it was after the introduction and entry of ChatGPT into our lives that we better understood the real power of artificial intelligence and the fundamental changes it is going to make in all aspects of our lives.
Tools like ChatGPT answer our questions from data from all over the web. One of the most important things that artificial intelligence does is that it can process, analyze, and categorize a huge amount of data very quickly and accurately, extract the information we need, and deliver it to us as an output.
The mentioned process is basic in Data Science.
Data science is a field of study that deals with huge amounts of data. Data science uses modern tools and techniques to extract hidden patterns and useful information from data and to make business decisions. In data science, complex machine learning algorithms are used to build prediction models.
Data collection and analysis is possible using machine learning algorithms. Data analytics and machine learning specialists train machines (software/programs) to analyze data based on specific patterns and deliver desired output. The first choice of many professionals to perform this process is the Python programming language and its powerful libraries.
Doctors also convert health and healthcare data from paper to electronic formats, which is why hospitals and research centers generate huge amounts of data. Data analysis specialists in collaboration with doctors and medical scientists and using the Python programming language, libraries, and its facilities can produce a diverse set of tools and software for analyzing medical data to diagnose diseases more quickly and accurately and improve the quality of health care and treatment.
The following are among the most important uses of Python in medicine:
- Building machine learning models to predict and diagnose risks, injuries,, and diseases
- Increasing the efficiency of hospital activities
- Genomic studies
- Predictive analytics
Python’s main applications in medicine and healthcare are based on machine learning (ML) and natural language processing (NLP) algorithms. Such applications include medical image analysis and recognition, natural language processing of medical documents, and disease prediction using human genetics.
In the continuation of this article and keeping this introduction in mind, I will explain the applications of Python in medicine and how data scientists and medical science specialists use ML and NLP to solve challenges and improve results in the field of healthcare and health.
Applications of Python and machine learning in the diagnosis of various physical diseases
One of the most important uses of Python in medicine is to use this language to train models (algorithms) that can understand and analyze medical images (such as MRI, CT scan, etc.). With the help of machine learning, software, and programs are created that understand medical images and inform doctors and specialists about abnormalities and possible diseases.
The existence of such programs makes the process of diagnosing the disease faster and, as a result, starting the treatment faster. Therefore, the chances of recovery of patients increase and more people are saved from death.
Python and machine learning are used to detect the following:
- Detecting the presence of disease or injury in medical images
- Diagnosis and classification of tumors
- Diagnosis of cardiovascular abnormalities
- Diagnosis of chest diseases
- Cancer screening
- Prediction and diagnosis of complex genetic diseases
In the following, I will explain briefly and usefully about each case.
1. Detecting the presence of disease or injury in medical images
One of the most promising technological advancements in medical care is the use of ML to analyze multiple medical images such as magnetic resonance imaging (MRI), computed tomography (CT), and diffusion tensor imaging (DTI) to diagnose a variety of diseases. The human brain (doctors) often has difficulty analyzing several images at the same time (like the image below).
That is why machine learning solutions in the simultaneous processing of information from different images can help doctors reach a diagnostic result. The accuracy of machine learning models for image analysis and disease diagnosis trained using Python is about 92%. This value is slightly lower than the 96% accuracy of senior doctors. The diagnostic accuracy of pathologists (pathologists) increases to 99% when they use machine learning models to review images.
In addition, doctors and surgeons can use ML-equipped tools to identify dislocations, fractures, and soft tissue injuries that are difficult to detect. The presence of these tools allows surgeons to make more confident treatment choices. Using algorithms to analyze images helps doctors consider all injuries and choose the best treatment. In other words, AI tools help to comprehensively analyze medical images and provide accurate and timely reports.
2. Diagnosis and classification of tumors
One of the most common applications of machine learning technologies in medicine is to diagnose tumors. Computer-aided diagnosis (CAD) tools are used to diagnose tumors. These tools use CNNs or Convolutional Neural Networks to calculate the probability that a lesion is a lesion or not.
