MLHCA-20: Machine Learning Application in HealthCare |
Abstract registration deadline | August 15, 2020 |
Submission deadline | September 15, 2020 |
Healthcare is an important industry which offers value-based care to millions of people, while at the same time becoming top revenue earners for many countries. Today, the Healthcare industry in India alone earns a revenue of $1 trillion. The US also spends more on healthcare per capita as compared to most other developed or developing nations. Quality, Value, and Outcome are three buzzwords that always accompany healthcare and promise a lot, and today, healthcare specialists and stakeholders around the globe are looking for innovative ways to deliver on this promise. Technology-enabled smart healthcare is no longer a flight of fancy, as Internet-connected medical devices are holding the health system as we know it together from falling apart under the population burden.
From playing a critical role in patient care, billing, and medical records, today technology is allowing healthcare specialists develop alternate staffing models, IP capitalization, provide smart healthcare, and reducing administrative and supply costs. Machine learning in healthcare is one such area which is seeing gradual acceptance in the healthcare industry. Google recently developed a machine-learning algorithm to identify cancerous tumors in mammograms, and researchers in Stanford University are using deep learning to identify skin cancer. Machine Learning (ML) is already lending a hand in diverse situations in healthcare. ML in healthcare helps to analyze thousands of different data points and suggest outcomes, provide timely risk scores, precise resource allocation, and has many other applications. In this article we will discuss some of the top applications of machine learning in healthcare, and how they stand to change the way we visualize the healthcare industry in 2018 and beyond.
In this book, we intend to focus on Healthcae and other medical applications, how can different field of engineering, computer science and technology based research and laboratory give novel concept, investigations, theoretical and practical views to foster positive health attitudes and potential growth in market prospect ? Our target is to analyse various issues related to computing, simulate novel investigations and how they will be beneficial in an insight understanding of the healthcare scenario.
Submission Guidelines
Researchers and practitioners are invited to submit on or before 15th August, 2020 , a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by 31st August, 2020 about the status of their proposals and sent chapter guidelines. Full chapters are expected to be submitted by 01st October, 2020 and all interested authors must consult the guidelines for manuscript submissions. There are no submission or acceptance fees for manuscripts submitted to this book publication, Trust in Knowledge Management and Systems in Organizations. All manuscripts are accepted based on a double-blind peer review editorial process. For manuscripts, we require an e-copy of full chapter submitted in Word on a letter-sized paper with no line numbering, a line spacing of 1.5, Times New Roman 11-point font size, margins top/bottom of 1”, margins left/right of 1.25”, and editable equations using an equation editor in MS-WORD 2010. A typical chapter will include the title, author(s) with affiliation/email/mailing address, introduction, literature review if applicable, theoretical approach if applicable, materials and methods, results and discussion, conclusions, summary, detailed list of keywords, references using numbering system and alphabetical order, any appendix, etc. Also, we prefer a numbering system for citations in the body of a chapter. Tables and figures should be included in the text at right place just after their citation. Specialized chapters need not follow this format.
Researchers and practitioners are invited to submit on or before 15th August, 2020 , a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by 31st August, 2020 about the status of their proposals and sent chapter guidelines. Full chapters are expected to be submitted by 01st October, 2020 and all interested authors must consult the guidelines for manuscript submissions. There are no submission or acceptance fees for manuscripts submitted to this book publication, Trust in Knowledge Management and Systems in Organizations. All manuscripts are accepted based on a double-blind peer review editorial process. For manuscripts, we require an e-copy of full chapter submitted in Word on a letter-sized paper with no line numbering, a line spacing of 1.5, Times New Roman 11-point font size, margins top/bottom of 1”, margins left/right of 1.25”, and editable equations using an equation editor in MS-WORD 2010. A typical chapter will include the title, author(s) with affiliation/email/mailing address, introduction, literature review if applicable, theoretical approach if applicable, materials and methods, results and discussion, conclusions, summary, detailed list of keywords, references using numbering system and alphabetical order, any appendix, etc. Also, we prefer a numbering system for citations in the body of a chapter. Tables and figures should be included in the text at right place just after their citation. Specialized chapters need not follow this format.
List of Topics
1. Identifying Diseases and DiagnosisOne of the chief ML applications in healthcare is the identification and diagnosis of diseases and ailments which are otherwise considered hard-to-diagnose. This can include anything from cancers which are tough to catch during the initial stages, to other genetic diseases. IBM Watson Genomics is a prime example of how integrating cognitive computing with genome-based tumor sequencing can help in making a fast diagnosis. Berg, the biopharma giant is leveraging AI to develop therapeutic treatments in areas such as oncology. P1vital's PReDicT (Predicting Response to Depression Treatment) aims to develop a commercially feasible way to diagnose and provide treatment in routine clinical conditions.
