A neural network multi-task learning approach to biomedical named entity recognition

$\textbf{Background}$ Named Entity Recognition (NER) is a key task in biomedical text mining. Accurate NER systems require task-specific, manually-annotated datasets, which are expensive to develop and thus limited in size. Since such datasets contain related but different information, an interesting question is whether it might be possible to use them together to improve NER performance. To investigate this, we develop supervised, multi-task, convolutional neural network models and apply them to a large number of varied existing biomedical named entity datasets. Additionally, we investigated the effect of dataset size on performance in both single- and multi-task settings. $\textbf{Results}$ We present a single-task model for NER, a Multi-output multi-task model and a Dependent multi-task model. We apply the three models to 15 biomedical datasets containing multiple named entities including Anatomy, Chemical, Disease, Gene/Protein and Species. Each dataset represent a task. The results from the single-task model and the multi-task models are then compared for evidence of benefits from Multi-task Learning. With the Multi-output multi-task model we observed an average F-score improvement of 0.8% when compared to the single-task model from an average baseline of 78.4%. Although there was a significant drop in performance on one dataset, performance improves significantly for five datasets by up to 6.3%. For the Dependent multi-task model we observed an average improvement of 0.4% when compared to the single-task model. There were no significant drops in performance on any dataset, and performance improves significantly for six datasets by up to 1.1%. The dataset size experiments found that as dataset size decreased, the multi-output model’s performance increased compared to the single-task model’s. Using 50, 25 and 10% of the training data resulted in an average drop of approximately 3.4, 8 and 16.7% respectively for the single-task model but approximately 0.2, 3.0 and 9.8% for the multi-task model. $\textbf{Conclusions}$ Our results show that, on average, the multi-task models produced better NER results than the single-task models trained on a single NER dataset. We also found that Multi-task Learning is beneficial for small datasets. Across the various settings the improvements are significant, demonstrating the benefit of Multi-task Learning for this task.This work was supported by Medical Research Council [grant number MR/M013049/1] and the Cambridge Commonwealth, European and International Trust

Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine.

Background: Word representations support a variety of Natural Language Processing (NLP) tasks. The quality of these representations is typically assessed by comparing the distances in the induced vector spaces against human similarity judgements. Whereas comprehensive evaluation resources have recently been developed for the general domain, similar resources for biomedicine currently suffer from the lack of coverage, both in terms of word types included and with respect to the semantic distinctions. Notably, verbs have been excluded, although they are essential for the interpretation of biomedical language. Further, current resources do not discern between semantic similarity and semantic relatedness, although this has been proven as an important predictor of the usefulness of word representations and their performance in downstream applications. Results: We present two novel comprehensive resources targeting the evaluation of word representations in biomedicine. These resources, Bio-SimVerb and Bio-SimLex, address the previously mentioned problems, and can be used for evaluations of verb and noun representations respectively. In our experiments, we have computed the Pearson’s correlation between performances on intrinsic and extrinsic tasks using twelve popular state-of-the-art representation models (e.g. word2vec models). The intrinsic–extrinsic correlations using our datasets are notably higher than with previous intrinsic evaluation benchmarks such as UMNSRS and MayoSRS. In addition, when evaluating representation models for their abilities to capture verb and noun semantics individually, we show a considerable variation between performances across all models. Conclusion: Bio-SimVerb and Bio-SimLex enable intrinsic evaluation of word representations. This evaluation can serve as a predictor of performance on various downstream tasks in the biomedical domain. The results on Bio-SimVerb and Bio-SimLex using standard word representation models highlight the importance of developing dedicated evaluation resources for NLP in biomedicine for particular word classes (e.g. verbs). These are needed to identify the most accurate methods for learning class-specific representations. Bio-SimVerb and Bio-SimLex are publicly available

Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches.

Background: Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes. Recently several works have used neural networks to create node representations which allow rich inputs to neural classifiers. Preliminary works were done on this and report promising results. However they did not use realistic settings like time-slicing, evaluate performances with comprehensive metrics or explain when or why neural network methods outperform. We investigated how inputs from four node representation algorithms affect performance of a neural link predictor on random- and time-sliced biomedical graphs of real-world sizes (∼6 million edges) containing information relevant to DTI, PPI and LBD. We compared the performance of the neural link predictor to those of established baselines and report performance across five metrics. Results: In random- and time-sliced experiments when the neural network methods were able to learn good node representations and there was a negligible amount of disconnected nodes, those approaches outperformed the baselines. In the smallest graph (∼15,000 edges) and in larger graphs with approximately 14% disconnected nodes, baselines such as Common Neighbours proved a justifiable choice for link prediction. At low recall levels (∼0.3) the approaches were mostly equal, but at higher recall levels across all nodes and average performance at individual nodes, neural network approaches were superior. Analysis showed that neural network methods performed well on links between nodes with no previous common neighbours; potentially the most interesting links. Additionally, while neural network methods benefit from large amounts of data, they require considerable amounts of computational resources to utilise them. Conclusions: Our results indicate that when there is enough data for the neural network methods to use and there are a negligible amount of disconnected nodes, those approaches outperform the baselines. At low recall levels the approaches are mostly equal but at higher recall levels and average performance at individual nodes, neural network approaches are superior. Performance at nodes without common neighbours which indicate more unexpected and perhaps more useful links account for this.This work was supported by Medical Research Council [grant number MR/M013049/1] and the Cambridge Commonwealth, European and International Trus