For example, we can mention the role that deep learning (which is a field of machine learning) has in diagnosing a type of brain tumor. Doctors faced challenges to diagnose and classify glioblastoma, which is a type of brain tumor. The problem lies in the invasive nature of these types of tumors. Unlike other brain tumors, these tumors are difficult to find and assess their response to treatment. Using programs equipped with deep learning, I evaluated. R. Glioblastoma is getting easier for doctors.
3. Diagnosis of cardiovascular abnormalities
The use of Python to automatically detect cardiac abnormalities from medical images speeds up the decision-making of doctors and specialists and reduces their diagnostic errors. When a patient comes to the hospital with symptoms such as shortness of breath, doctors often prescribe a chest X-ray.
Artificial intelligence tools help automate radiographic evaluations such as pulmonary artery diameter measurements and carina (trachea) angle measurements, which help to diagnose cardiovascular abnormalities in patients more quickly.
In addition to helping doctors better and more accurately evaluate radiographs and tests, artificial intelligence can predict a person’s risk of heart disease. The figure below is from university research conducted in 2017 with the title: Can machine learning improve cardiovascular disease risk prediction using clinical data? This figure shows how ML can be used to predict cardiovascular diseases by processing and analyzing data related to clinical characteristics of patients (such as gender, high blood pressure, smoking, presence of other diseases, etc.).
4. Diagnosis of chest injuries and diseases
For the treatment of chest diseases (such as pneumonia), the intervention and quick response of the medical staff is needed. Doctors use radiology images to diagnose pneumonia and other lung diseases, such as COVID-19. However, the problem is that radiologists may not always be available to analyze images and write reports for doctors.
The lack of specialized human resources in hospitals and medical centers is dangerous and problematic, especially in critical situations such as the spread of viruses or natural disasters that increase the number of visits to hospitals and medical centers.
In addition to the problem of lack of radiologists, it is difficult for doctors to diagnose pneumonia if the patient has other lung problems. This is where a Python-based AI algorithm can analyze radiology and medical images to diagnose pneumonia and suggest appropriate treatment to doctors.
5. Cancer screening
Oncologists use medical imaging to perform preventive screenings for early detection of cancers such as colon cancer, prostate cancer, and breast cancer. However, in these screenings, radiologists may have difficulty in definitively classifying the tumor as benign or malignant.
If the answer is falsely positive (malignant), the patient is subjected to unnecessary invasive tests or treatments. If it is misdiagnosed as benign, the cancer will progress and it may be too late for treatment. The use of artificial intelligence increases the accuracy of reading medical images and leads to correct and timely diagnosis.
6. Prediction and diagnosis of complex genetic diseases
Another application of machine learning algorithms, which are designed and built using the scientific libraries of the Python programming language, is for genetic analysis to predict, diagnose, and determine the cause of the disease. Finding the relationship between people’s genetic characteristics and the rate of various diseases helps specialists and doctors to start preventive treatments in time.
Finding that connection on the one hand and taking help from artificial intelligence to build models to predict how each disease will evolve, on the other hand, provides doctors with important and decisive information. They can use the data obtained to personalize each patient’s care, treatment, and medication plans.
Having this data and preventive and individual action based on the genetic characteristics of each person will reduce the risk of developing complex genetic diseases and diseases that mutate quickly and unexpectedly. With machine learning, scientists find the patterns and trends of the formation, progress, and mutation of diseases, and using those findings, they create more advanced models for predicting diseases in people.
Therefore, artificial intelligence and programs based on it have the potential to identify people who are at risk of certain diseases (such as cardiovascular diseases, cancers, Alzheimer’s, genetic diseases, etc.).
Applications of Python and Natural Language Processing (NLP) in Medicine
Natural language processing (NLP) is one of the subfields of artificial intelligence. The goal of natural language processing specialists is to teach machines to understand human language, understand the content of words or texts, and respond to words by imitating human language. Natural language processing enables machines to interact with humans in their natural language.
