The importance of using machine learning in healthcare

In this article, we will explain what machine learning is, what benefits it brings to the healthcare industry, and which ML approaches to learning ML models are used.

What is machine learning in healthcare?

Machine learning is a powerful tool that teaches computers to learn from experience. By using mathematics, ML algorithms learn independently from data without the presence of machine operators. The more data is available for learning, the better ML algorithms perform. In other words, algorithms find patterns in data, learn from these patterns, and make conclusions based on the identified patterns.

In healthcare, ML algorithms process and analyze large amounts of healthcare data and find insights that are impossible to detect manually. They can also predict outcomes, provide risk assessment, and much more.

ML-based solutions, however, are not meant to replace healthcare professionals. Such tools are designed to assist and to work in conjunction with them.

Whether they are used in drug development or patient treatment, ML solutions are changing healthcare for the better.

How does machine learning work?

To train ML models, several approaches can be used. They differ depending on the data sets provided and extent to which they are pre-processed by humans. Data sets can be unlabeled (raw) or labeled (prepared by data scientists).

Therefore, to choose the right approach, it is important to understand which data will be available, whether data scientists can be engaged to prepare data sets, and what specific problems need to be solved.

Three main approaches to training ML models are used:

• Supervised learning,
• Unsupervised learning,
• Semi-supervised learning.

Supervised learning

Along with gathering data, the supervised learning approach requires a number of preparatory steps so that acquired data can be applied. According to this approach, ML algorithms use provided input data and known responses to do the following:

• Learn patterns between inputs and outputs to predict outcomes from new data.
• Recognize objects and features that enable classifying such objects.

Therefore, supervised learning is mostly used for forecasting and classification issues. In general, supervised learning is considered to be accurate, however there are factors that can affect its accuracy:

• Incorrectly chosen input features can lead to incorrect classification results.
• Incomplete or improbable values used for training affect the accuracy of predictions.

Additionally, this approach requires significant computational and human resources to prepare large amounts of quality data sets.

Despite these limitations, supervised learning is widely used in healthcare. Here are a few examples of supervised learning algorithms and their practical applications:

• Decision Trees can be used as a decision support tool for classifying different types of tumors.
• Support Vector Machine finds its practical application in cancer diagnosis, handwriting recognition, etc.
• Naïve Bayes can be used in recommendation systems and for forecasting radiotherapy outcomes.

Unsupervised learning

In unsupervised learning, ML algorithms use unlabeled input data sets. These algorithms discover patterns or information in raw data without human intervention or given instructions. Unlabeled data for unsupervised learning is obtained through collection and observation.

Due to the absence of human intervention, unsupervised learning algorithms can perform more complicated tasks than supervised learning algorithms: discover virtually all kinds of patterns in data and find features to use for classification.

Typical tasks performed by unsupervised learning algorithms are clustering, dimensionality reduction, and anomaly detection.

In healthcare, unsupervised learning algorithms are applied in areas such as genomic sequence analysis, market research, objective recognition, and correct features (variables) selection for healthcare data pre-processing.

However, when choosing this approach, it is important to remember that the results obtained from unsupervised learning can be unpredictable and need to be carefully interpreted.

Below, we will highlight the distinctions between the approaches and provide recommendations for selecting the best method for you.

Apart from labeled data, there are a number of other differences between supervised and unsupervised learning.

Goals

Supervised learning is aimed at predicting outcomes for new data when the result type is pre-defined. The goal of unsupervised learning is to get insights from new data. Unsupervised learning algorithms themselves define hidden patterns in data sets.

Applications

Supervised learning is ideal for forecasting and predicting tasks. Unsupervised learning is used for detecting anomalies, making recommendations, and interpreting medical images.

Complexity

Even though supervised learning requires preparation, this approach is pretty simple compared to unsupervised learning. To work with unsupervised learning models, which imply large amounts of unclassified data, powerful computational tools are needed. In addition, unsupervised learning models need more data to produce outcomes.