2.Drug Discovery and ManufacturingOne of the primary clinical applications of machine learning lies in early-stage drug discovery process. This also includes R&D technologies such as next-generation sequencing and precision medicine which can help in finding alternative paths for therapy of multifactorial diseases.
3. Medical Imaging Diagnosis
Machine learning and deep learning are both responsible for the breakthrough technology called Computer Vision. This has found acceptance in the InnerEye initiative developed by Microsoft which works on image diagnostic tools for image analysis. As machine learning becomes more accessible and as they grow in their explanatory capacity, expect to see more data sources from varied medical imagery become a part of this AI-driven diagnostic process.
4. Personalized Medicine Personalized treatments can not only be more effective by pairing individual health with predictive analytics but is also ripe are for further research and better disease assessment. Currently, physicians are limited to choosing from a specific set of diagnoses or estimate the risk to the patient based on his symptomatic history and available genetic information. But machine learning in medicine is making great strides, and IBM Watson Oncology is at the forefront of this movement by leveraging patient medical history to help generate multiple treatment options.
5. Machine Learning-based Behavioral Modification Behavioral modification is an important part of preventive medicine, and ever since the proliferation of machine learning in healthcare, countless startups are cropping up in the fields of cancer prevention and identification, patient treatment, etc. Somatix is a B2B2C-based data analytics company which has released an ML-based app to recognize gestures which we make in our daily lives, allowing us to understand our unconscious behavior and make necessary changes
6. Smart Health Records Maintaining up-to-date health records is an exhaustive process, and while technology has played its part in easing the data entry process, the truth is that even now, a majority of the processes take a lot of time to complete. The main role of machine learning in healthcare is to ease processes to save time, effort, and money. Document classification methods using vector machines and ML-based OCR recognition techniques are slowly gathering steam, such as Google's Cloud Vision API and MATLAB's machine learning-based handwriting recognition technology. MIT is today at the cutting edge of developing the next generation of intelligent, smart health records, which will incorporate ML-based tolls from the ground up to help with diagnosis, clinical treatment suggestions, etc
7. Clinical Trial and ResearchMachine learning has several potential applications in the field of clinical trials and research. As anybody in the pharma industry would tell you, clinical trials cost a lot of time and money and can take years to complete in many cases. Applying ML-based predictive analytics to identify potential clinical trial candidates can help researchers draw a pool from a wide variety of data points, such as previous doctor visits, social media, etc. Machine learning has also found usage in ensuring real-time monitoring and data access of the trial participants, finding the best sample size to be tested, and leveraging the power of electronic records to reduce data-based errors.
8. Outbreak PredictionAI-based technologies and machine learning are today also being put to use in monitoring and predicting epidemics around the world. Today, scientists have access to a large amount of data collected from satellites, real-time social media updates, website information, etc. Artificial neural networks help to collate this information and predict everything from malaria outbreaks to severe chronic infectious diseases. Predicting these outbreaks is especially helpful in third-world countries as they lack in crucial medical infrastructure and educational systems. A primary example of this is the ProMED-mail, an Internet-based reporting platform which monitors evolving diseases and emerging ones and provides outbreak reports in real-time.
Publication
Apple Academic Press offers academic researchers, scientists, university faculty, and students high-quality reference and academic content on today’s important topics. Apple Academic Press, Inc., is an independent international publisher focusing on academic and professional books in STEM and other fields. With a focus on relevant content as well as first-class production, Apple Academic Press is dedicated to publishing cutting-edge, informative books written and edited by internationally renowned experts in their fields. Covering fields from nanotechnology to agricultural technology and from hospitality and tourism to chemical engineering and computer science, Apple Academic Press is committed to publishing quality books. Apple Academic Press has partnered with CRC Press, a member of the Taylor & Francis Group, for marketing and distribution worldwide. We are proud to be a member of the International Association of Scientific, Technical & Medical Publishers, please visit http://www.appleacademicpress.com. This publication is anticipated to be released in 2021.
All manuscript submissions to the edited volume should be sent to the mail Id :- parmaastya@gmail.com,sudeshna2529@gmail.com,nitin.rakesh@gmail.com,
rani.astya@sharda.ac.in
Contact
All questions about submissions should be emailed to ...
parmaastya@gmail.com
nitin.rakesh@gmail.com
rani.astya@sharda.ac.in
sudeshna2529@gmail.com