To process natural language or to produce tools and programs in which natural language processing is used, it is not necessary to be an expert in machine learning. If someone has learned the Python programming language, with the help of NLTK (Natural Language Toolkit), which is a Python package for natural language processing; It can process the text it wants, as needed, and output the results in the form of a graph or chart (visualized).
This is why medical professionals use NLP tools to process and analyze a wide variety of data and documents, including patient talk, patient history, and symptom reports. NLP is a cheaper way to quickly scan medical records and integrate the obtained information into a database. Because NLP systems can extract readable data from medical texts and images and identify keywords and terms among them.
NLP has many interesting and diverse applications in healthcare and research in various fields of medical sciences. Next, I introduce some of the most important applications of natural language processing in medicine.
1. Application of NLP to build a clinical decision support system
A clinical decision support system (CDSS) is a type of health information technology that helps doctors and medical staff make faster and more efficient decisions about the treatment process and necessary measures.
Such a system works with natural language processing and several other machine-learning models. The database contains information extracted from doctors’ notes (handwritten or typed) and previous test results about each patient or reference. Doctors give this system the current symptoms of the patient. By extracting the patient’s information from all available records and considering his current symptoms, this system suggests possible disease or diseases and the necessary measures to the treatment staff.
The best example of using natural language processing to help improve decision-making is Amazon’s NLP product called
Amazon Comprehend Medical uses machine learning to understand and extract medical and health data from various medical and therapeutic texts and documents.
2. Predicting the onset of psychosis
In an experiment conducted in 2019 the results of which were published in a research paper titled “A Machine Learning-Based Approach to Predicting Psychosis Using Semantic Density and Hidden Content Analysis”, a group of researchers developed an algorithm to analyze the hidden meaning in human speech using Made from natural language processing.
In this experiment, researchers gave this model audio files (along with text) of interviews with young people who were suffering from psychosis. By analyzing the sentences and the hidden meaning (semantic load) in the use of each word or sentence, this model has correctly identified which of the interviewees are in the initial stages of psychosis.
The success of this research shows that the use of Python and the training of machine learning models (natural language processing) can also help in the early diagnosis of diseases and mental disorders.
3. Using NLP for predictive analytics
NLP and programs equipped with it can predict possible suicides, especially among young people, by analyzing social media posts. According to the report of the World Health Organization, suicide is the fourth cause of death among 15-29-year-olds in the world, and about 700,000 people die by suicide every year.
That is why experts around the world have conducted numerous studies and experiments to find ways to predict and prevent suicide. Training machine learning models to analyze the posts and contents of social networks, including Facebook, to identify people who intend to commit suicide is one of the ways that some researchers worked on it.
In a study titled “Deep neural networks detect suicide risk from Facebook text posts”, the results of which were published in 2020, researchers built a sophisticated model that can analyze texts posted on Facebook. The important thing about this model is that it can identify not only users who have directly talked about suicide, but also those whose texts have indirectly or impliedly indicated their intention to commit suicide.
In the conclusion section of this research, it is stated that
Findings show that machine learning-based analyses of users’ daily activities in social media improve suicide risk prediction and help develop practical diagnostic tools.
Applications of Python to improve hospital management and patient interaction
Managing a hospital and designing a process that makes the interaction of clients and patients with the staff and different departments of the hospital satisfactory is a very difficult task. Hospitals and medical centers have limited financial and human resources. If hospitals and medical centers have applications that regulate the interaction of patients with employees in different departments, the workload of human resources will be reduced and patients will experience more satisfaction.
Python programming language is also used to develop applications and chatbots. Building a chatbot with Python is quick and easy. Applications help patients to
adjust or change doctor or laboratory appointments online,
Find answers to frequently asked questions and ask your questions online.
have their copies in one place,
get the answers to their tests,
call emergency services,
and regularly update their health and wellness data.
If the application has a smart chatbot, it can communicate with the patient like a nurse, talk to him, and guide him.
In addition, by using the collection and processing of financial and management data, artificial intelligence tools and machine learning models can be used to improve budget allocation to different departments, division of work and tasks among employees, and improvement of admission and treatment processes, especially in crises and busy times. Hospitals and medical centers benefited.