Tag: Healthcare data analysis

Computational Techniques in Medicine
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Computational Techniques in Medicine

What are Computational Techniques?

Computational Techniques are quick, easy, reliable, and efficient methods for solving mathematical, scientific, engineering, geometrical, geographical, and statistical problems. These techniques invariably utilize computers, and hence the name. They are specifically, steps or algorithm-based execution for achieving a solution to the problems. In other words, computational Techniques deliver solutions using mathematical models and Computational Tools.

Suitability of Computational Techniques in Medicine

Computational intelligence tools and techniques can add great value to the Medical and Biomedical industry.   In a sense, computational intelligence could be considered a complementary toolbox to standard Operational Research (OR) methods and techniques for optimization, problem-solving, and decision-making.

As a result, the computational have become the method of choice for problems and areas having specific characteristics, as mentioned below:

  • High degree of complexity,
  • Linguistic representation of concepts or decision variables,
  • High degree of uncertainty, Lack of precise or complete data sets, etc.

Applications:

The following paragraphs discuss applications computational techniques in Medical and Biomedical industry.

Computational Medicine

Computational Medicine aims to advance healthcare by developing computational models of disease, personalizing these models. This Personalization is achieved using data from patients, and applying these models to improve the diagnosis and treatment of disease. The personalized patient models can discover:

  • Novel risk biomarkers,
  • Predict disease progression,
  • Designs optimal treatment,
  • Identify new drug targets for treating cancer, cardiovascular disease, and neurological disorders.

Computational Techniques in Drug Discovery

Computer-Aided Drug Design (CADD) Technique significantly decreases the number of compounds necessary to screen. Interestingly CADD achieves this while retaining the same level of lead compound discovery. Many compounds that are predicted to be inactive can be skipped, and those predicted to be active can be prioritized. Thereby reducing the cost and workload of a full high-throughput screening (HTS) without compromising lead discovery. Additionally, traditional HTS assays often require extensive development and validation before they can be used. CADD requires significantly less preparation time. Hence the experimenters can perform CADD studies while thetraditional HTS assay is being prepared.

Finally, the fact that both of these tools can be used in parallel provides an additional benefit for CADD in a drug discovery project. It is capable of increasing the hit rate of novel drug compounds because it uses a much more targeted search than traditional HTS and combinatorial chemistry. It not only aims to explain the molecular basis of therapeutic activity but also to predict possible derivatives that would improve activity.

Nuclear Medicine and Radiotherapy

Modeling and simulation in radiation-related practices are becoming more and more popular. As a result, various algorithms, codes, and software have been developed for the same. For example, researchers are using the Monte Carlo method, role model the interaction of photons, electrons, positrons, and neutrons with the environment. Interestingly the approach provides most accurate representation of dose distributions in the patient and phantom calculations.

Furthermore, the techniques are extend their applications in nuclear medicine.

Therapeutic Decision-Making

The current paradigm for surgery planning for the treatment of cardiovascular disease relies exclusively on diagnostic imaging data. Firstly, the data defines the present state of the patient. Secondly, the Empirical data can be helpful to evaluate the efficacy of prior treatments for similar patients and to judge a preferred treatment. Owing to the individual variability and inherent complexity of human biological systems imaging and empirical data alone are insufficient to predict the outcome of a given treatment for an individual patient. As a result, the physician utilizes computational tools to construct and evaluate a combined anatomic/physiologic model to predict the outcome of alternative treatment plans for an individual patient. 

The predictive medicine paradigm is implemented in a software system developed for Simulation-Based Medical Planning. This system provides an integrated set of tools to test hypotheses regarding the effect of alternate treatment plans on blood flow in the cardiovascular system of an individual patient. It combines an internet-based user interface developed using Java and VRML, image segmentation, geometric solid modeling, automatic finite element mesh generation, computational fluid dynamics, and scientific visualization techniques. And thus devise a proper plan for the treatment of the patient.

Prediction, Prevention, Diagnosis, and Treatment of Neurodegenerative Diseases

Neurodegenerative disorders, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and Amyotrophic lateral sclerosis (ALS), are formidable clinical illnesses whose diagnosis, treatment, and prognosis are complex. As a result, no effective treatment for AD has been found so far. With the assistance of biomarkers identified by computational methods, neurologists can diagnose the disease at its early stage.