Cross-lingual semantic specialization via lexical relation induction

Semantic specialization integrates structured linguistic knowledge from external resources (such as lexical relations in WordNet) into pretrained distributional vectors in the form of constraints. However, this technique cannot be leveraged in many languages, because their structured external resources are typically incomplete or non-existent. To bridge this gap, we propose a novel method that transfers specialization from a resource-rich source language (English) to virtually any target language. Our specialization transfer comprises two crucial steps: 1) Inducing noisy constraints in the target language through automatic word translation; and 2) Filtering the noisy constraints via a state-of-the-art relation prediction model trained on the source language constraints. This allows us to specialize any set of distributional vectors in the target language with the refined constraints. We prove the effectiveness of our method through intrinsic word similarity evaluation in 8 languages, and with 3 downstream tasks in 5 languages: lexical simplification, dialog state tracking, and semantic textual similarity. The gains over the previous state-of-art specialization methods are substantial and consistent across languages. Our results also suggest that the transfer method is effective even for lexically distant source-target language pairs. Finally, as a by-product, our method produces lists of WordNet-style lexical relations in resource-poor languages

School performance is age appropriate with support services in very preterm children at 11 years of age

Aim: This Finnish regional birth‐cohort study compared the school performance of very preterm and full‐term children when they reached 11 years of age.Methods: Teachers rated the educational abilities of 123 preterm children and 133 full‐term controls at the age of 11 years as well as the support services they received. The children were all born in the Turku University Hospital between 2001 and 2005. In the preterm group, neurosensory impairments were confirmed at two years of corrected age, and full‐scale intelligence quotient (IQ) was assessed at 11 years of age using the Wechsler Intelligence Scale, Fourth Edition.Results: Educational abilities, including academic skills and classroom functioning, did not differ between the two groups after excluding the children with a full‐scale IQ Conclusion: A full‐scale IQ ≥ 70 and age‐appropriate educational abilities do not exclude a significant need for support services in very preterm children at the age of 11 years.</p

Executive Function Profiles at Home and at School in 11-Year-Old Very Low Birth Weight or Very Low Gestational Age Children

Objective: Executive function (EF) problems of children born at very low birth weight (VLBW; = 70 had clinically significant problems in the Working Memory subscale at school. Although they had clinically significant problems at home in the Behavioral Regulation Index, the difference disappeared when adjusted for paternal education. Lower gestational age, lower birth weight z-score, surgical necrotizing enterocolitis, low paternal and maternal education, and lower full-scale IQ were identified to be risk factors for higher scores in ecological assessment of EF. Conclusion: VLBW or VLGA children in this cohort exhibit fewer EF problems in ecological assessment of EF compared to previous literature. EF problems of this study population vary by home and school setting and are emphasized in working memory at school. Screening for EF problems in school environment is recommended to target the support.Peer reviewe

Kriittiset tapahtumat perheyritysten omistajajohtajien kokemuksellisen oppimisen taustalla

