​Machine learning algorithms are capable of analyzing electrocardiogram (ECG) data to identify arrhythmias and categorize heart disease. A new algorithm combining two-event-related moving averages (TERMA) and fractional Fourier transforms (FrFT) is outperforming state of the art algorithms in detecting heart rhythm peaks.

These two methods combined have achieved accurate detection of P, QRS, and T waves. In an electrocardiogram (ECG or EKG), the PQRST waves represent the heart's electrical activity, with the P wave triggering the beat, the QRS complex causing the ventricles to contract, and the T wave allowing the ventricles to recover and prepare for the next beat. The features of ECG signals can be used to train machine learning models to classify heart disease. The algorithm, with the help of different databases, detects the peaks of heart rhythms and classifies heart diseases.

The algorithm under consideration utilizes TERMA and FrFT to identify heart rhythm peaks effectively. TERMA identifies specific regions of interest to find the desired peaks, whereas FrFT rotates the ECG signals to reveal the peaks' positions. This fusion improves the algorithm by allowing it to detect P, QRS, and T waves with ease. The detection performance of the algorithm surpasses that of existing algorithms, proving the efficiency of the method used for ECG signal analysis.

The proposed algorithm extracts features such as PR and RT durations from the detected peak locations. Age, sex, and other features are used to train machine learning models such as MLP (multilayer perceptron, a type of neural network) and SVM (support vector machine, used for classification and regression analysis). These trained models can predict the presence of heart disease with high accuracy, demonstrating the applicability of machine learning to cardiovascular disease diagnosis and treatment.

​Atrial fibrillation (AFib), also known as irregular heartbeat, is a type of cardiac arrhythmia which causes both the lower and upper chambers of the heart to beat fast, erratically, and out of sync. Persistent atrial fibrillation is one of the main types of AFib, which occurs when an abnormal heart rhythm continues for more than one week. The heart rhythm can either return to normal on its own, or treatment may be required to regulate the heartbeat. Persistent AFib usually begins as short-term AFib or paroxysmal AFib. Some of the major risk factors associated with persistent AFib include old age, high blood pressure, and chronic pulmonary obstructive disease (COPD). Others include smoking, coronary heart disease, and heart valve disease.

Persistent AFib can be treated using medication to stabilize the heart’s rhythm or a procedure known as cardioversion. Cardioversion is performed to restore a normal heartbeat, using either electrical cardioversion or chemical cardioversion. In electrical cardioversion, a defibrillator using handheld paddles is placed on the chest and back to deliver shock to the heart in order to stabilize its rhythm. Chemical cardioversion involves the use of medication whereby drugs are either administered orally or through an IV to control the heart rhythm. The medication can take minutes, hours, or days to take effect.

​Electrocardiogram (ECG) deep learning models have proven to be capable of predicting heart failure in patients. The technology offers a potential solution for early intervention and improved patient outcomes. However, studies have shown disparities in the accuracy of these models based on the backgrounds of the patients. As this deep learning model machine becomes integrated into clinical practice, it is vital to address these disparities, which are the result of differences in patients’ age, sex, and race.

The primary model declined with increased patient age and performed significantly worse in Black patients from birth to 40 years of age when compared to other racial groups, with it being most pronounced among young Black women.

Despite efforts to address disparities in model performance by integrating race, sex, and age, the disparities persisted. However, using a customized probability threshold tailored to an individual's race, sex, and age led to an enhanced F1 score.

According to a report on the Stanford Medicine website, Dr. Sanjiv Narayan and other medical practitioners concluded that the results of this study highlight the risk of increasing existing health disparities through the development of these models for heart failure prognosis. To minimize this risk, practitioners can explore the use of personalized probability thresholds based on individual patient factors, leading to more inclusive and efficient healthcare solutions.

