Learner Analytics Applied to the Clinical Diagnostic Reasoning with Hepius Cognitive Tutor and Simulator (Preprint)

2020 ◽  
Author(s):  
Raffaello Furlan ◽  
Mauro Gatti ◽  
Roberto Menè ◽  
Dana Shiffer ◽  
Chiara Marchiori ◽  
...  
Keyword(s):  
Language Processing ◽  
Medical Knowledge ◽  
Final Diagnosis ◽  
Relevant Information ◽  
Diagnostic Reasoning ◽  
Diagnostic Process ◽  
Patient Simulator ◽  
Cognitive Tutor ◽  
Clinical Diagnostic ◽  
Maximal Sensitivity

BACKGROUND Virtual Patient Simulator is a tool that may generate a multi-dimensional representation of the student’s medical knowledge by analyzing the recordings of the user’s actions during a clinical simulation. Adequate metrics may provide teachers with valuable learning information. OBJECTIVE To describe the analytic metrics we used to analyze the clinical diagnostic reasoning of medical students obtained by a novel Cognitive Tutor and Simulator named Hepius embedding Natural Language Processing (NLP) techniques. METHODS Two clinical case simulations (Tests) were created to tune our metrics. During each simulation, students’ actions were logged into the program data base for off-line analysis. Twenty-six students, attending the 5th year of the School of Medicine at Humanitas University, underwent Test 1 (April 12th 2019) which simulated a patient suffering from dyspnea. Test 2 (May 21st 2019) dealt with abdominal pain and was attended by 36 students. Overall students’ performance was split into 7 issues: 1) the identification of relevant information in the given clinical scenario (SC); 2) history taking (AN); 3) physical exam (PE); 4) medical tests (MT) ordering; 5) diagnostic hypotheses (HY) setting; 6) binary analysis fulfillment (BA); 7) final diagnosis (RS) setting. Sensitivity (percentage of relevant information found) and precision (percentage of correct actions performed) metrics were computed for each issue and combined into a harmonic (F1), thereby obtaining a single score (1= maximal sensitivity and precision) evaluating the student’s performances. The seven F1-metric scores were further combined to obtained a convenient index assessing the student’s overall performances.The seven metrics were further grouped to reflect the student’s capability to collect (SC, AN, PE and MT) and to analyze (HY, BA and RS) information. A methodological score was computed on the basis of the discordance between the diagnostic pathway followed by the student and a reference one, previously defined by the teacher. RESULTS Mean overall scores were consistent between the two tests (0.6.±0.05 for Test 1 and 0.5±0.05 for Test 2). For each student, overall performance was achieved by a different contribution in collecting and analyzing information. Methodological scores highlighted some discordance between the reference diagnostic pattern previously set by the teachear and the one pursued by the student. CONCLUSIONS Different components of the student’s diagnostic process may be disentangled and quantified by appropriate metrics applied on students’ actions recorded while addressing a virtual case. Such an approach may help teachers in giving students individualized feedbacks aimed at filling up knowledge drawbacks and methodological inconsistencies.

scholarly journals Learning Analytics Applied to Clinical Diagnostic Reasoning Using an NLP-based Virtual Patient Simulator: a Case Study (Preprint)

10.2196/24372 ◽  
2020 ◽  
Author(s):  
Raffaello Furlan ◽  
Mauro Gatti ◽  
Roberto Menè ◽  
Dana Shiffer ◽  
Chiara Marchiori ◽  
...  
Keyword(s):  
Learning Analytics ◽  
Diagnostic Reasoning ◽  
Virtual Patient ◽  
Patient Simulator ◽  
Clinical Diagnostic

scholarly journals Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

2021 ◽  
Vol 18 (12) ◽  
pp. 6408
Author(s):  
Mario Jojoa Acosta ◽  
Gema Castillo-Sánchez ◽  
Begonya Garcia-Zapirain ◽  
Isabel de la Torre Díez ◽  
Manuel Franco-Martín
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Language Processing ◽  
Health Care Professionals ◽  
Ground Truth ◽  
Relevant Information ◽  
Free Text

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

Business intelligence tools to optimize the appropriateness of the diagnostic process for clinical and epidemiologic purposes in a multicenter veterinary pathology service

2021 ◽  
pp. 104063872110031
Author(s):  
Nicola Pozzato ◽  
Laura D’Este ◽  
Laura Gagliazzo ◽  
Marta Vascellari ◽  
Monia Cocchi ◽  
...  
Keyword(s):  
Business Intelligence ◽  
Reporting System ◽  
Animal Health ◽  
Final Diagnosis ◽  
Information Management System ◽  
Daily Basis ◽  
Diagnostic Process ◽  
Multifactorial Diseases ◽  
Specific Syndrome ◽  
Veterinary Pathology