Problems

Unlike unsupervised learning, supervised learning is time-consuming and requires highly skilled data specialists to label data.

On the other hand, human operators need to prove accuracy and applicability of outcomes delivered with the help of unsupervised learning.

To make the right decision, we recommend you to follow these steps:

Unsupervised learning problem

When the amount of data is huge and it is hard to identify relevant features from data, semi-supervised learning comes into play. Data sets for semi-supervised learning contain both labeled and unlabeled data, whereas the percentage of labeled data can be relatively small, e.g. 10 per cent. This approach can be of great help in medical imaging: only MR images of malignant tumors can be labeled, a machine then can detect patients that require attention in this regard.

Healthcare use cases for machine learning

In the following section, you will find real-world examples that show how the healthcare sector might benefit from using ML technologies.

10 Machine Learning in healthcare examples to know

Keeping healthcare records

ML-based tools can facilitate maintaining medical records, in particular, EHRs. This challenging task comprises regular updates of existing data and new data entry. Despite advancements in technology, keeping medical records is still time-consuming and prone to errors due to fractured interfaces and monotonous processes. Smart healthcare record systems driven by ML technologies come with text and medical speech recognition options and advanced analytics tools. Such tools allow for the digitization of paper copies, the conversion of oral communication between patients and doctors into written text, and ensure the maintenance of accurate and up-to-date patient health records.

In addition, these solutions can support healthcare professionals in diagnosis and decision-making.

Patient risk identification

Patient risk prediction is one of the most promising applications of ML in healthcare. By analyzing large amounts of historical patient data, ML models are trained to predict the state of a patient and the likelihood of disease progression in the future. This allows healthcare providers to identify high-risk patients early on and offer targeted care to them.

Personalized treatment

Treatment protocols provide standardized treatment methods for specific conditions. However, patients may often respond differently to the prescribed treatment. ML techniques enable doctors to find patient-specific treatment options based on input data (lab test results, genetics, nutrition, etc.).

Medical imaging

Using machine learning applications in healthcare for high-quality diagnostics

In healthcare, the interpretation of medical images has been traditionally carried out by radiologists and physicians. However, it can be very challenging due to numerous pathologies and subtle changes that may be difficult for the human eye to detect. ML algorithms can be trained to recognize complex malignancy patterns, even in difficult cases or at early stages of disease. As a result, ML tools are becoming particularly helpful in diagnosing and planning efficient treatment.

Clinical trials

Clinical research involves complex, labor-intensive and expensive processes that are prone to errors and deviations. These errors and deviations can lead to the failure of successful trial implementation and acceptance. ML, however, can improve operational and scientific efficiency of clinical trials by facilitating the selection of trial participants, optimizing data collection procedures, and analyzing results.

By utilizing ML-based solutions, researchers can effectively manage large and heterogeneous data sources, identify hidden patterns, and predict the outcomes of clinical trials.

Robotic surgery

When it comes to the application of ML in healthcare, one of the most impactful areas is robotic surgery. ML can be used to cope with various tasks within the field, including but not limited to:

Future of machine learning in healthcare

Robotisation of suturing. Even though suturing is an important part of surgical operations, it can be tedious and exhausting. Automating the suturing process can reduce the duration of surgeries as well as ease the burden on surgeons.

Surgical workflow modeling. Since surgeries today are becoming more and more complex, continuous improvement is necessary. One innovative approach is surgical process modeling, which aims to enhance safety and efficiency in today’s sophisticated procedures. With the help of ML techniques, surgical workflows can be recreated and models can be constructed taking into account factors such as surgeon expertise and patient-specific information, including health status and clinical history. As a result, ML can be of great help in making reliable and timely surgical decisions.

In addition, surgical procedure models can be used for other purposes beyond actual surgery, such as evaluating and training surgeon skills, analyzing workload, introducing new technologies, and predicting procedure duration.