Similarly, based on next-generation sequencing (NGS) technologies, the risk gene loci and proteins can be detected with the help of computational technologies.

When these techniques are accompanied by Magnetic Resonance Imaging (MRI) technology, clinicians can improve or assure their diagnosis and classification of neurodegenerative disorders.

All in all, appropriate bioinformatics tools can help biologists to explore the etiology of neurodegenerative diseases. The etiology may shed light on the underlying mechanisms of brain impairment. In addition, some biomarkers can promote drug repurposing as well as de novo drug design.

Conclusion

Computational Methods have been continuously progressing in all fields and especially in the Field of Medicine. Right from the development of new techniques for developing and designing medicine for various treatments. To advancements in therapies like laser surgery, and robot hands for surgeries. To making clinical and treatment decisions using the data and computational methods.

In conclusion, Computational Methods have become an integrated part of many fields and especially Medicines. And we foresee more development to come along the way of Computational Methods in Medicine.

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Predictive Analytics in Healthcare

Introduction to Predictive Analytics in Health Care

Predictive analytics in Healthcare has had a huge impact on the healthcare system and finds a great many applications driving innovations related to patient care. The purpose of this blog is to apprise you of the wonders predictive analytics is doing in the patient-care.

“Predictive analytics is a branch of Data Science that deals with the prediction of future outcomes. However, it is based on the analysis of past events to predict the future outcomes.”

Talking about predictions has always fascinated mankind since time immemorial. Nostradamus set forth prophecies about catastrophes, disease, health, and well-being. Who would have known that this art of foretelling could transform into a Science, Predictive Analytics!

Advantages of the applications of predictive analytics in healthcare.

  • Predict curable diseases at the right time.
  • Predict pandemic and epidemic outbreaks.
  • Mitigate the risks of clinical decision making.
  • Reduce the cost of medical treatments.
  • Improve the quality of patient life.

“Patient-care has quite a transitioned from relying on the extraordinary ability of a physician to diagnose and treat diseases to the use of sophisticated and  state-of-the-art technology to provide innovative patient care”

For the matter of discussion, the applications of predictive analytics in healthcare have been divided into three aspects of patient care.

  1. Diagnosis
  2. Prognosis
  3. Treatment

Use of predictive analytics in medical diagnosis

Early detection of cancer

Many Machine Learning algorithms are being used by clinicians for the screening and early detection of precancerous lesions. QuantX (Qlarity Imaging) is the first USFDA approved ML breast cancer diagnosis system for predictive analytics. This computer-aided (CAD) diagnosis software system assists radiologists in the assessment and characterization of potential breast anomalies using Magnetic Resonance Imaging (MRI) data. Another image processing ML application is developed by the National Cancer Institute (NCI) that uses digital images taken of women’s cervix to identify potentially cancerous changes that require immediate medical attention.

Predisposition to certain diseases

Predictive analytics has a huge potential to determine the occurrence and predisposition of genetic and other diseases. This domain leverages the data collected from the human genome project to study the effect of genes linked to certain disorders. This is known as pleiotropic gene information. Many such models have been developed to determine the risk of manifesting diseases like osteoporosis, diabetes, hypertension, etc., in the later stages of life.

Prediction of disease outbreaks

The prediction of disease outbreaks that could eventually turn epidemic and pandemic is an indispensable tool for emergency preparedness and disaster management. Many lives could be saved if the outbreak of such diseases is known to us in the first place. However, the efforts of researchers modeling the spread of deadly diseases like Covid19, Zika, and Ebola viruses have yet to bear the fruit of success. The most probable reason could be the complexities in the data collection procedures and the highly dynamic nature of the pathogens like viruses.

Use of predictive analytics in disease prognosis.

Deterioration of patients in ICU

The predictive algorithms developed from continuous monitoring of the vital signs of a patient are used to predict the probability of the patient deterioration and need for immediate intervention in the next 1 hour or so. It is well established that early intervention has a huge success in preventing patient deaths. These predictive algorithms are also used in the remote monitoring of patients in intensive care units (ICU). The remote monitoring of patients, also known as Tele-ICU, is highly effective for aiding intensivists and nurses during situations like Covid19 when the healthcare system is pushed to the limit.

