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This paper presents a versatile solution to formally represent the contents of electronic health records. It is based on the knowledge graph paradigm, and semantic web standards RDF and OWL. It employs the established semantic standards SNOMED CT and FHIR, which warrant international interoperability. A graph-based form is not only useful to feed different target visualizations, but it can also be subject to AI-powered services such as (fuzzy) retrieval and summarization.
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Registros Eletrônicos de Saúde , Humanos , Web Semântica , Systematized Nomenclature of Medicine , Medicina de Precisão , Semântica , Gráficos por Computador , Armazenamento e Recuperação da InformaçãoRESUMO
We present a data visualization tool for tumor boards, merging clinical with molecular and multi-omics data to refine precision oncology decisions. The tool offers a holistic patient perspective, facilitating personalized treatment strategies. By integrating clinical and laboratory datasets, it enables intuitive navigation through complex information. Clinicians are supported in their decision-making by user-friendly visualizations. Future studies are needed to evaluate its real-world impact and usability in precision oncology settings.
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Neoplasias , Medicina de Precisão , Humanos , Interface Usuário-Computador , Oncologia , Visualização de Dados , Sistemas de Apoio a Decisões ClínicasRESUMO
Annotated language resources derived from clinical routine documentation form an intriguing asset for secondary use case scenarios. In this investigation, we report on how such a resource can be leveraged to identify additional term candidates for a chosen set of ICD-10 codes. We conducted a log-likelihood analysis, considering the co-occurrence of approximately 1.9 million de-identified ICD-10 codes alongside corresponding brief textual entries from problem lists in German. This analysis aimed to identify potential candidates with statistical significance set at p < 0.01, which were used as seed terms to harvest additional candidates by interfacing to a large language model in a second step. The proposed approach can identify additional term candidates at suitable performance values: hypernyms MAP@5=0.801, synonyms MAP@5 = 0.723 and hyponyms MAP@5 = 0.507. The re-use of existing annotated clinical datasets, in combination with large language models, presents an interesting strategy to bridge the lexical gap in standardized clinical terminologies and real-world jargon.
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Classificação Internacional de Doenças , Processamento de Linguagem Natural , Vocabulário Controlado , Humanos , Terminologia como Assunto , Registros Eletrônicos de Saúde/classificação , AlemanhaRESUMO
OBJECTIVE: To assess the performance of large language models (LLMs) for zero-shot disambiguation of acronyms in clinical narratives. MATERIALS AND METHODS: Clinical narratives in English, German, and Portuguese were applied for testing the performance of four LLMs: GPT-3.5, GPT-4, Llama-2-7b-chat, and Llama-2-70b-chat. For English, the anonymized Clinical Abbreviation Sense Inventory (CASI, University of Minnesota) was used. For German and Portuguese, at least 500 text spans were processed. The output of LLM models, prompted with contextual information, was analyzed to compare their acronym disambiguation capability, grouped by document-level metadata, the source language, and the LLM. RESULTS: On CASI, GPT-3.5 achieved 0.91 in accuracy. GPT-4 outperformed GPT-3.5 across all datasets, reaching 0.98 in accuracy for CASI, 0.86 and 0.65 for two German datasets, and 0.88 for Portuguese. Llama models only reached 0.73 for CASI and failed severely for German and Portuguese. Across LLMs, performance decreased from English to German and Portuguese processing languages. There was no evidence that additional document-level metadata had a significant effect. CONCLUSION: For English clinical narratives, acronym resolution by GPT-4 can be recommended to improve readability of clinical text by patients and professionals. For German and Portuguese, better models are needed. Llama models, which are particularly interesting for processing sensitive content on premise, cannot yet be recommended for acronym resolution.