Predicting epidemics

Up-to-date technologies make it possible to forecast epidemic outbreaks based on analysis of vast amounts of data. For prognostication, spread of epidemics in specific areas, along with climatic, demographic, geographic and other factors, is analyzed. This helps identify areas prone to outbreaks and subsequently manage the potential spread of a disease, especially in areas with little or no healthcare facilities.

Clinical decision support systems (CDSS)

CDSS can analyze large volumes of data, suggest the next treatment options, identify potential issues, and enhance the efficiency of care teams. Typically, decisions related to diagnosis are made using limited patient data. In a CDSS, data of an individual patient are matched to a computerized clinical knowledge base, allowing for patient-specific assessments or recommendations. The latest advances in the development of CDSSs help clinicians make better informed diagnosis, recommend treatment options, and evaluate potential outcomes.

Patient behavior modification

Factors like smoking or lack of physical activity can easily trigger the development of chronic diseases or worsen their course. These factors can be managed or avoided by maintaining healthy habits, which requires guidance and adherence.

To help patients adjust their behavior and adopt a healthier lifestyle, ML-based solutions can be very useful. ML algorithms use patients’ input data (e.g. from wearables) related to their daily activities, analyze them, identify habits and routines, and create personalized plans with constant monitoring of effectiveness.

Drug discovery

The discovery of a valuable drug requires significant investments of time and resources . Thousands of molecules and their combinations must be tested before the right composition is identified. However, utilizing machine learning algorithms in healthcare can accelerate this process by quickly and accurately predicting molecular properties, allowing for more efficient identification of potential drugs.

Benefits of machine learning in healthcare

Wrapping up, here are the main advantages that implementation of ML methods in the healthcare sector can bring.

Advantages of machine learning in healthcare

Personalization

One of the most remarkable features of ML in healthcare is the ability to provide services tailored to specific patient needs such as individual treatment options, recommendations, healthy lifestyle plans, etc. As the customer-centric approach is becoming increasingly prevalent in healthcare, creating a personalized experience for each patient is a key goal in the sector, and ML is an extremely valuable tool for achieving this goal.

Innovations

A great example of using ML tools is in research and drug development. By speeding up drug discovery processes, including advanced sequencing methods and cutting-edge manufacturing workflows, we can reduce drug development costs and time-to-market for pharmaceutical companies. This benefits clinicians by providing them with new treatment scenarios for their patients. In addition, ML algorithms can facilitate the selection of clinical trial participants, analyze trial participants data, and reduce data-based errors.

Streamlined processes

The primary importance of implementing machine learning in healthcare is to help overcome disruptions, speed up the workflows, and meet patient demands.

Precise clinical decisions

Machine learning helps clinicians streamline diagnostics and improve decision-making by processing enormous healthcare data sets and making insightful conclusions and predictions in just a few minutes.

Conclusion

Machine learning has proved to have many benefits for healthcare, yet it is still often seen as a technology of the future rather than a real-time solution. However, by identifying the specific needs of your healthcare facility and implementing the appropriate machine learning solution, many of these concerns can be addressed. This is where EffectiveSoft comes in. With our extensive experience in AI and ML research and healthcare software development, as well as our team of top-notch engineers, we are well- equipped to help you with your healthcare project.

Our machine learning team comprises:

• Data analysts who work closely with clients to get insights from their data.
• Data engineers who are responsible for building infrastructure of the software that will include ML models.
• Data scientists whose exceptional skills revolve around developing ML models.
• ML engineers are responsible for determining which ML model to use, as well as what data is required for each model. Additionally, ML engineers work in cooperation with data scientists and deploy ML models built by them.

Our team has a broad range of expertise in ML, from recommendation systems to risk prediction solutions.

Don’t hesitate to contact us. We are happy to help with your healthcare project by leveraging the power of machine learning.

Together with EffectiveSoft, you will be able to unlock the potential of ML for your healthcare facility and your patients.