scholarly journals REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare

2021 ◽  
Author(s):  
Zohreh Shams ◽  
Botty Dimanov ◽  
Sumaiyah Kola ◽  
Nikola Simidjievski ◽  
Helena Andres Terre ◽  
...  
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Domain Knowledge ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Biological Research ◽  
Cancer Subtypes ◽  
Biologically Relevant ◽  
Human In The Loop

AbstractDeep learning models are receiving increasing attention in clinical decision-making, however the lack of interpretability and explainability impedes their deployment in day-to-day clinical practice. We propose REM, an interpretable and explainable methodology for extracting rules from deep neural networks and combining them with other data-driven and knowledge-driven rules. This allows integrating machine learning and reasoning for investigating applied and basic biological research questions. We evaluate the utility of REM on the predictive tasks of classifying histological and immunohistochemical breast cancer subtypes from genotype and phenotype data. We demonstrate that REM efficiently extracts accurate, comprehensible and, biologically relevant rulesets from deep neural networks that can be readily integrated with rulesets obtained from tree-based approaches. REM provides explanation facilities for predictions and enables the clinicians to validate and calibrate the extracted rulesets with their domain knowledge. With these functionalities, REM caters for a novel and direct human-in-the-loop approach in clinical decision making.

scholarly journals REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare

2021 ◽  
Author(s):  
Zohreh Shams ◽  
Botty Dimanov ◽  
Sumaiyah Kola ◽  
Nikola Simidjievski ◽  
Helena Andres Terre ◽  
...  
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Domain Knowledge ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Biological Research ◽  
Cancer Subtypes ◽  
Biologically Relevant ◽  
Human In The Loop

AbstractDeep learning models are receiving increasing attention in clinical decision-making, however the lack of interpretability and explainability impedes their deployment in day-to-day clinical practice. We propose REM, an interpretable and explainable methodology for extracting rules from deep neural networks and combining them with other data-driven and knowledge-driven rules. This allows integrating machine learning and reasoning for investigating applied and basic biological research questions. We evaluate the utility of REM on the predictive tasks of classifying histological and immunohistochemical breast cancer subtypes from genotype and phenotype data. We demonstrate that REM efficiently extracts accurate, comprehensible and, biologically relevant rulesets from deep neural networks that can be readily integrated with rulesets obtained from tree-based approaches. REM provides explanation facilities for predictions and enables the clinicians to validate and calibrate the extracted rulesets with their domain knowledge. With these functionalities, REM caters for a novel and direct human-in-the-loop approach in clinical decision making.

scholarly journals Explaining deep neural networks for knowledge discovery in electrocardiogram analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Steven A. Hicks ◽  
Jonas L. Isaksen ◽  
Vajira Thambawita ◽  
Jonas Ghouse ◽  
Gustav Ahlberg ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Medical Data ◽  
Medical Doctors ◽  
Medical Tests ◽  
Deep Learning Model

AbstractDeep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.

Improving the evidence-based clinical decision-making process: Interactive classification and topic discovery on diabetes-related biomedical literature (Preprint)

2021 ◽  
Author(s):  
Adrian Ahne ◽  
Guy Fagherazzi ◽  
Xavier Tannier ◽  
Thomas Czernichow ◽  
Francisco Orchard
Keyword(s):  
Decision Making ◽  
Active Learning ◽  
Hierarchical Clustering ◽  
Domain Knowledge ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Biomedical Literature ◽  
Decision Making Process ◽  
Evidence Based ◽  
Text Data