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Abreviaturas como Assunto , Processamento de Linguagem Natural , Humanos , Idioma , Narração , Registros Eletrônicos de SaúdeRESUMO
INTRODUCTION: Owing to the heterogenic picture of bipolar disorder, it takes approximately 8.8 years to reach a correct diagnosis. Early recognition and early intervention might not only increase quality of life, but also increase life expectancy as a whole in individuals with bipolar disorder. Therefore, we hypothesize that implementing machine learning techniques can be used to support the diagnostic process of bipolar disorder and minimize misdiagnosis rates. MATERIALS AND METHODS: To test this hypothesis, a de-identified data set of only demographic information and the results of cognitive tests of 196 patients with bipolar disorder and 145 healthy controls was used to train and compare five different machine learning algorithms. RESULTS: The best performing algorithm was logistic regression, with a macro-average F1-score of 0.69 [95% CI 0.66-0.73]. After further optimization, a model with an improved macro-average F1-score of 0.75, a micro-average F1-score of 0.77, and an AUROC of 0.84 was built. Furthermore, the individual amount of contribution per variable on the classification was assessed, which revealed that body mass index, results of the Stroop test, and the d2-R test alone allow for a classification of bipolar disorder with equal performance. CONCLUSION: Using these data for clinical application results in an acceptable performance, but has not yet reached a state where it can sufficiently augment a diagnosis made by an experienced clinician. Therefore, further research should focus on identifying variables with the highest amount of contribution to a model's classification.
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Transtorno Bipolar , Aprendizado de Máquina , Humanos , Transtorno Bipolar/diagnóstico , Feminino , Masculino , Adulto , Projetos Piloto , Pessoa de Meia-Idade , Testes NeuropsicológicosRESUMO
BACKGROUND: Oxygen saturation, a key indicator of COVID-19 severity, poses challenges, especially in cases of silent hypoxemia. Electronic health records (EHRs) often contain supplemental oxygen information within clinical narratives. Streamlining patient identification based on oxygen levels is crucial for COVID-19 research, underscoring the need for automated classifiers in discharge summaries to ease the manual review burden on physicians. METHOD: We analysed text lines extracted from anonymised COVID-19 patient discharge summaries in German to perform a binary classification task, differentiating patients who received oxygen supplementation and those who did not. Various machine learning (ML) algorithms, including classical ML to deep learning (DL) models, were compared. Classifier decisions were explained using Local Interpretable Model-agnostic Explanations (LIME), which visualize the model decisions. RESULT: Classical ML to DL models achieved comparable performance in classification, with an F-measure varying between 0.942 and 0.955, whereas the classical ML approaches were faster. Visualisation of embedding representation of input data reveals notable variations in the encoding patterns between classic and DL encoders. Furthermore, LIME explanations provide insights into the most relevant features at token level that contribute to these observed differences. CONCLUSION: Despite a general tendency towards deep learning, these use cases show that classical approaches yield comparable results at lower computational cost. Model prediction explanations using LIME in textual and visual layouts provided a qualitative explanation for the model performance.
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COVID-19 , Compostos de Cálcio , Óxidos , Humanos , Estudos Retrospectivos , Oxigênio , Suplementos NutricionaisRESUMO
Clinical texts are written with acronyms, abbreviations and medical jargon expressions to save time. This hinders full comprehension not just for medical experts but also laypeople. This paper attempts to disambiguate acronyms with their given context by comparing a web mining approach via the search engine BING and a conversational agent approach using ChatGPT with the aim to see, if these methods can supply a viable resolution for the input acronym. Both approaches are automated via application programming interfaces. Possible term candidates are extracted using natural language processing-oriented functionality. The conversational agent approach surpasses the baseline for web mining without plausibility thresholds in precision, recall and F1-measure, while scoring similarly only in precision for high threshold values.
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Processamento de Linguagem Natural , Software , Ferramenta de Busca , Comunicação , RedaçãoRESUMO
A log-likelihood based co-occurrence analysis of â¼1.9 million de-identified ICD-10 codes and related short textual problem list entries generated possible term candidates at a significance level of p<0.01. These top 10 term candidates, consisting of 1 to 5-grams, were used as seed terms for an embedding based nearest neighbor approach to fetch additional synonyms, hypernyms and hyponyms in the respective n-gram embedding spaces by leveraging two different language models. This was done to analyze the lexicality of the resulting term candidates and to compare the term classifications of both models. We found no difference in system performance during the processing of lexical and non-lexical content, i.e. abbreviations, acronyms, etc. Additionally, an application-oriented analysis of the SapBERT (Self-Alignment Pretraining for Biomedical Entity Representations) language model indicates suitable performance for the extraction of all term classifications such as synonyms, hypernyms, and hyponyms.