Laboratory tests provide essential support to the veterinary practitioner, and their use has grown exponentially. This growth is the result of several factors, such as the eradication of historical diseases, the occurrence of multifactorial diseases, and the obligation to control endemic and epidemic diseases. However, the introduction of novel techniques is counterbalanced by economic constraints, and the establishment of evidence- and consensus-based guidelines is essential to support the pathologist. Therefore, we developed standardized protocols, categorized by species, type of production, age, and syndrome at the Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), a multicenter institution for animal health and food safety. We have 72 protocols in use for livestock, poultry, and pets, categorized as, for example, “bovine enteric calf”, “rabbit respiratory”, “broiler articular”. Each protocol consists of a panel of tests, divided into ‘mandatory’ and ‘ancillary’, to be selected by the pathologist in order to reach the final diagnosis. After autopsy, the case is categorized into a specific syndrome, subsequently referred to as a syndrome-specific panel of analyses. The activity of the laboratories is monitored through a web-based dynamic reporting system developed using a business intelligence product (QlikView) connected to the laboratory information management system (IZILAB). On a daily basis, reports become available at general, laboratory, and case levels, and are updated as needed. The reporting system highlights epidemiologic variations in the field and allows verification of compliance with the protocols within the organization. The diagnostic protocols are revised annually to increase system efficiency and to address stakeholder requests.

scholarly journals Application of electronic trigger tools to identify targets for improving diagnostic safety

2018 ◽  
Vol 28 (2) ◽  
pp. 151-159 ◽  
Author(s):  
Daniel R Murphy ◽  
Ashley ND Meyer ◽  
Dean F Sittig ◽  
Derek W Meeks ◽  
Eric J Thomas ◽  
...  
Keyword(s):  
Adverse Event ◽  
Patient Safety ◽  
High Risk ◽  
Health Systems ◽  
Language Processing ◽  
Diagnostic Errors ◽  
Diagnostic Process ◽  
Future Research ◽  
Implementation Challenges ◽  
Preventable Harm

Progress in reducing diagnostic errors remains slow partly due to poorly defined methods to identify errors, high-risk situations, and adverse events. Electronic trigger (e-trigger) tools, which mine vast amounts of patient data to identify signals indicative of a likely error or adverse event, offer a promising method to efficiently identify errors. The increasing amounts of longitudinal electronic data and maturing data warehousing techniques and infrastructure offer an unprecedented opportunity to implement new types of e-trigger tools that use algorithms to identify risks and events related to the diagnostic process. We present a knowledge discovery framework, the Safer Dx Trigger Tools Framework, that enables health systems to develop and implement e-trigger tools to identify and measure diagnostic errors using comprehensive electronic health record (EHR) data. Safer Dx e-trigger tools detect potential diagnostic events, allowing health systems to monitor event rates, study contributory factors and identify targets for improving diagnostic safety. In addition to promoting organisational learning, some e-triggers can monitor data prospectively and help identify patients at high-risk for a future adverse event, enabling clinicians, patients or safety personnel to take preventive actions proactively. Successful application of electronic algorithms requires health systems to invest in clinical informaticists, information technology professionals, patient safety professionals and clinicians, all of who work closely together to overcome development and implementation challenges. We outline key future research, including advances in natural language processing and machine learning, needed to improve effectiveness of e-triggers. Integrating diagnostic safety e-triggers in institutional patient safety strategies can accelerate progress in reducing preventable harm from diagnostic errors.

scholarly journals A Survey of Arabic Named Entity Recognition and Classification

2014 ◽  
Vol 40 (2) ◽  
pp. 469-510 ◽  
Author(s):  
Khaled Shaalan
Keyword(s):  
Language Processing ◽  
Named Entity Recognition ◽  
Relevant Information ◽  
Arabic Language ◽  
Entity Recognition ◽  
Named Entities ◽  
Linguistic Resources ◽  
Named Entity ◽  
To Receive ◽  
Made In

As more and more Arabic textual information becomes available through the Web in homes and businesses, via Internet and Intranet services, there is an urgent need for technologies and tools to process the relevant information. Named Entity Recognition (NER) is an Information Extraction task that has become an integral part of many other Natural Language Processing (NLP) tasks, such as Machine Translation and Information Retrieval. Arabic NER has begun to receive attention in recent years. The characteristics and peculiarities of Arabic, a member of the Semitic languages family, make dealing with NER a challenge. The performance of an Arabic NER component affects the overall performance of the NLP system in a positive manner. This article attempts to describe and detail the recent increase in interest and progress made in Arabic NER research. The importance of the NER task is demonstrated, the main characteristics of the Arabic language are highlighted, and the aspects of standardization in annotating named entities are illustrated. Moreover, the different Arabic linguistic resources are presented and the approaches used in Arabic NER field are explained. The features of common tools used in Arabic NER are described, and standard evaluation metrics are illustrated. In addition, a review of the state of the art of Arabic NER research is discussed. Finally, we present our conclusions. Throughout the presentation, illustrative examples are used for clarification.