This study examines the critical incidents behind the experiential learning of family business owner-managers. In Finland, 70 % of employing companies are family businesses, forming the bulk of the Finnish economy. The skills and choices of owner-managers are crucial to the renewal of family businesses. The data of this study consists of ten Finnish owner-managers’ interviews, gathered from three successful family businesses that have undergone business succession. The narratives of significant incidents were identified by using the critical incident technique. Through themes in the narratives, six types of critical incidents were identified related to the support sought and received, internal motivation, conflicts, changes and choices, the engagement of the growth environment and ownership, the balance between family and entrepreneurship, and to the company’s external operating environment as a challenger and helper. Even though events were positive or negative, they contributed to the growth of managers through experience. This study confirms previous research indicating, that the growth of entrepreneurs occurs more through experiential learning than through formal education. Owner-managers also saw learning through work as an internal motivating factor. This study provides empirical insight into the theory of experiential learning, leader- and adult education, and debate on family businesses.Tässä tutkimuksessa selvitetään, millaisia kriittisiä tapahtumia voidaan havaita perheyritysten omistajajohtajien kokemuksellisessa oppimisessa. Perheyritysten merkitys maamme taloudelle on suuri, sillä Suomessa työllistävistä yrityksistä 70 % on perheyrityksiä. Omistajajohtajien osaaminen ja valinnat ovat ratkaisevia perheyritysten uudistamiselle. Tämän tutkimuksen aineisto koostuu kymmenen suomalaisen omistajajohtajan haastatteluista, jotka kerättiin kolmesta menestyneestä, sukupolvenvaihdoksen läpikäyneestä perheyrityksestä. Merkittävistä tapahtumista kertovat narratiivit tunnistettiin kriittisten tapahtumien menetelmällä. Narratiiveissa esiintyvien teemojen avulla muodostettiin kuusi kriittisistä tapahtumista kertovaa tapahtumatyyppiä. Ne kertoivat haetusta ja saadusta tuesta, johtajan sisäistä motivaatiota vahvistavista tapahtumista, ristiriita-, muutos- ja valintatilanteista, sitouttavasta kasvuympäristöstä ja omistajuudesta, perheen ja yrittäjyyden välisestä tasapainoilusta sekä yrityksen ulkoisen toimintaympäristön haastavista ja auttavista tapahtumista. Huolimatta siitä, olivatko tapahtumat positiivisia vai negatiivisia, tapahtumat edistivät johtajana kasvamista kokemuksellisuuden kautta. Tutkimustulokset vahvistavat aiempaa tutkimusta siitä, että yrittäjien tehtävässä kasvaminen tapahtuu enemmän kokemuksellisena oppimisena kuin muodollisen koulutuksen kautta. Omistajajohtajat myös kokivat työn kautta oppimisen sisäisenä motivaatiotekijänä. Tämä tutkimus tuottaa empiiristä tietoa kokemukselliseen oppimiseen liittyvään teoreettiseen keskusteluun, aikuis- ja johtamiskoulutuksiin sekä perheyrityskeskusteluun

Executive Function Profiles at Home and at School in 11-Year-Old Very Low Birth Weight or Very Low Gestational Age Children

OBJECTIVE: Executive function (EF) problems of children born at very low birth weight (VLBW; ≤1500 g) or very low gestational age (VLGA; METHODS: A total of 125 VLBW or VLGA children and 132 controls were assessed using the Behavior Rating Inventory of EF, which includes 8 subscales that form the Behavioral Regulation and Metacognition Indexes. For VLBW or VLGA children, full-scale intelligence quotient (IQ) was assessed using the Wechsler Intelligence scale for Children, Fourth Edition. Neonatal data were collected systematically.RESULTS: VLBW or VLGA children with full-scale IQ ≥ 70 had clinically significant problems in the Working Memory subscale at school. Although they had clinically significant problems at home in the Behavioral Regulation Index, the difference disappeared when adjusted for paternal education. Lower gestational age, lower birth weight z-score, surgical necrotizing enterocolitis, low paternal and maternal education, and lower full-scale IQ were identified to be risk factors for higher scores in ecological assessment of EF.CONCLUSION: VLBW or VLGA children in this cohort exhibit fewer EF problems in ecological assessment of EF compared to previous literature. EF problems of this study population vary by home and school setting and are emphasized in working memory at school. Screening for EF problems in school environment is recommended to target the support.</p

Impact of Machine Learning Assistance on the Quality of Life Prediction for Breast Cancer Patients

Proper and well-timed interventions may improve breast cancer patient adaptation, resilience and quality of life (QoL) during treatment process and time after disease. The challenge is to identify those patients who would benefit most from a particular intervention. The aim of this study was to measure whether the machine learning prediction incorporated in the clinical decision support system (CDSS) improves clinicians' performance to predict patients' QoL during treatment process. We conducted an experimental setup in which six clinicians used CDSS and predicted QoL for 60 breast cancer patients. Each patient was evaluated both with and without the aid of machine learning prediction. The clinicians were also open-ended interviewed to investigate the usage and perceived benefits of CDSS with the machine learning prediction aid. Clinicians' performance to evaluate the patients' QoL was higher with the aid of machine learning predictions than without the aid. AUROC of clinicians was .777 (95% CI .691 - .857) with the aid and .755 (95% CI .664 - .840) without the aid. When the machine learning model's prediction was correct, the average accuracy (ACC) of the clinicians was .788 (95% CI .739 - .838) with the aid and .717 (95% CI .636 - .798) without the aid.Peer reviewe