Reducing hospital stays

Prolonged hospital stay and readmission rates are very expensive in the patient’s pockets. The analysts are constantly looking at the patient data to monitor the patient prognosis to treatment that averts any unwarranted hospital stay. The effect of the future outcomes on patient health can also be determined to customize the patient-specific treatment modalities that prevent readmissions.

Risk scoring for chronic diseases

Predictive analytical applications have been designed that can identify patients who are at high risk of developing chronic conditions in the early stage of disease progression. The early detection of the disease progression allows better management of the condition. In the majority of the cases, the disease prognosis could be controlled to a great extent to have a significant effect on the patient’s quality of life.

Predictive analytics in treatment of diseases

Virtual hospital settings

Philips developed a concept technology of virtual hospital settings for predictive care of high-risk patients at their homes. This analytics employs data from the medical records of thousands of patients and the medical history of a particular patient (senior) to build predictive models that can identify the patients who are at risk of emergency treatment in the next month. Various devices have been developed that provide alerts for potential emergency treatment and are known as Automatic Fall Detection (AFD). The AFD collects data continuously from the patient’s movements in all directions (using accelerometer sensors) and uses the data to pick the subtle differences between normal gait and potential fall situations. This device has gained so much popularity that Apple added this feature to Apple Watch Series 4.

Digital twins

Another marvel of predictive analytics for patient care is digital twin technology. In this technology, predictive analytics, IoT, and cloud computing tools are used to develop a virtual representation of the human body. The virtual representation mimics the actual biochemical processes in the human body by constantly collecting data from millions of such patients. The data is modeled to project the possible cause of the patient’s symptoms and suggest the most viable treatment modality specific to the patient’s condition. The treatment modality recommended by the twin can be assessed virtually before implementation on the patient and possible complications can be known and averted in the first place.

Conclusion

The adoption of predictive analytics has ushered personalized and patient-centric transformations into the healthcare industry. However, its scope is not limited to patients alone, it has a huge potential to overhaul other areas of the healthcare system like administration, supply chain, engineering, public relations, and so on.

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Data Science
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Data Science in Healthcare Industry

Introduction


Data is everywhere. From small businesses to large multinational organizations, data is used in almost every area of study and work. From the small mathematical problems solved by a child to the complex functions executed in large organizations, data is used almost everywhere.

Data is one of the most important components of any organization, because it assists leaders in making decisions based on absolute certainty, comprising of facts, statistical results and trends. Any result based on correct and concise data tends to be correct. Data can reveal a lot about an organization, and organizations rely heavily on this data.

Due to the growing relevance and importance of data, data science came into the picture. Data science is a multidisciplinary field. It uses algorithms, scientific procedures and approaches to derive conclusions from massive amounts of data. This data can be either structured or unstructured. In this article, we shall be looking at data science in the healthcare industry.


Data Analytics in Healthcare

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Medicine and healthcare are two of the most important components of our lives. Traditionally, medicine and medical advice was given solely by the doctors based on the patient’s symptoms. However, this was not always accurate and was prone to errors. With the advancements in the field of data science, it is now possible to obtain a more accurate diagnosis. The AI is being used not only as a tool for diagnosis but also for break through discoveries. In a latest advancement Google has achieved a huge success in unfolding protein structures. The very core of the problem that many biochemical Scientists were trying to solve from many decades!

Scientists have also developed ‘DNA Nanopore Sequencer’ which is a tool that helps patients before they suffer from septic shock. It provides genetic sequences mapping, which abbreviates the time span of the information preparing activity. Moreover, this tool recovers genomic information, BAM document controls, and provides calculations.
The new health data science perspective allows applying data analytics, that are collected from various fields, to augment the healthcare sector. There are several areas in healthcare, such as drug discovery, medical imaging, genetics, predictive diagnosis and others which make full use of the results derived at through data science techniques. With ERM’s, clinical trials and internet research, there is so much data being accumulated every day. With the majority of people seeking healthcare advice online, gathering data has become increasingly convenient.

How can it work?

Let us now try to derive an insight into how data science and healthcare can become mutually beneficial.

