scholarly journals Medical code prediction via capsule networks and ICD knowledge

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Weidong Bao ◽  
Hongfei Lin ◽  
Yijia Zhang ◽  
Jian Wang ◽  
Shaowu Zhang
Keyword(s):  
Domain Knowledge ◽  
Routing Algorithm ◽  
Clinical Manifestations ◽  
International Classification Of Diseases ◽  
Dynamic Routing ◽  
Prediction Task ◽  
Clinical Notes ◽  
Classification Of Diseases ◽  
Label Information ◽  
Mimic Iii

Abstract Background Clinical notes record the health status, clinical manifestations and other detailed information of each patient. The International Classification of Diseases (ICD) codes are important labels for electronic health records. Automatic medical codes assignment to clinical notes through the deep learning model can not only improve work efficiency and accelerate the development of medical informatization but also facilitate the resolution of many issues related to medical insurance. Recently, neural network-based methods have been proposed for the automatic medical code assignment. However, in the medical field, clinical notes are usually long documents and contain many complex sentences, most of the current methods cannot effective in learning the representation of potential features from document text. Methods In this paper, we propose a hybrid capsule network model. Specifically, we use bi-directional LSTM (Bi-LSTM) with forwarding and backward directions to merge the information from both sides of the sequence. The label embedding framework embeds the text and labels together to leverage the label information. We then use a dynamic routing algorithm in the capsule network to extract valuable features for medical code prediction task. Results We applied our model to the task of automatic medical codes assignment to clinical notes and conducted a series of experiments based on MIMIC-III data. The experimental results show that our method achieves a micro F1-score of 67.5% on MIMIC-III dataset, which outperforms the other state-of-the-art methods. Conclusions The proposed model employed the dynamic routing algorithm and label embedding framework can effectively capture the important features across sentences. Both Capsule networks and domain knowledge are helpful for medical code prediction task.

Converging Semantic Knowledge and Deep Learning for Medical Coding

2019 ◽  
Vol 7 (2) ◽  
pp. 33-52
Author(s):  
Nuria Garcia-Santa ◽  
Beatriz San Miguel ◽  
Takanori Ugai
Keyword(s):  
Deep Learning ◽  
International Classification Of Diseases ◽  
Semantic Knowledge ◽  
Clinical Notes ◽  
Medical Coding ◽  
Advantages And Disadvantages ◽  
Classification Of Diseases ◽  
Medical Reports ◽  
Mimic Iii

The field of medical coding enables to assign codes of medical classifications such as the international classification of diseases (ICD) to clinical notes, which are medical reports about patients' conditions written by healthcare professionals in natural language. These texts potentially include medical terms that define diagnosis, symptoms, drugs, treatments, etc., and the use of spontaneous language is challenging for automatic processing. Medical coding is usually performed manually by human medical coders becoming time-consuming and prone to errors. This research aims at developing new approaches that combine deep learning elements together with traditional technologies. A semantic-based proposal supported by a proprietary knowledge graph (KG), neural network implementations, and an ensemble model to resolve the medical coding are presented. A comparative discussion between the proposals where the advantages and disadvantages of each one is analysed. To evaluate approaches, two main corpus have been used: MIMIC-III and private de-identified clinical notes.

scholarly journals An explainable CNN approach for medical codes prediction from clinical text

2021 ◽  
Vol 21 (S9) ◽  
Author(s):  
Shuyuan Hu ◽  
Fei Teng ◽  
Lufei Huang ◽  
Jun Yan ◽  
Haibo Zhang
Keyword(s):  
International Classification Of Diseases ◽  
Text Documents ◽  
Clinical Text ◽  
Clinical Notes ◽  
Low Level ◽  
Classification Of Diseases ◽  
Convolution Filter ◽  
Automatic Coding ◽  
Mimic Iii ◽  
Icd Coding

