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.

scholarly journals Generalization of Patterns by Identification with Polynomial Neural Network

2010 ◽  
Vol 61 (2) ◽  
pp. 120-124 ◽  
Author(s):  
Ladislav Zjavka
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Functional Dependence ◽  
Polynomial Neural Network ◽  
New Type ◽  
Input Variables ◽  
The Brain ◽  
Functional Output ◽  
Simplified Form

Generalization of Patterns by Identification with Polynomial Neural Network Artificial neural networks (ANN) in general classify patterns according to their relationship, they are responding to related patterns with a similar output. Polynomial neural networks (PNN) are capable of organizing themselves in response to some features (relations) of the data. Polynomial neural network for dependence of variables identification (D-PNN) describes a functional dependence of input variables (not entire patterns). It approximates a hyper-surface of this function with multi-parametric particular polynomials forming its functional output as a generalization of input patterns. This new type of neural network is based on GMDH polynomial neural network and was designed by author. D-PNN operates in a way closer to the brain learning as the ANN does. The ANN is in principle a simplified form of the PNN, where the combinations of input variables are missing.

Omission of Histologic Grading From Clinical Decision Making May Result in Overuse of Adjuvant Therapies in Breast Cancer: Results From a Nationwide Study

2001 ◽  
Vol 19 (1) ◽  
pp. 28-36 ◽  
Author(s):  
J. Lundin ◽  
M. Lundin ◽  
K. Holli ◽  
V. Kataja ◽  
L. Elomaa ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Histologic Grade ◽  
Node Negative ◽  
Histologic Grading ◽  
Adjuvant Therapies ◽  
Geographical Regions

PURPOSE: To investigate the influence of routinely performed histologic grading on breast cancer outcome prediction and patient selection for adjuvant therapy. PATIENTS AND METHODS: The analysis is based on a cohort of 2,842 women diagnosed with breast cancer and comprising 91% of all breast cancers diagnosed in five defined geographical regions in Finland in 1991 through 1992. Data on clinicopathologic factors and follow-up were collected from hospital case records and national registries. Histologic grade assessed at diagnosis and other clinicopathologic data were available for 1,554 operable unilateral invasive carcinomas. The relative value of grade with respect to competing prognostic factors was estimated with the Cox proportional hazards model and logistic regression. Interactions and nonlinearity of factors were accounted for by using an artificial neural network. RESULTS: Histologic grade was correlated strongly with survival in the entire series and in all subgroups studied. Women with well-differentiated node-negative cancer had a 97% 5-year distant disease-free survival rate as compared with 78% for women with poorly differentiated cancer. Grade was an independent prognostic factor in multivariate models and increased the predictive accuracy of a neural network model. Inclusion of grade data in a Cox multivariate model based on tumor size and hormone receptor status in node-negative cancer increased the proportion of patients with 5% or less risk for distant recurrence at 5 years from 15% to 54%. CONCLUSION: Even when assessed by pathologists who have no special training in breast cancer pathology, histologic grade has substantial and independent prognostic value in breast cancer. Omission of grading from clinical decision making may result in considerable overuse of adjuvant therapies.

Applying artificial neural network models to clinical decision making.

2000 ◽  
Vol 12 (1) ◽  
pp. 40-51 ◽  
Author(s):  
Rumi Kato Price ◽  
Edward L. Spitznagel ◽  
Thomas J. Downey ◽  
Donald J. Meyer ◽  
Nathan K. Risk ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Clinical Decision Making ◽  
Network Models ◽  
Clinical Decision ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Models

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.

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