0.6

the United Kingdom

The parametervector for a classcis~θc={θc1,θc2,...,θcn}

However, the class probabilities tend to beoverpowered by the combination of word probabilities,so we use a uniform prior estimate for simplicity

ages 2–10 y

average stature

height velocity

0.8 cm/mo (10 cm/y)

1 y

median height velocity

1 y of life

infants

≈1.7 cm/mo (20 cm/y)

≈3.7 cm/mo (44 cm/y)

shortly after birth

average stature

median height velocity

average stature

puberty

infancy

height velocity

average stature

height velocity

average stature

height

average stature

height

48.5 ± 0.1; n = 595

47.4 ± 0.2 cm; n = 242

birth length

average stature

mean birth length

average stature

Height

average-stature

median weight-for-age

average stature

5th

Ref. [44], and for BioNLP, in Ref. [45].

Add handling of negation and hedging

(or the entire document)

affects recall

ActualRelatedNot RelatedPredictedRelated2010Not Related2731Precision = 67%Recall = 43%

define a set of selected features for each one.

OMIM MorbidMap

The simple NER approach that we applied to medical records, leveraging Uruguayan terminology dictionaries from SNOMED CT, produced the results listed in Table 3 for the domains of disease and finding entities. A physician manually evaluated the expected vs. found entities for each medical record, considering an entity as correctly identified when its recognized name was exactly or nearly exactly as expected. We obtained a precision of 94% when counting entity repetitions (more than one mention), and 87% leaving repetitions apart

Fig. 5. Structure of the mini ontology: A graph with nodes representing entities and edges representing relations between them.

hipertensión arterial (English arterial hypertension), the dictionary-based approach correctly recognizes hipertensión arterial disease in Spanish, but the CoreNLP node recognized both hipertensión (English hypertension) and arterial only individually.

Since an actual set of medical records in Spanish was not available for research, we manually transcribed 109 clinical notes with physician observations, from actual patient cases, used for medical education.

The knowledge base can be queried in two ways: 1) starting from the medical record, and leading to related entities (like genes); or 2) starting from genes of interest (previously obtained from patients genome or exome analysis), and leading to related diseases and substances

Mini Ontology, is permanently updated from a corpus that contains novel articles, on a daily basis

{relation_type, entitity_1, entity_2}.

Wu et al. [22] compare alternatives based on word embeddings to improve NER results in BioNLP, against existent proposals based on CRF, MaxEnt, and SVM. Chiu et al. [23] devise guidelines for good word2vec based embeddings, both CBOW and skip-gram, working on PubMed and the PMC corpus. For auxiliary tasks, these authors use GeniaSS as a sentence splitter and NLTK [24] for word tokenizing

GeniaSS as a sentence splitter

Genia Tagger [13] has been frequently used both for part of speech tagging and named entity recognition (NER) in the BioNLP domain. For Spanish medical documents, Genia Tagger has been used in conjunction with Freeling [14] for entity recognition and automatic annotation [15]

HGVS

OMIM

SNOMED CT,

The goal of this work is to provide tools for the medical geneticist that optimize his/her access to the latest research pertaining to a specific patient (or to specific genomic information)

This makes attempts to become properly acquainted with the latest findings that could be relevant to a specific patient particularly challenging

Are there known substance/drug interactions?;

Is this variant pathogenic?; With which phenotypes/diseases is this variant associated?;

The literature requires reviewing in such a way that will allow the gathering of the latest findings

A patient genome can be sequenced in a few hours or even minutes [1],

PubMed abstracts

A patient genome can be sequenced in a few hours or evenminutes [1],

Nonsyndromic intellectual disability

P = 0.01

Enlarged ventricles

ituitarystalk hypoplasi

Patient 1

general or cortical atrophy

neuronal migration defect

enlarged cisterna magna

screening, surveillance, and interventional measures,

excess toxicities withparticular cytotoxic therapies