BACKGROUND The amount of available textual health data such as scientific and biomedical literature is constantly growing and it becomes more and more challenging for health professionals to properly summarise those data and in consequence to practice evidence-based clinical decision making. Moreover, the exploration of large unstructured health text data is very challenging for non experts due to limited time, resources and skills. Current tools to explore text data lack ease of use, need high computation efforts and have difficulties to incorporate domain knowledge and focus on topics of interest. OBJECTIVE We developed a methodology which is able to explore and target topics of interest via an interactive user interface for experts and non-experts. We aim to reach near state of the art performance, while reducing memory consumption, increasing scalability and minimizing user interaction effort to improve the clinical decision making process. The performance is evaluated on diabetes-related abstracts from Pubmed. METHODS The methodology consists of four parts: 1) A novel interpretable hierarchical clustering of documents where each node is defined by headwords (describe documents in this node the most); 2) An efficient classification system to target topics; 3) Minimized users interaction effort through active learning; 4) A visual user interface through which a user interacts. We evaluated our approach on 50,911 diabetes-related abstracts from Pubmed which provide a hierarchical Medical Subject Headings (MeSH) structure, a unique identifier for a topic. Hierarchical clustering performance was compared against the implementation in the machine learning library scikit-learn. On a subset of 2000 randomly chosen diabetes abstracts, our active learning strategy was compared against three other strategies: random selection of training instances, uncertainty sampling which chooses instances the model is most uncertain about and an expected gradient length strategy based on convolutional neural networks (CNN). RESULTS For the hierarchical clustering performance, we achieved a F1-Score of 0.73 compared to scikit-learn’s of 0.76. Concerning active learning performance, after 200 chosen training samples based on these strategies, the weighted F1-Score over all MeSH codes resulted in satisfying 0.62 F1-Score of our approach, compared to 0.61 of the uncertainty strategy, 0.61 the CNN and 0.45 the random strategy. Moreover, our methodology showed a constant low memory use with increased number of documents but increased execution time. CONCLUSIONS We proposed an easy to use tool for experts and non-experts being able to combine domain knowledge with topic exploration and target specific topics of interest while improving transparency. Furthermore our approach is very memory efficient and highly parallelizable making it interesting for large Big Data sets. This approach can be used by health professionals to rapidly get deep insights into biomedical literature to ultimately improve the evidence-based clinical decision making process.

scholarly journals Explaining Deep Neural Networks for Knowledge Discovery in Electrocardiogram Analysis

2021 ◽  
Author(s):  
Steven Hicks ◽  
Jonas Isaksen ◽  
Vajira Thambawita ◽  
Jonas Ghouse ◽  
Gustav Ahlberg ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Medical Doctors ◽  
Medical Tests ◽  
Deep Learning Network ◽  
Deep Learning Model

Deep learning-based tools may annotate and interpret medical tests more quickly, consistently, and accurately than medical doctors. However, as medical doctors remain ultimately responsible for clinical decision-making, any deep learning-based prediction must necessarily be accompanied by an explanation that can be interpreted by a human. In this study, we present an approach, called ECGradCAM, which uses attention maps to explain the reasoning behind AI decision-making and how interpreting these explanations can be used to discover new medical knowledge. Attention maps are visualizations of how a deep learning network makes, which may be used in the clinic to aid diagnosis, and in research to identify novel features and characteristics of diagnostic medical tests. Here, we showcase the use of ECGradCAM attention maps using a novel deep learning model capable of measuring both amplitudes and intervals in 12-lead electrocardiograms.

Neural Networks and Psychiatry: Candidate Applications in Clinical Decision Making

1994 ◽  
Vol 28 (4) ◽  
pp. 651-666 ◽  
Author(s):  
Tony Florio ◽  
Stewart Einfeld ◽  
Florence Levy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Computer System ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Comprehensive Search ◽  
New Type ◽  
The Brain ◽  
Computing Approach

Neural networks comprise a fundamentally new type of computer system inspired by the functioning of neurons in the brain. Such networks are good at solving problems that involve pattern recognition and categorisation. An important difference between a neural network and a traditional computer system is that in developing an application, a neural network is not programmed; instead, it is trained to solve a particular type of problem. This ability to learn to solve a problem makes neural networks adaptable to solving a wide variety of problems, some of which have proved intractable using a traditional computing approach. Neural networks are particularly suited to tasks involving the categorisation of patterns of information, such as is required in diagnosis and clinical decision making. In the last three years reports of applications involving neural networks have begun to appear in the medical literature, and these are described in this paper. However, a comprehensive search of the literature has shown that there have not as yet been reports of any applications in psychiatry. This paper discusses the nature of clinical decision making, outlines the sorts of problems in psychiatry which neural networks applications might be developed to address, and gives examples of candidate applications in clinical decision making.

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