Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data.

Objectives: Attention deficit hyperactivity disorder (ADHD) is a prevalent childhood disorder, but often goes unrecognised and untreated. To improve access to services, accurate predictions of populations at high risk of ADHD are needed for effective resource allocation. Using a unique linked health and education data resource, we examined how machine learning (ML) approaches can predict risk of ADHD.

A natural language processing approach for identifying temporal disease onset information from mental healthcare text.

Receiving timely and appropriate treatment is crucial for better health outcomes, and research on the contribution of specific variables is essential. In the mental health domain, an important research variable is the date of psychosis symptom onset, as longer delays in treatment are associated with worse intervention outcomes. The growing adoption of electronic health records (EHRs) within mental health services provides an invaluable opportunity to study this problem at scale retrospectively.

Clinical History Segment Extraction from Chronic Fatigue Syndrome Assessments to Model Disease Trajectories.

Chronic fatigue syndrome (CFS) is a long-term illness with a wide range of symptoms and condition trajectories. To improve the understanding of these, automated analysis of large amounts of patient data holds promise. Routinely documented assessments are useful for large-scale analysis, however relevant information is mainly in free text.

Generation and evaluation of artificial mental health records for Natural Language Processing.

A serious obstacle to the development of Natural Language Processing (NLP) methods in the clinical domain is the accessibility of textual data. The mental health domain is particularly challenging, partly because clinical documentation relies heavily on free text that is difficult to de-identify completely. This problem could be tackled by using artificial medical data.

Temporal information extraction from mental health records to identify duration of untreated psychosis.

Background: Duration of untreated psychosis (DUP) is an important clinical construct in the field of mental health, as longer DUP can be associated with worse intervention outcomes. DUP estimation requires knowledge about when psychosis symptoms first started (symptom onset), and when psychosis treatment was initiated. Electronic health records (EHRs) represent a useful resource for retrospective clinical studies on DUP, but the core information underlying this construct is most likely to lie in free text, meaning it is not readily available for clinical research.

Annotating Temporal Relations to Determine the Onset of Psychosis Symptoms.

For patients with a diagnosis of schizophrenia, determining symptom onset is crucial for timely and successful intervention. In mental health records, information about early symptoms is often documented only in free text, and thus needs to be extracted to support clinical research. To achieve this, natural language processing (NLP) methods can be used.

Supervised methods to extract clinical events from cardiology reports in Italian.

Clinical narratives are a valuable source of information for both patient care and biomedical research. Given the unstructured nature of medical reports, specific automatic techniques are required to extract relevant entities from such texts. In the natural language processing (NLP) community, this task is often addressed by using supervised methods.

Automatic Processing of Anatomic Pathology Reports in the Italian Language to Enhance the Reuse of Clinical Data.

Medical reports often contain a lot of relevant information in the form of free text. To reuse these unstructured texts for biomedical research, it is important to extract structured data from them. In this work, we adapted a previously developed information extraction system to the oncology domain, to process a set of anatomic pathology reports in the Italian language.

Information extraction from Italian medical reports: An ontology-driven approach.

Objective: In this work, we propose an ontology-driven approach to identify events and their attributes from episodes of care included in medical reports written in Italian. For this language, shared resources for clinical information extraction are not easily accessible.

Materials And Methods: The corpus considered in this work includes 5432 non-annotated medical reports belonging to patients with rare arrhythmias.