Identifying patterns in medical records through latent semantic analysis

Gefen, David; Miller, J. M.; Armstrong, Johnathon Kyle; Cornelius, Fran; Robertson, Noreen M.; Smith‐McLallen, Aaron; Taylor, Jennifer A.

doi:10.1145/3209086

Cited by 20 publications

(14 citation statements)

References 15 publications

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“…Entity-Specific Semantic Information In order to semantically enrich drug as well as disease entities, we use (medical) classification systems as a source. Considering pharmaceutical entities, there are a couple of popular classification systems such as the Medical Subject Headings (MeSH) Trees 9 or the Anatomical Therapeutic Chemical (ATC) Classification System 10 . The ATC subdivides drugs (hierarchically) according to their anatomical, therapeutic/pharmacologic, and chemical features.…”

Section: Impact Of Domain Knowledge To Disambiguate Predicted Drug-dimentioning

confidence: 99%

“…Scientific literature is one of the primary sources in the investigation of new drugs [2], which is why newer approaches use neural embedding models (NEMs) to calculate linguistic or lexical similarities between entities in order to deduce their properties, semantics, and relationships [9,10]. The use of NEMs in this area is based on a (context) hypothesis [9], where words which share numerous similar surrounding word contexts are spatially positioned closer to each other in a high-dimensional space. This property leads to the fact that with increasing similarity (i.e.…”

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confidence: 99%

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Exploiting Latent Semantic Subspaces to Derive Associations for Specific Pharmaceutical Semantics

et al. 2020

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State-of-the-art approaches in the field of neural embedding models (NEMs) enable progress in the automatic extraction and prediction of semantic relations between important entities like active substances, diseases, and genes. In particular, the prediction property is making them valuable for important research-related tasks such as hypothesis generation and drug repositioning. A core challenge in the biomedical domain is to have interpretable semantics from NEMs that can distinguish, for instance, between the following two situations: (a) drug x induces disease y and (b) drug x treats disease y. However, NEMs alone cannot distinguish between associations such as treats or induces. Is it possible to develop a model to learn a latent representation from the NEMs capable of such disambiguation? To what extent do we need domain knowledge to succeed in the task? In this paper, we answer both questions and show that our proposed approach not only succeeds in the disambiguation task but also advances current growing research efforts to find real predictions using a sophisticated retrospective analysis. Furthermore, we investigate which type of associations is generally better contextualized and therefore probably has a stronger influence in our disambiguation task. In this context, we present an approach to extract an interpretable latent semantic subspace from the original embedding space in which therapeutic drug–disease associations are more likely .

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Section: Impact Of Domain Knowledge To Disambiguate Predicted Drug-dimentioning

confidence: 99%

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confidence: 99%

Exploiting Latent Semantic Subspaces to Derive Associations for Specific Pharmaceutical Semantics

et al. 2020

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“…Others have used Latent Semantic Analysis to identify patterns in electronic medical records [12,13] when based in writing standards. But there are several challenges when there are no writing standards.…”

Section: Semantic Indexingmentioning

confidence: 99%

Towards early and automatic detection of Urinary Infection during pregnancy

Escobedo¹,

Hirales-Carbajal²

2019

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Worldwide Fetal-Maternal morbidity and mortality is frightfully high. Most of these diseases occur in developing countries. One of the main reasons for this problem, after gestational hypertension and complications in childbirth, is infections. Urinary Tract Infections (UTI) during pregnancy is one of the main causes for fetal-maternal morbidity and mortality in Mexico. Among others, the pervasiveness and heterogeneity of data in Electronic Medical Records (EMR) complicates early diagnosis and treatment of UTI. OBJECTIVES: Our goal is extract empirical knowledge, in the form of association rules, that generalize symptomatology and treatment of UTI patients with positive and nagative diagnosis. METHODS: In this study, we developed a criterion to extract words and expressions that uniquely characterize each patient class. We extracted association rules from EMRs and evaluated its level of correspondence between the rules and the extracted word sets. RESULTS: By defining a bound on word frequency usage and evaluating the positive to negative word ratio we were able to identify word sets that uniquely characterize each patient class. A bound of 47 enabled extraction of 25 unique words and expressions for each patient class. Further, approximately 17% and 27% of association rules drew terms from each word set correspondingly. CONCLUSION: This work seeks to promote the creation of more effective criterions to extract features, from EMRs, that improve characterization of patients and that ultimately lead to a more accurate diagnosis of UTIs.

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“…As a mathematical model of thematic similarity, Latent Semantic Analysis has been extremely useful in demonstrating patterns within linguistic corpora with thousands of citations for its use. For a recent example, Gefen et al (2018) applied LSA to medical records, accurately pairing keywords with medical conditions across all records. LSA has also been utilized to model personality traits (Kwantes, Derbentseva, Lam, Vartanian, & Marmurek, 2016), topic modelling of political debates (Valdez, Pickett, & Goodson, 2018), and automatically grading essays (Williams, 2006).…”

Section: Latent Semantic Analysismentioning

confidence: 99%

Love, Language, and Linear Algebra: Linguistic Modeling of Personality and Mate Preference

Marshall¹,

Buchanan²,

Fallone³

2018

Preprint

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This study utilized Latent Semantic Analysis to determine whether similarities in personality predicted similarities in responses to a romantic writing prompt (Landauer & Dumais, 1997). From participants’ writing samples, we calculated thematic cosines (a measure of relatedness) between each male and female participant. Participants also completed the Big Five Personality Questionnaire Short Form (Morizet, 2014). Extraversion, agreeableness, and conscientiousness were related to cosines, which suggested small-medium relationships from personality traits to written responses. This relationship was consistent with previous studies on mate preference, which suggests that LSA may be useful in quantifying mate preference. We conclude with a discussion of the compatibility survey data and continuous measures in examining complex phenomena in the Behavioral Sciences, such as mate preference.

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Identifying patterns in medical records through latent semantic analysis

Abstract: Text analysis can reveal patterns of association among medical terms and medical codes.

Cited by 20 publications

References 15 publications

Exploiting Latent Semantic Subspaces to Derive Associations for Specific Pharmaceutical Semantics

Exploiting Latent Semantic Subspaces to Derive Associations for Specific Pharmaceutical Semantics

Towards early and automatic detection of Urinary Infection during pregnancy

Love, Language, and Linear Algebra: Linguistic Modeling of Personality and Mate Preference

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