Abstract Background Clinical notes are unstructured text documents generated by clinicians during patient encounters, generally are annotated with International Classification of Diseases (ICD) codes, which give formatted information about the diagnosis and treatment. ICD code has shown its potentials in many fields, but manual coding is labor-intensive and error-prone, lead to researches of automatic coding. Two specific challenges of this task are (1) given an annotated clinical notes, the reasons behind specific diagnoses and treatments are  implicit; (2) explainability is important for practical automatic coding method, the method should not only explain its prediction output but also have explainable internal mechanics. This study aims to develop an explainable CNN approach to address these two challenges. Method Our key idea is that for the automatic ICD coding task, the presence of informative snippets in the clinical text that correlated with each code plays an important role in the prediction of codes, and an informative snippet can be considered as a local and low-level feature. We infer that there exists a correspondence between a convolution filter and a local and low-level feature. Base on the inference, we come up with the Shallow and Wide Attention convolutional Mechanism (SWAM) to improve the CNN-based models’ ability to learn local and low-level features for each label. Results We evaluate our approach on MIMIC-III, an open-access dataset of ICU medical records. Our approach substantially outperforms previous results on top-50 medical code prediction on MIMIC-III dataset, the precision of the worst-performing 10% labels in previous works is increased from 0% to 53% on average. We attribute this improvement to SWAM, by which the wide architecture with attention mechanism gives the model ability to more extensively learn the unique features of different codes, and we prove it by an ablation experiment. Besides, we perform manual analysis of the performance imbalance between different codes, and preliminary conclude the characteristics that determine the difficulty of learning specific codes. Conclusions Our main contributions can be summarized into the following three: (1) We present local and low-level features, a.k.a. informative snippets play an important role in the automatic ICD coding task, and the informative snippets extracted from the clinical text provide explanations for each code. (2) We propose that there exists a correspondence between a convolution filter and a local and low-level feature. A combination of wide and shallow convolutional layer and attention layer can help the CNN-based models better learn local and low-level features. (3) We improved the precision of the worst-performing 10% labels from 0 to 53% on average.

Clinical manifestations in patients with acute and transient psychosis

2016 ◽  
Vol 33 (S1) ◽  
pp. S363-S364
Author(s):  
Á. López Díaz ◽  
A. Soler Iborte ◽  
S. Galiano Rus ◽  
J.L. Fernández González ◽  
J.I. Aznarte López
Keyword(s):  
Psychotic Disorder ◽  
Psychotic Disorders ◽  
Clinical Manifestations ◽  
Retrospective Chart Review ◽  
International Classification Of Diseases ◽  
Exact Test ◽  
Chi Squared ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Competing Interest

IntroductionThe term, acute and transient psychosis, is comprehended as a heterogeneous group of disorders, which share, as a common feature, the abrupt and brief deployment of typical psychotic behaviour, either polymorph, delusional, or schizophreniform. This diversity of symptoms may also be present in other psychotic disorders, for which, some authors question its reliability.ObjetiveTo analyse the clinical manifestations present in acute and transient psychotic disorders (ATPD), and determine the differences between its different subcategories.MethodRetrospective chart review study of adult patients admitted in our psychiatric unit between 2011 and 2015, with a mean diagnosis of ATPD at hospital discharge. Diagnostic criteria was according to the International Classification of Diseases (ICD-10). Symptoms were divided under operative procedures, as set out in psychopatologic descriptions. For methodological reasons, statistical analysis was conducted between polymorphic features group (PM) and nonpolymorphic group (NPM). Chi-squared test and Fisher's exact test (as appropriate) were performed, using MedCalc software.ResultsThirty-nine patients met the inclusion criteria. Acute polymorphic psychotic disorder with and without symptoms of schizophrenia (39%), acute schizophrenia-like psychotic disorder (20%), acute predominantly delusional psychotic disorder (23%), other and NOS (18%). There were statistically significant differences between PM and NPM groups in emotional turmoil (>PM, P = 0.0006), grossly disorganized or abnormal motor behaviour (>PM, P = 0.0038), and type of onset (sudden >PM, P = 0.0145).ConclusionCurrently, the same concept encompasses two categories (PM and NPM) to be differentiated. The ATPD construct is under review, due its long-term instability.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Utility of ICD Codes for Stress Cardiomyopathy in Hospital Administrative Databases: What Do They Signify?

2019 ◽  
pp. 160-163
Author(s):  
Anusha G Bhat ◽  
Kevin White ◽  
Kyle Gobeil ◽  
Tara Lagu ◽  
Peter K Lindenauer ◽  
...  
Keyword(s):  
Chart Review ◽  
Clinical Manifestations ◽  
International Classification Of Diseases ◽  
Stress Cardiomyopathy ◽  
Administrative Databases ◽  
Classification Of Diseases ◽  
Icd Codes ◽  
Hospital Administrative ◽  
New Diagnosis

Prior studies of stress cardiomyopathy (SCM) have used International Classification of Diseases (ICD) codes to identify patients in administrative databases without evaluating the validity of these codes. Between 2010 and 2016, we identified 592 patients discharged with a first known principal or secondary ICD code for SCM in our medical system. On chart review, 580 charts had a diagnosis of SCM (positive predictive value 98%; 95% CI: 96.4-98.8), although 38 (6.4%) did not have active clinical manifestations of SCM during the hospitalization. Moreover, only 66.8% underwent cardiac catheterization and 91.5% underwent echocardiography. These findings suggest that, although all but a few hospitalized patients with an ICD code for SCM had a diagnosis of SCM, some of these were chronic cases, and numerous patients with a new diagnosis of SCM did not undergo a complete diagnostic workup. Researchers should be mindful of these limitations in future studies involving administrative databases.

