How donor selection criteria can be evaluated with limited scientific evidence: lessons learned from the TRANSPOSE project.

Background And Objective: Donor selection criteria (DSC) are a vital link in the chain of supply of Substances of Human Origin (SoHO) but are also subject to controversy and differences of opinion. Traditionally, DSC have been based on application of the precautionary principle.

Materials And Methods: From 2017 to 2020, TRANSPOSE (TRANSfusion and transplantation PrOtection and SElection of donors), a European research project, aimed to identify discrepancies between current DSC by proposing a standardized risk assessment method for all SoHO (solid organs excluded) and all levels of evidence.

Putting the spotlight on donation-related risks and donor safety - are we succeeding in protecting donors?

Background And Objective: The European consortium project TRANSPOSE (TRANSfusion and transplantation: PrOtection and SElection of donors) aimed to assess and evaluate the risks to donors of Substances of Human Origin (SoHO), and to identify gaps between current donor vigilance systems and perceived risks.

Materials And Methods: National and local data from participating organizations on serious and non-serious adverse reactions in donors were collected from 2014 to 2017. Following this, a survey was performed among participants to identify risks not included in the data sets.

Why do young men lapse from blood donation?

Background And Objectives: Most men have larger blood volumes and iron stores, making them more suitable blood donors; however, women dominate the donor population in Stockholm. Motives for cessation and returning were examined in a group of lapsing young male donors, in order to improve retention.

Methods: Demographic studies of the donor population.

Detecting Protected Health Information in Heterogeneous Clinical Notes.

To enable secondary use of healthcare data in a privacy-preserving manner, there is a need for methods capable of automatically identifying protected health information (PHI) in clinical text. To that end, learning predictive models from labeled examples has emerged as a promising alternative to rule-based systems. However, little is known about differences with respect to PHI prevalence in different types of clinical notes and how potential domain differences may affect the performance of predictive models trained on one particular type of note and applied to another.

Prevalence Estimation of Protected Health Information in Swedish Clinical Text.

Obscuring protected health information (PHI) in the clinical text of health records facilitates the secondary use of healthcare data in a privacy-preserving manner. Although automatic de-identification of clinical text using machine learning holds much promise, little is known about the relative prevalence of PHI in different types of clinical text and whether there is a need for domain adaptation when learning predictive models from one particular domain and applying it to another. In this study, we address these questions by training a predictive model and using it to estimate the prevalence of PHI in clinical text written (1) in different clinical specialties, (2) in different types of notes (i.

Identifying adverse drug event information in clinical notes with distributional semantic representations of context.

For the purpose of post-marketing drug safety surveillance, which has traditionally relied on the voluntary reporting of individual cases of adverse drug events (ADEs), other sources of information are now being explored, including electronic health records (EHRs), which give us access to enormous amounts of longitudinal observations of the treatment of patients and their drug use. Adverse drug events, which can be encoded in EHRs with certain diagnosis codes, are, however, heavily underreported. It is therefore important to develop capabilities to process, by means of computational methods, the more unstructured EHR data in the form of clinical notes, where clinicians may describe and reason around suspected ADEs.

Handling Temporality of Clinical Events for Drug Safety Surveillance.

Using longitudinal data in electronic health records (EHRs) for post-marketing adverse drug event (ADE) detection allows for monitoring patients throughout their medical history. Machine learning methods have been shown to be efficient and effective in screening health records and detecting ADEs. How best to exploit historical data, as encoded by clinical events in EHRs is, however, not very well understood.

Finding Cervical Cancer Symptoms in Swedish Clinical Text using a Machine Learning Approach and NegEx.

Detection of early symptoms in cervical cancer is crucial for early treatment and survival. To find symptoms of cervical cancer in clinical text, Named Entity Recognition is needed. In this paper the Clinical Entity Finder, a machine-learning tool trained on annotated clinical text from a Swedish internal medicine emergency unit, is evaluated on cervical cancer records.

Detection of healthcare-associated urinary tract infection in Swedish electronic health records.

The prevalence of healthcare-associated infections (HAI) stresses the need for automatic surveillance in order to follow the effect of preventive measures. A number of detection systems have been set up for several languages, but none is known for Swedish hospitals. We plan a series of infection type specific programs for detection of HAI in electronic health records at a Swedish university hospital.

Abbreviations in Swedish Clinical Text--use by three professions.

A list of 266 abbreviations from dieticians' notes in patient records was used to extract the same abbreviations from patient records written by three professions: dieticians, nurses and physicians. A context analysis of 40 of the abbreviations showed that ambiguous meanings were common. Abbreviations used by dieticians were found to be used by other professions, but not always with the same meaning.

Cue-based assertion classification for Swedish clinical text--developing a lexicon for pyConTextSwe.

Objective: The ability of a cue-based system to accurately assert whether a disorder is affirmed, negated, or uncertain is dependent, in part, on its cue lexicon. In this paper, we continue our study of porting an assertion system (pyConTextNLP) from English to Swedish (pyConTextSwe) by creating an optimized assertion lexicon for clinical Swedish.

Methods And Material: We integrated cues from four external lexicons, along with generated inflections and combinations.

Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: an annotation and machine learning study.

Automatic recognition of clinical entities in the narrative text of health records is useful for constructing applications for documentation of patient care, as well as for secondary usage in the form of medical knowledge extraction. There are a number of named entity recognition studies on English clinical text, but less work has been carried out on clinical text in other languages. This study was performed on Swedish health records, and focused on four entities that are highly relevant for constructing a patient overview and for medical hypothesis generation, namely the entities: Disorder, Finding, Pharmaceutical Drug and Body Structure.

Using text prediction for facilitating input and improving readability of clinical text.

Text prediction has the potential for facilitating and speeding up the documentation work within health care, making it possible for health personnel to allocate less time to documentation and more time to patient care. It also offers a way to produce clinical text with fewer misspellings and abbreviations, increasing readability. We have explored how text prediction can be used for input of clinical text, and how the specific challenges of text prediction in this domain can be addressed.

Extending the NegEx lexicon for multiple languages.

We translated an existing English negation lexicon (NegEx) to Swedish, French, and German and compared the lexicon on corpora from each language. We observed Zipf's law for all languages, i.e.

Factuality levels of diagnoses in Swedish clinical text.

Different levels of knowledge certainty, or factuality levels, are expressed in clinical health record documentation. This information is currently not fully exploited, as the subtleties expressed in natural language cannot easily be machine analyzed. Extracting relevant information from knowledge-intensive resources such as electronic health records can be used for improving health care in general by e.