  1. Data management and Data Governance: The opportunities derived from managing data efficiently are extensive. When data is managed effectively, it makes information easily accessible to all those in the healthcare industry. When data is analysed and shared effectively among doctors and healthcare providers, it will enable them to be more personal and humane in their approach towards treatment. Since the healthcare sector has its fair share of risks, data analytics should always be at the top of its game; it should be up-to-date and acute. The Data related to Medical records, ongoing condition charts of patients, medical database, genetic research, medical image diagnoses can be effectively leveraged to unfold valuable information.
  2. Each patient’s medical records can be combined into one dataset, and then analysed and utilised when needed, to derive at the required conclusions.
  3. Data management also involves data sharing. Data can be shared across several datasets, eliminating the need for excessive office work.
  4. When data is analysed repeatedly, it will bring out any and all errors in clinical data.
  5. Cloud-based clinical software enables faster processing of data, leading to time saved when deciding on treatment or obtaining test results.
  6. Machine learning assists in shortening the process of drug discovery.

Challenges ahead


While data governance has been recognized as crucial to healthcare, there are opportunities to expedite the prioritization of data governance, so that data is accurate, complete, structured, precise and available. Data governance plays a pivotal role in patient engagement, care coordination, and looking after the overall health of the community. If data is not governed properly, different healthcare companies will release inconsistent data which will prove to be a major hindrance. Healthcare data science apps exist in order to avoid such inconveniences.

Workflow Optimization and Process Improvements: Big data analytics is not as profound in healthcare. Hence, certain decisions are taken based on the ‘gut instinct’. Apart from this, lack of coherent healthcare information exchange between the systems and shortage of skilled workers to fill knowledge gaps are other two challenges involved in the process.


Opportunities Genetics/Genomics

  • Treatment personalization: With the introduction of new technologies, including new forms of genomic profiling or sequencing, it provides a new look at the world of genomics. The massive amounts of data today produce genetic data faster than ever. This is partly because the techniques of structuring data, lag behind the ability to actually get the data. Healthcare data science produces copious amounts of data, but that data needs to be made sense of. Some of the challenges in the field of genomics are:
  • Studying human genetic variation and its impact on patients
  • Identifying genetic risk factors for drug response

Opportunities in Medical Imaging

  • Medical Imaging: Medical imaging refers to the process of creating a visual representation of the body for medical analysis and treatment. If is a non-invasive method for doctors to look inside the human body and decide on the required treatment plan. With the swift growth of healthcare and artificial intelligence, this process of medical imaging becomes easier. Some of the types of medical imaging include tomography, longitudinal tomography etc. The primary methods of medical imaging are X-ray computer tomography (CT), PET, and MRI. Medical imaging needs the images to be absolutely accurate. Even minor discrepancies might lead to disastrous results, which can be catastrophic to the patients. The images need to be precisely viewed and interpreted. Data analysis refines these images by enhancing their characteristics like

Opportunities in Predictive Analytics

Predictive analytics refers to a technology that learns from experience, i.e. data, to predict a patient’s behaviour. It builds a connection between the data and the consequent actions which need to be taken based on that data. Predictive analytics allows healthcare to use predictive models or models found specifically in health data science. This allows identification of risks even before they occur. However, there are some drawbacks to predictive analytics.

Predictive analytics is already being used in healthcare manufacturing to meet safety and efficacy requirements of drug products and medical devices.

Opportunities in Drug Research

If we look back to the time of another major pandemic, the Spanish Flu, we see that drugs and vaccines took a considerable amount of time. But now, with the help of data science, data from millions of test cases can be processed within weeks. Development of vaccines and other drugs has become easier and less time-consuming.

How can Let’s Excel Analytics Solutions help here          

We at Let’s Excel develop easy-to-use software interfaces using Artificial Intelligence and Machine Learning algorithms to take healthcare research to next level with data science.  Below is an example of the diagnosis of a tumor as benign or malignant using DataPandit‘s MagicPCA solution.  

Advantages

  • Lesser time taken and more precise outcomes lead to more effective work processes.
  • Healthcare providers and other staff get the chance to perform more tasks in limited time.
  • More effective work processes lead to higher recovery rates, faster reactions to crises and, in turn, less fatal results.
  • Patients get more personalized treatments.

