State-of-the-Art Techniques in 
Text Mining for Healthcare Data Analysis and Prediction

Description

The process of searching through vast amounts of unstructured medical data and/or well-organised health records for correlations and associations between facts and data parameters in order to extract important information, supporting data, scientific discoveries, and conclusions that can advance medical practice and/or knowledge is known as healthcare data mining. Finding hidden patterns, connections, and anomalies in massive databases using statistical analysis and machine learning is known as data mining. This information can assist users in understanding complicated occurrences, forecasting the future, and making judgments. The technique of examining past healthcare data to find patterns and trends that might be indicative of future events is known as predictive analytics in healthcare, or simply "predictive analytics healthcare." A machine learning technique called text analysis (TA) is used to automatically extract insightful information from unstructured text input. Businesses utilise text analysis tools to quickly turn papers and web data into insights that can be put to use.

Text mining employs Information Retrieval (IR), Natural Language Processing (NLP), and Information Extraction (IE) techniques to analyse unstructured text data. Information retrieval techniques are employed to first isolate significant data from unstructured data. the most sophisticated and widely applied techniques in a given field, renowned for their exceptional efficacy and performance. Using the prior data as a guide, scientists must forecast the missing data for a new observation in the prediction process. Alternatively said, one could state that the predictive models build a model that can be used to forecast values for new data by using understood outcomes. Modern data analytics makes use of advanced algorithms and powerful computing capacity to reveal trends, correlations, and patterns that are concealed within intricate datasets. Neural networks and decision trees are examples of machine learning algorithms that can automatically learn from data and generate predictions or suggestions. Businesses can forecast future trends and make more educated business decisions by utilising data mining techniques and technologies.

One of the fundamental subfields of data science, data mining employs sophisticated analytical methods to extract valuable insights from large data sets. In particular, predictive modelling makes it possible to predict how the disease will progress, which helps doctors prevent health concerns such medication side effects, treatment resistance that is inherited, and noncompliance with prescribed dosages. Models for predictive analytics are made to evaluate past data, find trends, identify patterns, and utilise that knowledge to forecast future trends. Time series, clustering, and classification models are common predictive analytics models. The method of utilising data to project future results is known as predictive analytics. To identify patterns that might indicate future behaviour, the procedure makes use of statistical models, machine learning, artificial intelligence, and data analysis. The process of converting unstructured text into a structured format in order to find significant patterns and fresh insights is called text mining, often referred to as text data mining. Large textual datasets can be analysed using text mining techniques to uncover hidden links, patterns, and important topics. Articles are invited that explore State-of-the-Art Techniques in Text Mining for Healthcare Data Analysis and Prediction. Case studies and practitioner perspectives are also welcome.

Potential topics include but are not limited to the following:

• Text mining for adverse drug events: the promise, problems, and state of the art.
• Computational intelligence and big data analytics for cyber-physical systems.
• An overview of current research on heart disease prediction systems.
• The Latest Developments in Data Mining Techniques for COVID-19 Pandemic Forecasting.
• Overview of data mining innovations in structural health monitoring at the cutting edge.
• Computational intelligence methods for medical data classification.
• Techniques, Applications, and Challenges of Text Mining in the Health Care Sector.
• Researching artificial neural network developments and potential research areas.
• An extensive text classification system using cutting-edge natural language processing models.
• The review of ultra-precision machining employing text mining at the cutting edge.
• Discovering the key topics and offering suggestions for the way forward.
• A review on the use of data mining techniques for effective disease prediction.

Timeline:

Manuscript submissions due: February 02, 2025
First round of reviews completed: April 20, 2025
Revised manuscripts due: June 10, 2025
Second round of reviews completed: August 20, 2025
 

APC

• The $650.00 USD university rate apples for submissions from currently enrolled students, and $1,150.00 USD for all others
• No waivers will be granted
• Papers must not exceed 10,000 words
• ORCID IDs are required

Submission Details

1. Manuscript Preparation Details
   https://blockchainhealthcaretoday.com/index.php/journal/authors-submission
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3. Download the BHTY Manuscript Template to assist developing your paper at https://blockchainhealthcaretoday.com/index.php/journal/libraryFiles/downloadPublic/6

Submission Portal
Upload your manuscript through the journal Submission Portal at https://blockchainhealthcaretoday.com/index.php/journal/about/submissions
Note: APC will apply unless your university or organization has a Publisher Agreement on file.

Editors-in-Chief

• Jennifer Hinkel, Founder & President, Sigla Sciences, and Managing Director, The Data Economics Company, USA
• Umit Cali, MSc, PhD, Professor of Digital Engineering for Future Technologies, University of York, UK

Lead Editor

• Dr. Jawad Khan, Assistant Professor, Gachon University, Seongnam, South Korea

Additional Theme Issue Editors

• Dr. Muhammad Hameed Siddiqi, Associate Professor, Jouf University, Sakaka, Aljouf, Saudi Arabia
• Dr. Tariq Rahim, Lecturer , Kingston University, Kingston, England
• Dr. Shah Khalid, Assistant Professor, National University of Sciences & Technology, Islamabad, Pakistan