Quantifying decision support level of explainable automatic classification of diagnoses in Spanish medical records.

Background And Objective: In the realm of automatic Electronic Health Records (EHR) classification according to the International Classification of Diseases (ICD) there is a notable gap of non-black box approaches and more in Spanish, which is also frequently ignored in clinical language classification. An additional gap in explainability pertains to the lack of standardized metrics for evaluating the degree of explainability offered by distinct techniques.

Methods: We address the classification of Spanish electronic health records, using methods to explain the predictions and improve the decision support level.

Cause of Death estimation from Verbal Autopsies: Is the Open Response redundant or synergistic?

Civil registration and vital statistics systems capture birth and death events to compile vital statistics and to provide legal rights to citizens. Vital statistics are a key factor in promoting public health policies and the health of the population. Medical certification of cause of death is the preferred source of cause of death information.

Implementation of specialised attention mechanisms: ICD-10 classification of Gastrointestinal discharge summaries in English, Spanish and Swedish.

Multi-label classification according to the International Classification of Diseases (ICD) is an Extreme Multi-label Classification task aiming to categorise health records according to a set of relevant ICD codes. We implemented PlaBERT, a new multi-label text classification head with per-label attention, on top of a BERT model. The model assessment is conducted on Electronic Health Records, conveying Discharge Summaries in three languages - English, Spanish, and Swedish.

Explainable ICD multi-label classification of EHRs in Spanish with convolutional attention.

Background: This work deals with Natural Language Processing applied to Electronic Health Records (EHRs). EHRs are coded following the International Classification of Diseases (ICD) leading to a multi-label classification problem. Previously proposed approaches act as black-boxes without giving further insights.

Exploiting ICD Hierarchy for Classification of EHRs in Spanish Through Multi-Task Transformers.

Electronic Health Records (EHRs) convey valuable information. Experts in clinical documentation read the report, understand the prior work, procedures, tests carried out, and encode the EHRs according to the International Classification of Diseases (ICD). Assigning these codes to the EHRs helps to share information, and extract statistics.

Extracting Cause of Death From Verbal Autopsy With Deep Learning Interpretable Methods.

The international standard to ascertain the cause of death is medical certification. However, in many low and middle-income countries, the majority of deaths occur outside of health facilities. In these cases, Verbal Autopsy (VA), the narrative provided by a family member or friend together with a questionnaire is designed by the World Health Organization as the main information source.

Neural negated entity recognition in Spanish electronic health records.

This work deals with negation detection in the context of clinical texts. Negation detection is a key for decision support systems since negated events (detection of absence of some events) help ascertain current medical conditions. For artificial intelligence, negation detection is a valuable point as it can revert the meaning of a part of a text and, accordingly, influence other tasks such as medical dosage adjustment, the detection of adverse drug reactions or hospital acquired diseases.

Recent advances in Swedish and Spanish medical entity recognition in clinical texts using deep neural approaches.

Background: Text mining and natural language processing of clinical text, such as notes from electronic health records, requires specific consideration of the specialized characteristics of these texts. Deep learning methods could potentially mitigate domain specific challenges such as limited access to in-domain tools and data sets.

Methods: A bi-directional Long Short-Term Memory network is applied to clinical notes in Spanish and Swedish for the task of medical named entity recognition.

Boosting ICD multi-label classification of health records with contextual embeddings and label-granularity.

Background And Objective: This work deals with clinical text mining, a field of Natural Language Processing applied to biomedical informatics. The aim is to classify Electronic Health Records with respect to the International Classification of Diseases, which is the foundation for the identification of international health statistics, and the standard for reporting diseases and health conditions. Within the framework of data mining, the goal is the multi-label classification, as each health record has assigned multiple International Classification of Diseases codes.

Measuring the effect of different types of unsupervised word representations on Medical Named Entity Recognition.

Background: This work deals with Natural Language Processing applied to the clinical domain. Specifically, the work deals with a Medical Entity Recognition (MER) on Electronic Health Records (EHRs). Developing a MER system entailed heavy data preprocessing and feature engineering until Deep Neural Networks (DNNs) emerged.

Smoothing dense spaces for improved relation extraction between drugs and adverse reactions.

Background And Objective: This work aims at extracting Adverse Drug Reactions (ADRs), i.e. a harm directly caused by a drug at normal doses, from Electronic Health Records (EHRs).

Exploring Joint AB-LSTM With Embedded Lemmas for Adverse Drug Reaction Discovery.

This work focuses on the detection of adverse drug reactions (ADRs) in electronic health records (EHRs) written in Spanish. The World Health Organization underlines the importance of reporting ADRs for patients' safety. The fact is that ADRs tend to be under-reported in daily hospital praxis.

The class imbalance problem detecting adverse drug reactions in electronic health records.

This work focuses on adverse drug reaction extraction tackling the class imbalance problem. Adverse drug reactions are infrequent events in electronic health records, nevertheless, it is compulsory to get them documented. Text mining techniques can help to retrieve this kind of valuable information from text.

Cardiology record multi-label classification using latent Dirichlet allocation.

Background And Objectives: Electronic health records (EHRs) convey vast and valuable knowledge about dynamically changing clinical practices. Indeed, clinical documentation entails the inspection of massive number of records across hospitals and hospital sections. The goal of this study is to provide an efficient framework that will help clinicians explore EHRs and attain alternative views related to both patient-segments and diseases, like clustering and statistical information about the development of heart diseases (replacement of pacemakers, valve implantation etc.

Inferred joint multigram models for medical term normalization according to ICD.

Background: Electronic Health Records (EHRs) are written using spontaneous natural language. Often, terms do not match standard terminology like the one available through the International Classification of Diseases (ICD).

Objective: Information retrieval and exchange can be improved using standard terminology.

Machine Learning Approaches on Diagnostic Term Encoding With the ICD for Clinical Documentation.

This work focuses on data mining applied to the clinical documentation domain. Diagnostic terms (DTs) are used as keywords to retrieve valuable information from electronic health records. Indeed, they are encoded manually by experts following the International Classification of Diseases (ICD).

Semi-supervised medical entity recognition: A study on Spanish and Swedish clinical corpora.

Objective: The goal of this study is to investigate entity recognition within Electronic Health Records (EHRs) focusing on Spanish and Swedish. Of particular importance is a robust representation of the entities. In our case, we utilized unsupervised methods to generate such representations.

On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions.

The advances achieved in Natural Language Processing make it possible to automatically mine information from electronically created documents. Many Natural Language Processing methods that extract information from texts make use of annotated corpora, but these are scarce in the clinical domain due to legal and ethical issues. In this paper we present the creation of the IxaMed-GS gold standard composed of real electronic health records written in Spanish and manually annotated by experts in pharmacology and pharmacovigilance.