Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. We introduce a novel adaptation of the word2vec model, PK-word2vec (where PK stands for prior knowledge), for small-scale messages. PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec in a case study of patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. The dataset was composed of 1389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7981 non-medical and 1116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p = 0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks\' choices between the two groups of reviewers ( p = 0.774 under a paired t-test). PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.

UNASSIGNED: Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small.
UNASSIGNED: We introduce a novel adaptation of the word2vec model, PK-word2vec, for small-scale messages.
UNASSIGNED: PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec on patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks.
UNASSIGNED: The dataset was composed of 1,389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7,981 non-medical and 1,116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p=0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks\' choices between the two groups of reviewers (p = 0.774 under a paired t-test).
UNASSIGNED: PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.

Word valence is one of the principal dimensions in the organization of word meaning. Co-occurrence-based similarities calculated by predictive natural language processing models are relatively poor at representing affective content, but very powerful in their own way. Here, we determined how these two canonical but distinct ways of representing word meaning relate to each other in the human brain both functionally and neuroanatomically. We re-analysed an fMRI study of word valence. A co-occurrence-based model was used and the correlation with the similarity of brain activity patterns was compared to that of affective similarities. The correlation between affective and co-occurrence-based similarities was low (r = 0.065), confirming that affect was captured poorly by co-occurrence modelling. In a whole-brain representational similarity analysis, word embedding similarities correlated significantly with the similarity between activity patterns in a region confined to the superior temporal sulcus to the left, and to a lesser degree to the right. Affective word similarities correlated with the similarity in activity patterns in this same region, confirming previous findings. The affective similarity effect extended more widely beyond the superior temporal cortex than the effect of co-occurrence-based similarities did. The effect of co-occurrence-based similarities remained unaltered after partialling out the effect of affective similarities (and vice versa). To conclude, different aspects of word meaning, derived from affective judgements or from word co-occurrences, are represented in superior temporal language cortex in a neuroanatomically overlapping but functionally independent manner.

This data article introduces a reproducibility dataset with the aim of allowing the exact replication of all experiments, results and data tables introduced in our companion paper (Lastra-Díaz et al., 2019), which introduces the largest experimental survey on ontology-based semantic similarity methods and Word Embeddings (WE) for word similarity reported in the literature. The implementation of all our experiments, as well as the gathering of all raw data derived from them, was based on the software implementation and evaluation of all methods in HESML library (Lastra-Díaz et al., 2017), and their subsequent recording with Reprozip (Chirigati et al., 2016). Raw data is made up by a collection of data files gathering the raw word-similarity values returned by each method for each word pair evaluated in any benchmark. Raw data files were processed by running a R-language script with the aim of computing all evaluation metrics reported in (Lastra-Díaz et al., 2019), such as Pearson and Spearman correlation, harmonic score and statistical significance p-values, as well as to generate automatically all data tables shown in our companion paper. Our dataset provides all input data files, resources and complementary software tools to reproduce from scratch all our experimental data, statistical analysis and reported data. Finally, our reproducibility dataset provides a self-contained experimentation platform which allows to run new word similarity benchmarks by setting up new experiments including other unconsidered methods or word similarity benchmarks.

Natural language processing (NLP) of health-related data is still an expertise demanding, and resource expensive process. We created a novel, open source rapid clinical text mining system called NimbleMiner. NimbleMiner combines several machine learning techniques (word embedding models and positive only labels learning) to facilitate the process in which a human rapidly performs text mining of clinical narratives, while being aided by the machine learning components.
This manuscript describes the general system architecture and user Interface and presents results of a case study aimed at classifying fall-related information (including fall history, fall prevention interventions, and fall risk) in homecare visit notes.
We extracted a corpus of homecare visit notes (n = 1,149,586) for 89,459 patients from a large US-based homecare agency. We used a gold standard testing dataset of 750 notes annotated by two human reviewers to compare the NimbleMiner\'s ability to classify documents regarding whether they contain fall-related information with a previously developed rule-based NLP system.
NimbleMiner outperformed the rule-based system in almost all domains. The overall F- score was 85.8% compared to 81% by the rule based-system with the best performance for identifying general fall history (F = 89% vs. F = 85.1% rule-based), followed by fall risk (F = 87% vs. F = 78.7% rule-based), fall prevention interventions (F = 88.1% vs. F = 78.2% rule-based) and fall within 2 days of the note date (F = 83.1% vs. F = 80.6% rule-based). The rule-based system achieved slightly better performance for fall within 2 weeks of the note date (F = 81.9% vs. F = 84% rule-based).
NimbleMiner outperformed other systems aimed at fall information classification, including our previously developed rule-based approach. These promising results indicate that clinical text mining can be implemented without the need for large labeled datasets necessary for other types of machine learning. This is critical for domains with little NLP developments, like nursing or allied health professions.