Critical thinking and diagnostic reasoning when assessing problems with the genitourinary system

2022 ◽  
Vol 31 (1) ◽  
pp. 28-32
Author(s):  
Karen Powell
Keyword(s):  
Critical Thinking ◽  
Early Diagnosis ◽  
Clinical Diagnosis ◽  
Relevant Information ◽  
Diagnostic Reasoning ◽  
Comprehensive Understanding ◽  
Genitourinary System ◽  
Urological Disorders

Urological conditions have become increasingly common and early diagnosis is key to achieving better outcomes. This article discusses the importance of having a comprehensive understanding of urological disorders, having the skills to interpret relevant information, and recognising the relationships among given elements to make an appropriate clinical diagnosis.

GRAPHICAL INDUCTION OF QUALIFIED MEDICAL KNOWLEDGE

2013 ◽  
Vol 07 (04) ◽  
pp. 377-405 ◽  
Author(s):  
TRAVIS GOODWIN ◽  
SANDA M. HARABAGIU
Keyword(s):  
Language Processing ◽  
Clinical Data ◽  
Medical Knowledge ◽  
Knowledge Bases ◽  
Clinical Knowledge ◽  
Biomedical Knowledge ◽  
Clinical Text ◽  
Relational Information ◽  
Processing Techniques ◽  
Medical Concepts

The introduction of electronic medical records (EMRs) enabled the access of unprecedented volumes of clinical data, both in structured and unstructured formats. A significant amount of this clinical data is expressed within the narrative portion of the EMRs, requiring natural language processing techniques to unlock the medical knowledge referred to by physicians. This knowledge, derived from the practice of medical care, complements medical knowledge already encoded in various structured biomedical ontologies. Moreover, the clinical knowledge derived from EMRs also exhibits relational information between medical concepts, derived from the cohesion property of clinical text, which is an attractive attribute that is currently missing from the vast biomedical knowledge bases. In this paper, we describe an automatic method of generating a graph of clinically related medical concepts by considering the belief values associated with those concepts. The belief value is an expression of the clinician's assertion that the concept is qualified as present, absent, suggested, hypothetical, ongoing, etc. Because the method detailed in this paper takes into account the hedging used by physicians when authoring EMRs, the resulting graph encodes qualified medical knowledge wherein each medical concept has an associated assertion (or belief value) and such qualified medical concepts are spanned by relations of different strengths, derived from the clinical contexts in which concepts are used. In this paper, we discuss the construction of a qualified medical knowledge graph (QMKG) and treat it as a BigData problem addressed by using MapReduce for deriving the weighted edges of the graph. To be able to assess the value of the QMKG, we demonstrate its usage for retrieving patient cohorts by enabling query expansion that produces greatly enhanced results against state-of-the-art methods.

scholarly journals End-to-end automated cache-timing attack driven by Machine Learning

10.29007/nwj8 ◽  
2019 ◽  
Author(s):  
Sebastien Carré ◽  
Victor Dyseryn ◽  
Adrien Facon ◽  
Sylvain Guilley ◽  
Thomas Perianin
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Relevant Information ◽  
Security Threats ◽  
Cache Memories ◽  
Function Calls ◽  
Timing Data ◽  
Timing Attack ◽  
End To End ◽  
Timing Attacks

Cache timing attacks are serious security threats that exploit cache memories to steal secret information.We believe that the identification of a sequence of operations from a set of cache-timing data measurements is not a trivial step when building an attack. We present a recurrent neural network model able to automatically retrieve a sequence of function calls from cache-timings. Inspired from natural language processing, our model is able to learn on partially labelled data. We use the model to unfold an end-to-end automated attack on OpenSSL ECDSA on the secp256k1 curve. Contrary to most research, we did not need human processing of the traces to retrieve relevant information.

Becoming a teacher of clinical reasoning

Diagnosis ◽  
2018 ◽  
Vol 5 (1) ◽  
pp. 11-14 ◽  
Author(s):  
Robert L. Trowbridge ◽  
Andrew P.J. Olson
Keyword(s):  
Clinical Reasoning ◽  
Cognitive Biases ◽  
Diagnostic Reasoning ◽  
Dual Process ◽  
Diagnostic Process ◽  
Clinical Experiences ◽  
Context Specificity ◽  
Clinician Educators ◽  
Medical Educators

AbstractDiagnostic reasoning is one of the most challenging and rewarding aspects of clinical practice. As a result, facility in teaching diagnostic reasoning is a core necessity for all medical educators. Clinician educators’ limited understanding of the diagnostic process and how expertise is developed may result in lost opportunities in nurturing the diagnostic abilities of themselves and their learners. In this perspective, the authors describe their journeys as clinician educators searching for a coherent means of teaching diagnostic reasoning. They discuss the initial appeal and immediate applicability of dual process theory and cognitive biases to their own clinical experiences and those of their trainees, followed by the eventual and somewhat belated recognition of the importance of context specificity. They conclude that there are no quick fixes in guiding learners to expertise of diagnostic reasoning, but rather the development of these abilities is best viewed as a long, somewhat frustrating, but always interesting journey. The role of the teacher of clinical reasoning is to guide the learners on this journey, recognizing true mastery may not be attained, but should remain a goal for teacher and learner alike.

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