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. RESULTS: The current DSC were assessed using a modified risk assessment method based on the Alliance of Blood Operators' Risk-based decision-making framework for blood safety. It was found that with limited or diverging scientific evidence, it was difficult to reach consensus and an international standardized method for decision-making was lacking. Furthermore, participants found it hard to disregard their local guidelines when providing expert opinion, which resulted in substantial influence on the consensus-based decision-making process. CONCLUSIONS: While the field of donation-safety research is expanding rapidly, there is an urgent need to formalize the decision-making process regarding DSC. This includes the need for standardized methods to increase transparency in the international decision-making process and to ensure that this is performed consistently. Our framework provides an easy-to-implement approach for standardizing risk assessments, especially in the context of limited scientific 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. Finally, participants rated the risks according to severity, level of evidence and prevalence. RESULTS: Significant discrepancies between anticipated donor risks and the collected data were found. Furthermore, many participants reported that national data on adverse reactions in donors of stem cells, gametes, embryos and tissues were not routinely collected and/or available. CONCLUSIONS: These findings indicate that there is a need to further develop and standardize donor vigilance in Europe and to include long-term risks to donors, which are currently underreported, ensuring donor health and securing the future supply of SoHO.

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. An online survey was sent to 1012 lapsing male donors aged 18-35 years. Questions focused on reasons for lapsing, returning and donor motivation. RESULTS: Demographic studies showed a predominance of female donors, especially in younger age groups. Most lapsing male donors were 18-35 years old. In this age group, there was a large turnover of male donors. The most common reason for lapsing was simply falling out of habit despite repeated invitations. Other reasons were lack of time, work, travel, new sex partner and change in residence. Adverse events were of less importance. The majority indicated that they would return for donation if they had more time and/or received yet another invitation. Donors lapsing after a single donation would significantly more often donate again, than repeat donors, if they were given information on the use of their blood. Furthermore, single-time donors found the time spent donating significantly more important. CONCLUSIONS: Increased accessibility and repeated invitations are essential for retention of young male donors. Time constraints are important, suggesting improvements in increased availability as well as shortening of visits. Young men also need help integrating blood donation routines into their life.

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. In this study, we report on the creation of an annotated corpus of Swedish health records for the purpose of learning to identify information pertaining to ADEs present in clinical notes. To this end, three key tasks are tackled: recognizing relevant named entities (disorders, symptoms, drugs), labeling attributes of the recognized entities (negation, speculation, temporality), and relationships between them (indication, adverse drug event). For each of the three tasks, leveraging models of distributional semantics - i.e., unsupervised methods that exploit co-occurrence information to model, typically in vector space, the meaning of words - and, in particular, combinations of such models, is shown to improve the predictive performance. The ability to make use of such unsupervised methods is critical when faced with large amounts of sparse and high-dimensional data, especially in domains where annotated resources are scarce.

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. The study had two aims: to explore how well named entity recognition methods previously applied to English clinical text perform on similar texts written in Swedish; and to evaluate whether it is meaningful to divide the more general category Medical Problem, which has been used in a number of previous studies, into the two more granular entities, Disorder and Finding. Clinical notes from a Swedish internal medicine emergency unit were annotated for the four selected entity categories, and the inter-annotator agreement between two pairs of annotators was measured, resulting in an average F-score of 0.79 for Disorder, 0.66 for Finding, 0.90 for Pharmaceutical Drug and 0.80 for Body Structure. A subset of the developed corpus was thereafter used for finding suitable features for training a conditional random fields model. Finally, a new model was trained on this subset, using the best features and settings, and its ability to generalise to held-out data was evaluated. This final model obtained an F-score of 0.81 for Disorder, 0.69 for Finding, 0.88 for Pharmaceutical Drug, 0.85 for Body Structure and 0.78 for the combined category Disorder+Finding. The obtained results, which are in line with or slightly lower than those for similar studies on English clinical text, many of them conducted using a larger training data set, show that the approaches used for English are also suitable for Swedish clinical text. However, a small proportion of the errors made by the model are less likely to occur in English text, showing that results might be improved by further tailoring the system to clinical Swedish. The entity recognition results for the individual entities Disorder and Finding show that it is meaningful to separate the general category Medical Problem into these two more granular entity types, e.g. for knowledge mining of co-morbidity relations and disorder-finding relations.