​For a long time, the health care sector relied solely on humans to deliver patient care, utilizing their medical knowledge, experience, and physical observations. However, in recent times, health care has undergone a significant revolution thanks to the advent of machine learning, which enables more precise and effective treatments. There are several use cases for machine learning in health care.

One notable application of machine learning in health care is the development of predictive modeling. Predictive modeling involves training algorithms with past data, allowing them to identify patterns and make predictions using historical data. This facilitates the easy identification of patients at higher risk of specific diseases and suggests effective treatments based on historical data. For instance, machine learning can identify patients at a higher risk of developing sepsis by analyzing vital signs, laboratory values, and medical history.

Doctors can also leverage machine learning to enhance treatment accuracy. Machine learning models can analyze patient data and assist doctors in determining the most suitable treatment plan. A machine learning-based medical support tool can take into account factors such as a patient's age, weight, and allergies, guiding clinicians in selecting the most effective treatment for an antibacterial infection.

Machine learning also contributes to improving interactions between patients and doctors. This includes using chatbots and virtual assistants to promptly respond to patients' inquiries. By considering essential data like blood glucose levels or lung function, these chatbots can provide patients with personalized tips on managing their conditions. This can effectively reduce the workload of health care professionals.

Sanjiv Narayan, MD PhD, is a Professor at Stanford University who is an expert in machine learning in healthcare. He has researched and published on this topic for several years, in order to find better treatments for patients. His particular focus is on the use of machine learning for patients with heart rhythm conditions.

​Machine learning refers to a set of techniques and tools that enable computers to learn and adapt independently. It has found applications in various industries, such as enhancing social media, product recommendations, and image recognition.

Software engineers are leveraging machine learning algorithms to develop appealing features that enhance the user experience on social media platforms. For instance, machine learning algorithms on platforms like Facebook and TikTok can track a user’s activities, including chats, likes, comments, and time spent on specific types of posts. The data allows the algorithm to suggest suitable friends and pages for the user’s profile.

E-commerce websites use machine learning to analyze potential client behaviors based on past purchases, search patterns, and cart history. Machine learning algorithms can tailor product recommendations to each user's interests by tracking this information.

Furthermore, image recognition is a notable application of machine learning. This technique involves digitally cataloging and detecting features or objects in images. Advanced analyses like pattern recognition, face detection, and facial recognition use image recognition.

Dr. Sanjiv Narayan (@S_NarayanMD) is a recognized authority in the use of machine learning and artificial intelligence for medicine. He has written on "Use of Artificial Intelligence in Improving Outcomes in Heart Disease" on this link https://www-ahajournals-org.laneproxy.stanford.edu/doi/10.1161/CIR.0000000000001201 and also published extensively on "Machine Learning Primer for Medicine" as in this link. https://academic.oup.com/eurheartj/article/40/25/2058/5366208?login=true

​Ablation is one of the alternative ways to destroy liver tumors. It mainly targets small tumors, about an inch wide, without the need to remove them. It's used when surgery is not possible due to poor health and issues that reduce liver function. Although ablation has lower chances of treating cancer compared to surgery, it can be helpful to some patients or for those waiting for liver transplants. Different ablation techniques for the liver exist, with the variance mainly depending on the medium used to destroy the tumor.

One of the most common ablations for liver cancer, radiofrequency ablation (RFA), uses high-energy radio waves on small tumors. The doctor inserts a thin, long probe through a laceration on the skin to the probe and, guided by a computed tomography (CT) scan or ultrasonic aid, passes a high-frequency wave through the probe tip. The waves heat the tumor and destroy it. RFA procedure can be used solely to kill a tumor or as part of a more extensive operation.

Like RFA, microwave ablation (MWA) uses a probe under CT and ultrasound guidance to introduce microwaves that heat the tumor. However, microwave ablation has several advantages over RFA. MWA is faster and destroys cells faster, can be used for simultaneous tumor ablation, and works on both small and larger tumors, unlike RFA.

