Listed Key Characteristics Of Cloud Computing
157 days ago
A Strong Determination Of Machine Learning In 2K19
197 days ago
Data Science: A Team Spirit
204 days ago
Python Opens The Door For Computer Programming
Introducing Data Science In Healthcare Industries
There is no more field left by Data Science as with the help of Data Analytics the medical science will move to the new level from computerizing medical records to drug discovery and genetic disease exploration. Data Science and medicine are continuously developing and it is important that they both will advance together.
Here in this article, the different use cases of data science with the highest impact and the most significant potential for future development in medicine and healthcare are discussed:
Medical Science Image Analysis:
The healthcare sector receives great benefits from the data science application in medical imaging. There is a lot of research in this area, and one of the major studies is Big Data Analytics in Healthcare, published in Bio-Med Research International. According to the study, popular imaging techniques include magnetic resonance imaging (MRI), X-ray, computed tomography, mammography, and so on. Numerous methods are used to tackle the difference in modality, resolution, and dimension of these images. Many more are being developed to improve the image quality, extract data from images more efficiently, and provide the most accurate interpretation. The deep-learning based algorithms increase the diagnostic accuracy by learning from the previous examples and then suggest better treatment solutions.
The most popular image-processing techniques focus on enhancement, segmentation, and de-noising that allows deep analysis of organ anatomy, and detection of diverse disease conditions. The most promising applications aim to detect tumors, artery stenosis, organ delineation, etc. Different methods and frameworks contribute to medical imaging in various aspects. Hadoop, a popular analytical framework, employs MapReduce to find the optimal parameters for tasks like lung texture classification. It applies machine learning methods, support vector machines (SVM), content-based medical image indexing, and wavelet analysis for solid texture classification.
Creation of Drugs:
The drug discovery process is highly complicated and involves many disciplines. The greatest ideas are often bounded by billions of testing, huge financial and time expenditure. On average, it takes twelve years to get a drug officially submitted. The data science and machine learning algorithms simplify and shorten this process, adding a perspective to each step from the initial screening of drug compounds to the prediction of success rate based on the biological factors. Such algorithms can forecast how the compound will act in the body using advanced mathematical modeling and simulations instead of the "lab experiments". The idea behind the computational drug discovery is to create computer model simulations as a biologically relevant network simplifying the prediction of future outcomes with high accuracy. It allows choosing, which experiments should be done and incorporates all the new information in a continuous learning loop. Analogous techniques are used to predict the side effects of some particular chemical combinations.
The computational drug discovery also improves the collection and application of different types of historical data during the drug development process. Combining the genetic research with the drug-protein binding databases can bring remarkable results. Moreover, it allows testing of chemical compounds against every possible combination of different cell type, genetic mutation, and other conditions. Using this data, unsupervised learning, and technologies like next-generation sequencing, enables scientists to build models that predict the outcome from a diversity of independent variables.
Predictive medication: prognosis and diagnostic accurateness:
The data science predictive analytics methods learn from historical data and make accurate predictions about the outcomes. They process the patient data, make sense of clinical notes, find the correlations, associations of symptoms, familiar antecedents, habits, diseases, and then make predictions. The impacts of certain biomedical factors such as genome structure or clinical variables are taken into the account to predict the evolution of certain diseases. Common cases include the prognosis of disease progress or prevention to reduce the risk and the negative outcomes. The main benefit is the improvement of the quality of life for patients and the quality of working conditions for doctors.
Handling customer data:
In the data management area, machine learning allows the creation of comprehensive registers of medical data, where all the paperwork will be transferred to a much more promising digital form. The whole medical history of a person will be stored in one system. Techniques like the support vector machines and optical character recognition are great helpers in such digitalization. The constantly improving machine learning algorithms will make it possible to use and exchange the information to aid diagnostics and treatment decisions, a huge contribution using simple data. Next, comes the introduction of electronic cards for each patient, which would be available to every doctor who deals with different cases.
The knowledge management in healthcare is essential for improving the services and providing the best possible treatment. It implies the combination of internal knowledge and externally generated information. The industry is changing rapidly new technologies are being created all the time that require effective gathering, storing, and distribution of various facts. Data science tools ensure the integration of different sources of knowledge and their collective use in treatment processes, which can help the healthcare organizations to achieve progressive results.
Virtual support for patients and customer:
Optimization of the clinical process builds upon the concept that for many cases it is not actually necessary for patients to visit doctors in person. Using a mobile application can give a more effective solution by bringing the doctor to the patient's instead. The AI-powered mobile apps can provide basic healthcare support, usually as chatbots. You simply describe your symptoms, or ask questions, and then receive key information about your medical condition derived from a wide network linking symptoms to causes. Apps can remind you to take your medicine on time, and if necessary, assign an appointment with a doctor. This approach promotes a healthy lifestyle by encouraging patients to make healthy decisions, saves their time on waiting in line for an appointment, and allows doctors to focus on more critical cases.
The machine learning algorithms use natural language processing and generation to provide correct information, create a complex map of the user's condition, and provide a personalized experience. The most popular applications nowadays are Your.MD, Babylon Health, Ada, and so on. This way, the most appropriate customer support is created which obviously cannot fully rely on the machines in healthcare. So, the main task for machine learning is to find the perfect balance between doctors and computers. The key is to automate simple routines, like we have just explained, and give professionals the ability to concentrate on more complicated problems.
Genetics and Genomics:
The research in genetics and genomics enables an advanced level of treatment personalization. The goal is to understand the impact of the DNA on our health and find individual biological connections between genetics, diseases, and drug response. Data science techniques allow integration of different kinds of data with genomic data in the disease research, which provides a deeper understanding of genetic issues in reactions to particular drugs and diseases. Let us review the most popular techniques and frameworks.
MapReduce allows reading genetic sequences mapping and shortens the time for efficient data processing. SQL contributes to retrieving genomic data, BAM file manipulations, and computation. The Deep Genomics made a remarkable impact on predicting the molecular effects of genetic variation essential to DNA interpretation. Their database has enabled the scientists to understand how genetic variations can impact a genetic code. Many challenges remain due to the continuous interactions between genes and the external variables. As soon as we acquire a reliable personal genome data, we will achieve a deeper understanding of the human DNA. The advanced genetic risk prediction will be a major step towards more individual care.