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.g. building automated information access systems. We present an annotation model of six factuality levels linked to diagnoses in Swedish clinical assessments from an emergency ward. Our main findings are that overall agreement is fairly high (0.7/0.58 F-measure, 0.73/0.6 Cohen's κ, Intra/Inter). These distinctions are important for knowledge models, since only approx. 50% of the diagnoses are affirmed with certainty. Moreover, our results indicate that there are patterns inherent in the diagnosis expressions themselves conveying factuality levels, showing that certainty is not only dependent on context cues.

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. In this study, we analyze the performance of a predictive model trained on an existing PHI corpus of Swedish clinical notes and applied to a variety of clinical notes: written (i) in different clinical specialties, (ii) under different headings, and (iii) by persons in different professions. The results indicate that domain adaption is needed for effective detection of PHI in heterogeneous clinical notes.

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.e., under different headings), and (3) by persons in different professional roles. It is demonstrated that the overall PHI density is 1.57%; however, substantial differences exist across domains.

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. The Clinical Entity Finder identifies entities of the types body part, finding and disorder and is extended with negation detection using the rule-based tool NegEx, to distinguish between negated and non-negated entities. To measure the performance of the tools on this new domain, two physicians annotated a set of clinical notes from the health records of cervical cancer patients. The inter-annotator agreement for finding, disorder and body part obtained an average F-score of 0.677 and the Clinical Entity Finder extended with NegEx had an average F-score of 0.667.

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. In this study, three strategies for handling temporality of clinical events are proposed and evaluated using an EHR database from Stockholm, Sweden. The random forest learning algorithm is applied to predict fourteen ADEs using clinical events collected from different lengths of patient history. The results show that, in general, including longer patient history leads to improved predictive performance, and that assigning weights to events according to time distance from the ADE yields the biggest improvement.

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. This ambiguity of abbreviations might cause misunderstandings and put patient safety at risk.

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. Also, we aim at detecting HAI for patients entering hospital with HAI from previous care, a task that is not often addressed. This first study aims at surveillance of healthcare-associated urinary tract infections. The created rule-based system depends on acquiring the essential clinical information, and a combination of data and text mining is used. The wide range of diverse clinics with different traditions of documentation poses difficulties for detection. Results from evaluation on 1,867 care episodes from Oncology and Surgery show high precision (0.98), specificity (0.99) and negative predictive value (0.99), but an intermediate recall (0.60). An error analysis of the evaluation is presented and discussed.

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. We used subsets of a clinical corpus in Swedish. We applied four assertion classes (definite existence, probable existence, probable negated existence and definite negated existence) and two binary classes (existence yes/no and uncertainty yes/no) to pyConTextSwe. We compared pyConTextSwe's performance with and without the added cues on a development set, and improved the lexicon further after an error analysis. On a separate evaluation set, we calculated the system's final performance. RESULTS: Following integration steps, we added 454 cues to pyConTextSwe. The optimized lexicon developed after an error analysis resulted in statistically significant improvements on the development set (83% F-score, overall). The system's final F-scores on an evaluation set were 81% (overall). For the individual assertion classes, F-score results were 88% (definite existence), 81% (probable existence), 55% (probable negated existence), and 63% (definite negated existence). For the binary classifications existence yes/no and uncertainty yes/no, final system performance was 97%/87% and 78%/86% F-score, respectively. CONCLUSIONS: We have successfully ported pyConTextNLP to Swedish (pyConTextSwe). We have created an extensive and useful assertion lexicon for Swedish clinical text, which could form a valuable resource for similar studies, and which is publicly available.

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. A text prediction prototype was constructed using data from a medical journal and from medical terminologies. This prototype achieved keystroke savings of 26% when evaluated on texts mimicking authentic clinical text. The results are encouraging, indicating that there are feasible methods for text prediction in the clinical domain.

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., a few phrases occur a large number of times, and a large number of phrases occur fewer times. Negation triggers "no" and "not" were common for all languages; however, other triggers varied considerably. The lexicon is available in OWL and RDF format and can be extended to other languages. We discuss the challenges in translating negation triggers to other languages and issues in representing multilingual lexical knowledge.