scholarly journals Predicting ICD-9 Codes Using Self-Report of Patients

2021 ◽  
Vol 11 (21) ◽  
pp. 10046
Author(s):  
Anandakumar Singaravelan ◽  
Chung-Ho Hsieh ◽  
Yi-Kai Liao ◽  
Jia-Lien Hsu
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
International Classification Of Diseases ◽  
Discharge Summary ◽  
Self Report ◽  
Health Trends ◽  
Clinical Notes ◽  
Classification Of Diseases ◽  
Medical Classification ◽  
Remote Assistance

The International Classification of Diseases (ICD) is a globally recognized medical classification system that aids in the identification of diseases and the regulation of health trends. The ICD framework makes it easy to keep track of records and evaluate medical data for evidence-based decision-making. Several methods have predicted ICD-9 codes based on the discharge summary, clinical notes, and nursing notes. In our study, our approach only utilizes the subjective component to predict ICD-9 codes. Data cleaning and segmentation, and Natural Language Processing (NLP) techniques are applied on the subjective component during the pre-processing. Our study builds the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU) to develop a model for predicting ICD-9 codes. The ICD-9 codes contain different ICD levels such as chapter, block, three-digit code, and full code. The GRU model scores the highest recall of 57.91% in the chapter level and the top-10 experiment has a recall of 67.37%. Based on the subjective component, the model can help patients in the form of a remote assistance tool.

scholarly journals Evolution of diagnostic criteria in psychoses

2001 ◽  
Vol 3 (4) ◽  
pp. 257-263
Keyword(s):  
Psychiatric Disorders ◽  
Diagnostic Criteria ◽  
Motor Behavior ◽  
Clinical Manifestations ◽  
Disease Process ◽  
International Classification Of Diseases ◽  
World Health ◽  
Formal Thought Disorder ◽  
Classification Of Diseases ◽  
Health Organization

The term psychosis was first introduced in the mid-19th century for the separation of psychiatric disorders from neurological disorders within the neuroses. The concept of psychosis has become gradually restricted from a generic term for psychiatric disorders to one of the major classes of mental illness, which was assumed to be the result of a disease process, and, more recently, to a symptom present in many psychiatric disorders. In the course of this development, the díagnostic criteria for psychosis shifted from the severity of the clinical manifestations and the degree of impairment in social functioning to the presence of one or more symptoms in a set of psychopathological symptoms, which include hallucinations, formal thought disorder manifest in disorganized or odd speech, delusions, flat/inappropriate affect, avolition/apathy disorganized behavior, catatonic motor behavior, and depersonalization/derealization. The changes in the conceptualization of psychosis and in the diagnostic criteria for psychosis are documented in the various editions of the Diagnostic and Statistical Manual of Mental Disorders of the American Psychiatric Assocíation (from DSM-I to DSM-IV] and the International Classification of Diseases of the World Health Organization (from ICD-9 to ICD-10].

scholarly journals Using the Electronic Health Record to Identify Educational Gaps for Internal Medicine Interns

2017 ◽  
Vol 9 (1) ◽  
pp. 109-112 ◽  
Author(s):  
Alvin Rajkomar ◽  
Sumant R. Ranji ◽  
Bradley Sharpe
Keyword(s):  
Internal Medicine ◽  
Electronic Health Record ◽  
Medical Center ◽  
Academic Medical Center ◽  
International Classification Of Diseases ◽  
Health Record ◽  
Clinical Notes ◽  
Classification Of Diseases ◽  
Clinical Classification Software ◽  
Electronic Health

ABSTRACT Background  An important component of internal medicine residency is clinical immersion in core rotations to expose first-year residents to common diagnoses. Objective  Quantify intern experience with common diagnoses through clinical documentation in an electronic health record. Methods  We analyzed all clinical notes written by postgraduate year (PGY) 1, PGY-2, and PGY-3 residents on medicine service at an academic medical center July 1, 2012, through June 30, 2014. We quantified the number of notes written by PGY-1s at 1 of 3 hospitals where they rotate, by the number of notes written about patients with a specific principal billing diagnosis, which we defined as diagnosis-days. We used the International Classification of Diseases 9 (ICD-9) and the Clinical Classification Software (CCS) to group the diagnoses. Results  We analyzed 53 066 clinical notes covering 10 022 hospitalizations with 1436 different ICD-9 diagnoses spanning 217 CCS diagnostic categories. The 10 most common ICD-9 diagnoses accounted for 23% of diagnosis-days, while the 10 most common CCS groupings accounted for more than 40% of the diagnosis-days. Of 122 PGY-1s, 107 (88%) spent at least 2 months on the service, and 3% were exposed to all of the top 10 ICD-9 diagnoses, while 31% had experience with fewer than 5 of the top 10 diagnoses. In addition, 17% of PGY-1s saw all top 10 CCS diagnoses, and 5% had exposure to fewer than 5 CCS diagnoses. Conclusions  Automated detection of clinical experience may help programs review inpatient clinical experiences of PGY-1s.