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Idioma , Processamento de Linguagem Natural , Funções Verossimilhança , Análise por ConglomeradosRESUMO
Clinical information systems have become large repositories for semi-structured and partly annotated electronic health record data, which have reached a critical mass that makes them interesting for supervised data-driven neural network approaches. We explored automated coding of 50 character long clinical problem list entries using the International Classification of Diseases (ICD-10) and evaluated three different types of network architectures on the top 100 ICD-10 three-digit codes. A fastText baseline reached a macro-averaged F1-score of 0.83, followed by a character-level LSTM with a macro-averaged F1-score of 0.84. The top performing approach used a downstreamed RoBERTa model with a custom language model, yielding a macro-averaged F1-score of 0.88. A neural network activation analysis together with an investigation of the false positives and false negatives unveiled inconsistent manual coding as a main limiting factor.
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Idioma , Redes Neurais de Computação , Classificação Internacional de Doenças , Registros Eletrônicos de SaúdeRESUMO
Word vector representations, known as embeddings, are commonly used for natural language processing. Particularly, contextualized representations have been very successful recently. In this work, we analyze the impact of contextualized and non-contextualized embeddings for medical concept normalization, mapping clinical terms via a k-NN approach to SNOMED CT. The non-contextualized concept mapping resulted in a much better performance (F1-score = 0.853) than the contextualized representation (F1-score = 0.322).
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Processamento de Linguagem Natural , Systematized Nomenclature of MedicineRESUMO
A semi-structured clinical problem list containing â¼1.9 million de-identified entries linked to ICD-10 codes was used to identify closely related real-world expressions. A log-likelihood based co-occurrence analysis generated seed-terms, which were integrated as part of a k-NN search, by leveraging SapBERT for the generation of an embedding representation.
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Registros Eletrônicos de Saúde , Processamento de Linguagem Natural , Funções VerossimilhançaRESUMO
A large clinical diagnosis list is explored with the goal to cluster syntactic variants. A string similarity heuristic is compared with a deep learning-based approach. Levenshtein distance (LD) applied to common words only (not tolerating deviations in acronyms and tokens with numerals), together with pair-wise substring expansions raised F1 to 13% above baseline (plain LD), with a maximum F1 of 0.71. In contrast, the model-based approach trained on a German medical language model did not perform better than the baseline, not exceeding an F1 value of 0.42.
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Idioma , Processamento de Linguagem Natural , Registros Eletrônicos de Saúde , Registros , Análise por ConglomeradosRESUMO
This paper provides an overview of current linguistic and ontological challenges which have to be met in order to provide full support to the transformation of health ecosystems in order to meet precision medicine (5 PM) standards. It highlights both standardization and interoperability aspects regarding formal, controlled representations of clinical and research data, requirements for smart support to produce and encode content in a way that humans and machines can understand and process it. Starting from the current text-centered communication practices in healthcare and biomedical research, it addresses the state of the art in information extraction using natural language processing (NLP). An important aspect of the language-centered perspective of managing health data is the integration of heterogeneous data sources, employing different natural languages and different terminologies. This is where biomedical ontologies, in the sense of formal, interchangeable representations of types of domain entities come into play. The paper discusses the state of the art of biomedical ontologies, addresses their importance for standardization and interoperability and sheds light to current misconceptions and shortcomings. Finally, the paper points out next steps and possible synergies of both the field of NLP and the area of Applied Ontology and Semantic Web to foster data interoperability for 5 PM.
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Wearable sensors and mHealth apps collect fitness and health data outside of clinical settings. These data are essential for precision medicine. This paper addresses and analyzes the available tools for extracting health and fitness data from wearables and mHealth apps. We focus on the most common tools used for research, namely, the Open mHealth-Shimmer application and Fitrockr research platform.
