The RareDis corpus: A corpus annotated with rare diseases, their signs and symptoms

Rare diseases affect a small number of people compared to the general population. However, more than 6,000 different rare diseases exist and, in total, they affect more than 300 million people worldwide. Rare diseases share as part of their main problem, the delay in diagnosis and the sparse information available for researchers, clinicians, and patients. Finding a diagnostic can be a very long and frustrating experience for patients and their families. The average diagnostic delay is between 6–8 years. Many of these diseases result in different manifestations among patients, which hampers even more their detection and the correct treatment choice. Therefore, there is an urgent need to increase the scientific and medical knowledge about rare diseases. Natural Language Processing (NLP) can help to extract relevant information about rare diseases to facilitate their diagnosis and treatments, but most NLP techniques require manually annotated corpora. Therefore, our goal is to create a gold standard corpus annotated with rare diseases and their clinical manifestations. It could be used to train and test NLP approaches and the information extracted through NLP could enrich the knowledge of rare diseases, and thereby, help to reduce the diagnostic delay and improve the treatment of rare diseases. The paper describes the selection of 1,041 texts to be included in the corpus, the annotation process and the annotation guidelines. The entities (disease, rare disease, symptom, sign and anaphor) and the relationships (produces, is a, is acron, is synon, increases risk of, anaphora) were annotated. The RareDis corpus contains more than 5,000 rare diseases and almost 6,000 clinical manifestations are annotated. Moreover, the Inter Annotator Agreement evaluation shows a relatively high agreement (F1-measure equal to 83.5% under exact match criteria for the entities and equal to 81.3% for the relations). Based on these results, this corpus is of high quality, supposing a significant step for the field since there is a scarcity of available corpus annotated with rare diseases. This could open the door to further NLP applications, which would facilitate the diagnosis and treatment of these rare diseases and, therefore, would improve dramatically the quality of life of these patients.This work was supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of "Fostering Young Doctors Research" (NLP4RARE-CM-UC3M) and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation; the Multiannual Agreement with UC3M in the line of "Excellence of University Professors (EPUC3M17)"; and a grant from Spanish Ministry of Economy and Competitiveness (SAF2017-86810-R)

Extracting diagnostic knowledge from MedLine Plus: a comparison between MetaMap and cTAKES Approaches

The development of diagnostic decision support systems (DDSS) requires having a reliable and consistent knowledge base about diseases and their symptoms, signs and diagnostic tests. Physicians are typically the source of this knowledge, but it is not always possible to obtain all the desired information from them. Other valuable sources are medical books and articles describing the diagnosis of diseases, but again, extracting this information is a hard and time-consuming task. In this paper we present the results of our research, in which we have used Web scraping, natural language processing techniques, a variety of publicly available sources of diagnostic knowledge and two widely known medical concept identifiers, MetaMap and cTAKES, to extract diagnostic criteria for infectious diseases from MedLine Plus articles. A performance comparison of MetaMap and cTAKES is also presented

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

Automated machine learning for healthcare and clinical notes analysis

Machine learning (ML) has been slowly entering every aspect of our lives and its positive impact has been astonishing. To accelerate embedding ML in more applications and incorporating it in real-world scenarios, automated machine learning (AutoML) is emerging. The main purpose of AutoML is to provide seamless integration of ML in various industries, which will facilitate better outcomes in everyday tasks. In healthcare, AutoML has been already applied to easier settings with structured data such as tabular lab data. However, there is still a need for applying AutoML for interpreting medical text, which is being generated at a tremendous rate. For this to happen, a promising method is AutoML for clinical notes analysis, which is an unexplored research area representing a gap in ML research. The main objective of this paper is to fill this gap and provide a comprehensive survey and analytical study towards AutoML for clinical notes. To that end, we first introduce the AutoML technology and review its various tools and techniques. We then survey the literature of AutoML in the healthcare industry and discuss the developments specific to clinical settings, as well as those using general AutoML tools for healthcare applications. With this background, we then discuss challenges of working with clinical notes and highlight the benefits of developing AutoML for medical notes processing. Next, we survey relevant ML research for clinical notes and analyze the literature and the field of AutoML in the healthcare industry. Furthermore, we propose future research directions and shed light on the challenges and opportunities this emerging field holds. With this, we aim to assist the community with the implementation of an AutoML platform for medical notes, which if realized can revolutionize patient outcomes

Enhancing the interactivity of a clinical decision support system by using knowledge engineering and natural language processing

Mental illness is a serious health problem and it affects many people. Increasingly,Clinical Decision Support Systems (CDSS) are being used for diagnosis and it is important to improve the reliability and performance of these systems. Missing a potential clue or a wrong diagnosis can have a detrimental effect on the patient's quality of life and could lead to a fatal outcome. The context of this research is the Galatean Risk and Safety Tool (GRiST), a mental-health-risk assessment system. Previous research has shown that success of a CDSS depends on its ease of use, reliability and interactivity. This research addresses these concerns for the GRiST by deploying data mining techniques. Clinical narratives and numerical data have both been analysed for this purpose.Clinical narratives have been processed by natural language processing (NLP)technology to extract knowledge from them. SNOMED-CT was used as a reference ontology and the performance of the different extraction algorithms have been compared. A new Ensemble Concept Mining (ECM) method has been proposed, which may eliminate the need for domain specific phrase annotation requirements. Word embedding has been used to filter phrases semantically and to build a semantic representation of each of the GRiST ontology nodes.The Chi-square and FP-growth methods have been used to find relationships between GRiST ontology nodes. Interesting patterns have been found that could be used to provide real-time feedback to clinicians. Information gain has been used efficaciously to explain the differences between the clinicians and the consensus risk. A new risk management strategy has been explored by analysing repeat assessments. A few novel methods have been proposed to perform automatic background analysis of the patient data and improve the interactivity and reliability of GRiST and similar systems