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By Amjad Zia 1, Muzzamil Aziz 2, Ioana Popa 1, Sabih Ahmed Khan 2, Amirreza Fazely Hamedani 2 and Abdul R. Asif 1, *

Understanding published unstructured textual data using traditional text mining approaches and tools is becoming a challenging issue due to the rapid increase in electronic open-source publications. The application of data mining techniques in the medical sciences is an emerging trend; however, traditional text-mining approaches are insufficient to cope with the current upsurge in the volume of published data. Therefore, artificial intelligence-based text mining tools are being developed and used to process large volumes of data and to explore the hidden features and correlations in the data. This review provides a clear-cut and insightful understanding of how artificial intelligence-based data-mining technology is being used to analyze medical data. We also describe a standard process of data mining based on CRISP-DM (Cross-Industry Standard Process for Data Mining) and the most common tools/libraries available for each step of medical data mining.

With the rapid growth in online available medical literature, it is almost hard for readers to obtain the desired information without an extensive time investment. For example, in the ongoing COVID-19 pandemic, the number of publications talking about COVID-19 increased very rapidly. In the first 2 years of the pandemic, there were 228, 640 articles in PubMed, 282, 883 articles in PMC, and 7551 COVID-19 clinical trials listed in ClinicalTrials.gov databases (Data accessed on 16 February 2022), and this is increasing at an amazing speed. Because of the high degree of dimensional heterogeneity, irregularity, and timeliness, these data are often underutilized. This exponential growth in the scientific literature has made it difficult for the researchers to (i) obtain relevant information from the literature, (ii) present information in a concise and structured manner from an unstructured literature pile, and (iii) fully comprehend the current state and the direction of development in a research field.

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The rapidly increasing literature cannot be managed and/or processed using traditional technologies and methods within an acceptable period. This massive volume of data makes it rather difficult for researchers to explore, analyze, visualize, and obtain a concise outcome. The process of extracting hidden, meaningful, and engrossing patterns from unstructured text literature is known as text mining [1]. Traditional text mining techniques are not sufficient to cope with the current large volumes of published literature. Therefore, a rapid increase in the development of new data mining techniques based on artificial intelligence can be seen on the horizon for the benefit of patients and physicians. The inclusion of artificial intelligence (also machine learning (ML), deep learning (DL), and natural language processing (NLP) as the subsets) empowers the data mining process with multifold benefits: Gaining new insights into the decision-making process, processing large dataset with increased accuracy and efficiency, and the ability to learn and improve continuously from the new data.

The current review sheds light on the role of different AI-based methods, i.e., NLP and neural network (NN) in medical text mining, the current data mining processes, different database sources, and various AI-based tools used in the text mining process along with various algorithms. We reviewed the latest text mining approaches, highlighted the key differences between medical and non-medical data mining, and presented a set of tools and techniques currently being used for each step of medical literature text mining. Additionally, we described the role of artificial intelligence and machine learning in medical data mining and pointed out challenges, difficulties, and opportunities along the road.

Human medical data are unique and may be difficult when it comes to mining and analysis. First, due to the fact that humans are the most advanced and the most observed (in-depth) species on the globe, their observation is enriched because humans may provide their sensory input easily compared to the other species on the earth [2]. However, medical data mining faces numerous key challenges, mainly due to the heterogeneity and verbosity of data coming from various non-standardized patient records. Similarly, the insufficient quality of data is also a known issue in medical science that needs to be handled with care for data mining. Such challenges can be met by standardization of the process of selection of patients, collection, storage, annotation, and management of data [3]. However, sometimes this means that existing data and data acquired at multiple centers without good coordination and standard operating procedures (SOPs) could not be used. The major divergence between medical data and non-medical data mining is expected in ethical and legal aspects. The use of information that can be traced back to individuals involves privacy risks, which could result in legal issues. More than fifteen Federal US departments with the US Department of Health and Human Services have issued final revisions to the Federal Policy for the Protection of Human Subjects “the Common Rule, 45 CFR 46, Subpart A” (Protection of Human Subjects, 45 CFR 46 (2018). The federal framework for privacy and security does not apply to the information, which is de-identified or anonymized [4].

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The ownership of medical data is another critical issue, as the data are acquired by different entities where the individuals may have been during their treatment or for diagnostic purposes. These entities can gather and store the data as per the authorization of the individual at the time of data acquisition. However, this permission on consent can be withdrawn by the patient at any time, and/or the consent is only valid for a limited period and data must be erased after this time [5]. Most of the clinical text is produced in a telegraphic way and the information is highly enriched. Additionally, it is written for the clinical staff and colleagues, therefore is full of incomplete sentences and abbreviations. Special tools are required to read, understand, and process this text [6]. Electronic patient records, also known as clinical text, have a unique problem in that they are written in a highly specialized language that can only be processed with a few available tools. Secondly, patient records are sometimes written in a telegraphic and information-dense style for clinician-to-clinician communication, and there exists no developed dictionary for such communications to check grammar and spelling mistakes. In addition, doctors and medical staff frequently use rudimentary sentences and frequently fail to mention the object, such as the patient, because the patient is implied in the text. “Arrived with 38.3 fever and a pulse of 132”, for example, could be written or simply mentioned.

The digital era has shown immense trust and growing confidence in machine learning techniques to increase the quality of life in almost every field of life. This is the case in health care and precision medicine, where a continuous feed of medical data from heterogeneous sources becomes a key enabler for AI/ML-assisted treatments and diagnosis. For instance, AI today can help doctors to bring better patient outcomes with early diagnosis and treatment plans as well as increased quality of life. Similarly, health organizations and authorities also aim for the timely execution of AI routines for the prognosis of outbreaks and pandemics at the national and international levels. Healthcare today is also witnessing the use of AI-aided procedures for operational management in the form of automated documentation, appointment scheduling, and virtual assistance for patients. In this section, we will see some real-life references of AI\\ML tools and technologies currently used in various areas of medical sciences (Table 1).

Before going into further detail, it is worth mentioning that data mining and machine learning concepts go hand in hand and overlap each other to an extent but with a clear distinction of the overall outcome of both technologies. Data mining is the process of discovering correlations, anomalies, and new patterns in a large set of data from an experiment or event to forecast results [7]. The basis of data mining is statistical modeling techniques to represent data in some well-defined mathematical model and then use this model to create relationships and patterns among the data variables. Machine learning, on the