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Aplicativos Móveis , Telemedicina , Exercício FísicoRESUMO
Word embeddings have become the predominant representation scheme on a token-level for various clinical natural language processing (NLP) tasks. More recently, character-level neural language models, exploiting recurrent neural networks, have again received attention, because they achieved similar performance against various NLP benchmarks. We investigated to what extent character-based language models can be applied to the clinical domain and whether they are able to capture reasonable lexical semantics using this maximally fine-grained representation scheme. We trained a long short-term memory network on an excerpt from a table of de-identified 50-character long problem list entries in German, each of which assigned to an ICD-10 code. We modelled the task as a time series of one-hot encoded single character inputs. After the training phase we accessed the top 10 most similar character-induced word embeddings related to a clinical concept via a nearest neighbour search and evaluated the expected interconnected semantics. Results showed that traceable semantics were captured on a syntactic level above single characters, addressing the idiosyncratic nature of clinical language. The results support recent work on general language modelling that raised the question whether token-based representation schemes are still necessary for specific NLP tasks.
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Idioma , Processamento de Linguagem Natural , Análise por Conglomerados , Redes Neurais de Computação , Assistência ao Paciente , SemânticaRESUMO
OBJECTIVE: Automated clinical phenotyping is challenging because word-based features quickly turn it into a high-dimensional problem, in which the small, privacy-restricted, training datasets might lead to overfitting. Pretrained embeddings might solve this issue by reusing input representation schemes trained on a larger dataset. We sought to evaluate shallow and deep learning text classifiers and the impact of pretrained embeddings in a small clinical dataset. MATERIALS AND METHODS: We participated in the 2018 National NLP Clinical Challenges (n2c2) Shared Task on cohort selection and received an annotated dataset with medical narratives of 202 patients for multilabel binary text classification. We set our baseline to a majority classifier, to which we compared a rule-based classifier and orthogonal machine learning strategies: support vector machines, logistic regression, and long short-term memory neural networks. We evaluated logistic regression and long short-term memory using both self-trained and pretrained BioWordVec word embeddings as input representation schemes. RESULTS: Rule-based classifier showed the highest overall micro F1 score (0.9100), with which we finished first in the challenge. Shallow machine learning strategies showed lower overall micro F1 scores, but still higher than deep learning strategies and the baseline. We could not show a difference in classification efficiency between self-trained and pretrained embeddings. DISCUSSION: Clinical context, negation, and value-based criteria hindered shallow machine learning approaches, while deep learning strategies could not capture the term diversity due to the small training dataset. CONCLUSION: Shallow methods for clinical phenotyping can still outperform deep learning methods in small imbalanced data, even when supported by pretrained embeddings.
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Ensaios Clínicos como Assunto/métodos , Mineração de Dados/métodos , Aprendizado de Máquina , Processamento de Linguagem Natural , Seleção de Pacientes , Classificação , Aprendizado Profundo , Humanos , Modelos Logísticos , Redes Neurais de ComputaçãoRESUMO
BACKGROUND: The identification of sections in narrative content of Electronic Health Records (EHR) has demonstrated to improve the performance of clinical extraction tasks; however, there is not yet a shared understanding of the concept and its existing methods. The objective is to report the results of a systematic review concerning approaches aimed at identifying sections in narrative content of EHR, using both automatic or semi-automatic methods. METHODS: This review includes articles from the databases: SCOPUS, Web of Science and PubMed (from January 1994 to September 2018). The selection of studies was done using predefined eligibility criteria and applying the PRISMA recommendations. Search criteria were elaborated by using an iterative and collaborative keyword enrichment. RESULTS: Following the eligibility criteria, 39 studies were selected for analysis. The section identification approaches proposed by these studies vary greatly depending on the kind of narrative, the type of section, and the application. We observed that 57% of them proposed formal methods for identifying sections and 43% adapted a previously created method. Seventy-eight percent were intended for English texts and 41% for discharge summaries. Studies that are able to identify explicit (with headings) and implicit sections correspond to 46%. Regarding the level of granularity, 54% of the studies are able to identify sections, but not subsections. From the technical point of view, the methods can be classified into rule-based methods (59%), machine learning methods (22%) and a combination of both (19%). Hybrid methods showed better results than those relying on pure machine learning approaches, but lower than rule-based methods; however, their scope was more ambitious than the latter ones. Despite all the promising performance results, very few studies reported tests under a formal setup. Almost all the studies relied on custom dictionaries; however, they used them in conjunction with a controlled terminology, most commonly the UMLSâ metathesaurus. CONCLUSIONS: Identification of sections in EHR narratives is gaining popularity for improving clinical extraction projects. This study enabled the community working on clinical NLP to gain a formal analysis of this task, including the most successful ways to perform it.