Conclusion

Healthcare has a vast amount of data being generated every day. This data needs to be made sense of, it needs to be structured and organized so that meaningful conclusions can be derived at from the data. The healthcare industry needs to heavily utilize this data so that patients’ lifestyle can improve, diseases can be predicted before their inception. Moreover, with medical imaging analysis, it is now possible for doctors to find even the most microscopic tumours. Doctors can also monitor the conditions of their patients from remote locations.

Data science is already doing wonders for the healthcare industry. It is only a matter of time before it proves itself to be invaluable.

Data Science Journey
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Data Science Journey: Guidance for the New Bee


Considering the fast paced development in the world of Data Science his words are likely to become true. We live in the age of information and it’s quite usual to get overwhelmed with the amount of data we process each day, both in our professional and personal lives. The Internet these days is full of buzzwords related to machine learning, artificial intelligence, deep learning and the Internet of Things. Have you been wondering, if you can really make use of all these techniques in real life? Do you wish to begin your data science journey too? Then read this article to know where you can begin as a new bee!

Bill Gates once said, “A breakthrough in machine learning would be worth ten Microsofts”

Data Science Journey is based on the foundation of mathematical and statistical concepts which are universally applicable to all the sciences. That is the reason why data science is not limited to any specific field of study. It finds applications in numerous fields such as Healthcare, Food and Beverages, Petrochemicals, Agriculture, Defence and Space. To back these claims, let’s take a look at some common applications of artificial intelligence and machine learning in above mentioned fields:

Field NameCommon Applications
HealthcareClassification and Quantification of raw materials: Non-destructive testing of raw materials using spectroscopic sensors like IR, NIR, Raman etc.Distinguish between materials: Innovator Vs. Generic ProductDrug Discovery: Quantitative Structure Activity Relationship, Molecular modellingGenomics: Personalised medicines or dietMedical diagnosis: Cancer PredictionMaterial selection: Composition of materials that results in desired quality
Food and BeveragesAutomating sensory evaluation of productsClassification and Quantification of raw material: Identifying the source of raw materials and nutritional profile of the material (% of carbohydrate, fat and protein)Similarity between materials:Identifying substitute for an ingredientMaterial selection: Composition of materials that results in desired qualityShelf life: When is the product likely to degrade
PetrochemicalsClassification and Quantification of raw materials: Non-destructive testing of raw materials using spectroscopic sensors like IR, NIR, Raman etc.
AgricultureBetter crop yield: Identifying seeds with superior qualityCrop quality/ harvesting: Is it best time to harvest crop Shelf life: Predicting shelf life of harvested cropSoil texture using sensors
Defence and SpaceMaterial selection: Composition of materials that results in desired qualitySpace exploration: Is there water on mars?
Data Science Applications in various fields

I am sure you must have gotten interested in this new age Mantra and be wondering will this be applicable to you and how?

To know this let’s begin by answering below questions:

  • Are you dealing with large sets of data that do not make real sense to the human eye?
  • Are you currently using some tools to sort and analyze your data but still struggling and thus looking for a viable alternative?
  • Have you been told that the buzzwords of machine learning, artificial intelligence or the Internet of Things could solve a problem that you are faced with today?
  •  Are you very much fascinated by this new avenue seen all over the internet, but taking the first steps seem too daunting to make any real progress?
  • Do you believe that, trust is good but evidence is better?
Trust is good, evidence is better.

If you answered yes for any of the above questions, then yes, Data Science Journey is for you! Peter Sondergaard has once famously said that, ‘“Information is the oil of the 21st century, and analytics is the combustion engine”.

The best part is that anyone can use the data science techniques and benefit from them. You need not have to be a coder or an expert mathematician. Various software tools have been developed by experts in the field which can be purchased as per your requirements. 

Our cloud-based DataPandit software solutions is one such simple and user friendly interface developed by Let’s Excel Analytics Solutions.These softwares enable you to get appropriate insights out of your data and lead you in the right direction.

Data science can be learnt not just with theory but with hands-on experience. It can be said that Data Science is a habit, not a skill. The more you practice it, the stronger you get.

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