Successful Mining of Important Data from Patient Archives with Natural Language Processing

Commentary Medical records in the files of millions of patients across the globe represent a mega-data repository from which we can draw a host of valuable information that might be useful for the evolution of medicine and for improvement in the prevention and diagnosis of disease and patient care in general. Taking advantage of the wealth of this important information and the details kept in the text of the patient records, as discussed in the article by Thirukumaran et al., for the purpose of generating knowledge, is an extremely demanding task to carry out manually. The difficulty of mining what is important in this pool of data and deriving meaningful findings from the narratives written by caring physicians arises from the fact that the data are unstructured and require a labor-intensive and costly human effort as well as resources for searching and unlocking their value. A manual search may also produce variable results because of differences in perceptions among researchers. Similar obstacles are raised by the limitations of the coded records kept by the hospital administrative services. The identification of early signs of disease is useful, allowing us to intervene more effectively, but treating physicians will express and write their observations in their own way. Language is a uniquely human tool for the expression of thoughts and for the communication of sounds, gestures, and words, but it is very personal. The early signs in the patients who developed surgical site infections while hospitalized or in the first 3 months after discharge are probably the most complete and are better documented as free text in the patients’ medical records by the physicians than in the coded records kept by the administrative hospital services. However, one of the barriers is the variable, heterogenous, and probably incomplete expressions utilized by doctors to describe each of these early signs of surgical site infections. As this documentation was primarily created for purposes other than research, it presents many challenges, many of which are beginning to be overcome with the application of computer science artificial-intelligence techniques, such as natural language processing (NLP) and machine learning. The advent and the extremely fast pace of the progress of computing have led us to an era in which human language has become the basis of technology tools used by consumers. Progress in computer science and the developments of artificial neural networks and deep learning as elements of artificial intelligence have made available computer algorithms enabling a machine to learn how to perform the mining of written data on a large scale, using stop words and employing semantics and lemmatizations that help computers to communicate and understand the human language. The applications of similar technologies are part of our daily routine when we are using keyword searching, speech-recognition software, spell-checking, machine translation, or even video recognition. NLP is a rapidly developing field combining computer science and computational linguistics. Program designers have created algorithms to teach machines how to read and comprehend through use of linguistic patterns and natural language models, which are formulated to convert text into a structured representation and, thus, enable computers to derive meaning from the input of written natural language of humans to understand whole expressions. To carry out this task, the specialists must provide appropriate rules and programming. Deep learning technology is making a breakthrough in the field of NLP, now applied in the field of medicine and, in extension, in orthopaedic surgery for the extraction of important information hidden in the archived medical records. In this well-structured study, the NLP model, based on specifically designed rules to identify surgical site infections inside the text of medical records, achieved reproducible results comparable with those of a manual abstraction process. The results were also superior to mining through coded administrative data models. The authors have proven the feasibility and the value of the concept and highlighted the successful and effective implementation and the benefits of using NLP over other traditional methods for the identification of surgical site infections. Studies addressing numerous research questions in the field of other specialties, with appropriately pre-formed rules to mine information in registries, or across multiple centers, may also prove the cost-effectiveness of this technology. The expanding use of electronic medical records integrated with valid NLP models will unlock and unravel the hidden wealth of important information in the records of both inpatients and outpatients, which, after analysis, could improve several aspects of health-care systems, such as prevention of infection and implementation of surveillance for the benefit of our patients.