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Registros Eletrônicos de Saúde , Narração , Dicionários como Assunto , Humanos , Aprendizado de Máquina , Terminologia como AssuntoRESUMO
BACKGROUND: The amount of patient-related information within clinical information systems accumulates over time, especially in cases where patients suffer from chronic diseases with many hospitalizations and consultations. The diagnosis or problem list is an important feature of the electronic health record, which provides a dynamic account of a patient's current illness and past history. In the case of an Austrian hospital network, problem list entries are limited to fifty characters and are potentially linked to ICD-10. The requirement of producing ICD codes at each hospital stay, together with the length limitation of list items leads to highly redundant problem lists, which conflicts with the physicians' need of getting a good overview of a patient in short time. This paper investigates a method, by which problem list items can be semantically grouped, in order to allow for fast navigation through patient-related topic spaces. METHODS: We applied a minimal language-dependent preprocessing strategy and mapped problem list entries as tf-idf weighted character 3-grams into a numerical vector space. Based on this representation we used the unweighted pair group method with arithmetic mean (UPGMA) clustering algorithm with cosine distances and inferred an optimal boundary in order to form semantically consistent topic spaces, taking into consideration different levels of dimensionality reduction via latent semantic analysis (LSA). RESULTS: With the proposed clustering approach, evaluated via an intra- and inter-patient scenario in combination with a natural language pipeline, we achieved an average compression rate of 80% of the initial list items forming consistent semantic topic spaces with an F-measure greater than 0.80 in both cases. The average number of identified topics in the intra-patient case (µIntra = 78.4) was slightly lower than in the inter-patient case (µInter = 83.4). LSA-based feature space reduction had no significant positive performance impact in our investigations. CONCLUSIONS: The investigation presented here is centered on a data-driven solution to the known problem of information overload, which causes ineffective human-computer interactions at clinicians' work places. This problem is addressed by navigable disease topic spaces where related items are grouped and the topics can be more easily accessed.
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Análise por Conglomerados , Gerenciamento de Dados/métodos , Registros Eletrônicos de Saúde , Áustria , Humanos , Classificação Internacional de Doenças , Semântica , Interface Usuário-ComputadorRESUMO
Clinical information systems contain free-text entries in different contexts to be used in a variety of application scenarios. In this study we investigate to what extent diagnosis codes using the disease classification system ICD-10 can be automatically post-assigned to patient-based short problem list entries, (50 characters maximum). Classifiers using random forest and Adaboost performed best with an F-measure of 0.87 and 0.85 running against an unbalanced data set, and an F-measure of 0.88 and 0.94 using a balanced data set, respectively.
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Classificação Internacional de Doenças , Automação , HumanosRESUMO
Patients with multiple disorders usually have long diagnosis lists, constitute by ICD-10 codes together with individual free-text descriptions. These text snippets are produced by overwriting standardized ICD-Code topics by the physicians at the point of care. They provide highly compact expert descriptions within a 50-character long text field frequently not assigned to a specific ICD-10 code. The high redundancy of these lists would benefit from content-based categorization within different hospital-based application scenarios. This work demonstrates how to accurately group diagnosis lists via a combination of natural language processing and hierarchical clustering with an overall F-measure value of 0.87. In addition, it compresses the initial diagnosis list up to 89%. The manuscript discusses pitfall and challenges as well as the potential of a large-scale approach for tackling this problem.