scholarly journals Changes needed in the classification of anxiety disorders in childhood: Options for ICD-11

2015 ◽  
Vol 143 (5-6) ◽  
pp. 369-372
Author(s):  
Miodrag Stankovic ◽  
Grozdanko Grbesa ◽  
Jelena Kostic ◽  
Sandra Stankovic ◽  
Jelena Stevanovic
Keyword(s):  
Anxiety Disorders ◽  
Emotional Disorders ◽  
Clinical Picture ◽  
Clinical Manifestations ◽  
International Classification Of Diseases ◽  
Phobic Disorders ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Separate Block

Considering the intensive preparation of the 11th revision of the International Classification of Diseases (ICD-11), we discussed the justification of the existing classification of emotional disorders with onset specific to childhood. This paper presents the citations from the ICD-10 (F93 block) and the authors? comments as a critical review of the justification of further existence of emotional disorders with onset specific to childhood as a separate block in ICD-11 classification. We concluded that the block F93 is insufficiently defined and should be completely changed or removed from the ICD-11 classification. Additionally, the specificities of the clinical picture of anxiety disorders in children should be adequately described within the future category of anxiety and phobic disorders by giving an explicit set of instructions for identifying clinical manifestations which vary by age.

Comparison of Alcohol Use Disorders in DSM and ICD

2020 ◽  
Vol 66 (3) ◽  
pp. 143-153
Author(s):  
Jessica W. M. Wong ◽  
Friedrich M. Wurst ◽  
Ulrich W. Preuss
Keyword(s):  
Alcohol Use Disorders ◽  
International Classification Of Diseases ◽  
Clinical Use ◽  
Diagnostic Systems ◽  
Statistical Manual ◽  
Diagnostic And Statistical Manual ◽  
Dsm 5 ◽  
Classification Of Diseases ◽  
Historical Developments

Abstract. Introduction: With advances in medicine, our understanding of diseases has deepened and diagnostic criteria have evolved. Currently, the most frequently used diagnostic systems are the ICD (International Classification of Diseases) and the DSM (Diagnostic and Statistical Manual of Mental Disorders) to diagnose alcohol-related disorders. Results: In this narrative review, we follow the historical developments in ICD and DSM with their corresponding milestones reflecting the scientific research and medical considerations of their time. The current diagnostic concepts of DSM-5 and ICD-11 and their development are presented. Lastly, we compare these two diagnostic systems and evaluate their practicability in clinical use.

Angststörungen – von der ICD-10 zur ICD-11

2020 ◽  
pp. 1-5
Author(s):  
Timo D. Vloet ◽  
Marcel Romanos
Keyword(s):  
Mental Disorders ◽  
International Classification Of Diseases ◽  
International Classification ◽  
Statistical Manual ◽  
Diagnostic And Statistical Manual ◽  
Dsm 5 ◽  
Classification Of Diseases ◽  
Icd 10

Zusammenfassung. Hintergrund: Nach 12 Jahren Entwicklung wird die 11. Version der International Classification of Diseases (ICD-11) von der Weltgesundheitsorganisation (WHO) im Januar 2022 in Kraft treten. Methodik: Im Rahmen eines selektiven Übersichtsartikels werden die Veränderungen im Hinblick auf die Klassifikation von Angststörungen von der ICD-10 zur ICD-11 zusammenfassend dargestellt. Ergebnis: Die diagnostischen Kriterien der generalisierten Angststörung, Agoraphobie und spezifischen Phobien werden angepasst. Die ICD-11 wird auf Basis einer Lebenszeitachse neu organisiert, sodass die kindesaltersspezifischen Kategorien der ICD-10 aufgelöst werden. Die Trennungsangststörung und der selektive Mutismus werden damit den „regulären“ Angststörungen zugeordnet und können zukünftig auch im Erwachsenenalter diagnostiziert werden. Neu ist ebenso, dass verschiedene Symptomdimensionen der Angst ohne kategoriale Diagnose verschlüsselt werden können. Diskussion: Die Veränderungen im Bereich der Angsterkrankungen umfassen verschiedene Aspekte und sind in der Gesamtschau nicht unerheblich. Positiv zu bewerten ist die Einführung einer Lebenszeitachse und Parallelisierung mit dem Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Schlussfolgerungen: Die entwicklungsbezogene Neuorganisation in der ICD-11 wird auch eine verstärkte längsschnittliche Betrachtung von Angststörungen in der Klinik sowie Forschung zur Folge haben. Damit rückt insbesondere die Präventionsforschung weiter in den Fokus.

Sign in / Sign up

Export Citation Format

Share Document