A second type of cryotherapy, cryoablation, destroys a tumor by freezing it. The doctor uses a cryoprobe to inject gas like liquid nitrogen, liquid nitrogen oxide, or compressed argon into the tumor. The tumor freezes and dies. Doctors also use ethanol ablation for liver tumors by injecting ethanol or concentrated alcohol. For this method, the patient sometimes requires multiple injections to destroy a tumor.

​Artificial intelligence (AI) has emerged as a pivotal technological advancement in healthcare. One of the most significant impacts of AI in medical research is its ability to enhance diagnostics.

Machine learning algorithms allow AI systems to rapidly and accurately analyze vast medical datasets, facilitating the early detection of diseases such as cancer, cardiovascular conditions, and neurological disorders.

Moreover, AI-driven approaches have ushered in personalized medicine, tailoring treatments to individual patients based on their unique genetic profiles, lifestyle factors, and medical histories. By employing predictive analytics and genomic sequencing, AI algorithms can predict a patient's response to specific therapies, enabling clinicians to prescribe targeted treatments with increased efficacy and fewer adverse effects.

Traditional drug discovery and development methods often face challenges such as lengthy timelines, high costs, and a high failure rate. However, AI algorithms offer a promising solution by swiftly identifying potential drug candidates and predicting their efficacy and safety profiles. By analyzing vast repositories of molecular data, biological pathways, and clinical trial records, AI-powered platforms expedite the identification of novel drug targets and optimize existing therapies.

In clinical settings, AI serves as a valuable tool for providing healthcare professionals with real-time decision support. By integrating patient data from electronic health records, wearable devices, and medical imaging modalities, AI algorithms assist clinicians in diagnosing illnesses, predicting disease trajectories, and recommending appropriate treatment regimens.

​In an era where technology is a part of every aspect of current existence, the fusion of healthcare and technology, known as digital health, has spurred groundbreaking advancements, transforming how we approach medical care. The COVID-19 pandemic accelerated the adoption of telemedicine, allowing patients to receive medical care remotely. As a result, research in telehealth technologies and remote patient monitoring has surged. Innovations in wearable devices, smartphone apps, and remote monitoring systems enable healthcare providers to remotely track patients' vital signs, manage chronic conditions, and provide timely interventions, ultimately enhancing patient outcomes and reducing healthcare costs.

Artificial Intelligence (AI) and machine learning algorithms are revolutionizing healthcare by unlocking insights from vast amounts of data. From diagnosing diseases to predicting treatment outcomes, AI-powered tools streamline decision-making processes and improve the accuracy of medical interventions.

Digital therapeutics are evidence-based interventions delivered through software platforms to prevent, manage, or treat medical conditions. These interventions often leverage cognitive behavioral therapy, mindfulness techniques, or therapeutic gaming to address various health challenges.

Moreover, advancements in genomics and biotechnology are driving the emergence of personalized medicine, tailoring treatments to individuals' genetic makeup, lifestyle factors, and environmental influences. Research endeavors in this domain aim to refine digital therapeutics platforms and harness genomic insights to develop targeted therapies for diverse patient populations.

​Sanjiv M Narayan, M.D., PhD, is an award-winning cardiologist who has worked in many positions in his medical career. A respected researcher, he is a professor of medicine at Stanford University. In San Diego, he gained recognition for his research on heart rhythm disorders. Dr. Sanjiv Narayan has developed outstanding bioengineering inventions in arrhythmia medicine through his research. He also keenly follows transformations in digital health, such as on-demand healthcare.

On-demand healthcare is one of the major tools of digital health. It enables patients to access online healthcare services in real time. On-demand healthcare aims to ensure patients can use their mobile phones from anywhere to access medical services quickly, easily, and remotely. Because of their busy schedules, patients want healthcare services when their schedules can accommodate them.

Due to technological advancement, patients are more willing to allow smart technologies, such as applications take a vital role in their medical care. As a result, waiting times have dropped, allowing efficiency and convenience.