The colorectal cancer recurrence support (CARES) System

doi:10.1016/S0933-3657(97)00029-8

Artificial Intelligence in Medicine

Volume 11, Issue 3, November 1997, Pages 175-188

https://doi.org/10.1016/S0933-3657(97)00029-8 Get rights and content

Abstract

Colorectal cancer has risen in incidence to become the second commonest form of cancer in Singapore. The primary treatment is surgery but up to 50% of patients still suffer from recurrence of the cancer after surgery. Early identification of recurrence will increase the effectiveness of therapy and the survival of patients. This paper describes the CARES (Cancer Recurrence Support) System, whose objective is to predict the recurrence of colorectal cancer, using Case-based Reasoning (CBR), and supported by other techniques such as data mining and natural language processing. The CARES System employs CBR to compare and contrast between the new and past colorectal cancer patient cases, and makes inferences based on those comparisons to determine the high risk patient groups. The features and functionality of the system are described.

References (17)

A.W. Malcolm et al.
Analysis of recurrence patterns following curative resection for carcinoma of the colon and rectum
Surg. Gynecol. Obstet.
(1981)
K. Woolfson
Tumor markers in cancer of the colon and rectum
Dis. Colon Rectum
(1991)
H.S. Goh et al.
Clinico-pathological prognostic factors of large bowel cancer in Singapore: a multivariate analysis
Ann. Acad. Med. Singapore
(1987)
J. Kolodner
Case-Based Reasoning
(1993)
Cognitive Systems
ReMind Developer's Reference Manual
(1992)
E. Fix et al.
Discriminatory analysis: non-parametric discrimination, consistency properties
USAF School of Aviation Medicine, Randolph Field, Texas, Project 21-49-004, Rept. No. 4
(1951)
E. Fix et al.
Discriminatory analysis: small sample performance
USAF School of Aviation Medicine, Randolph Field, Texas, Project 21-49-004, Rept. No. 11
(1952)
L. Breiman et al.
Classification and Regression Trees
(1984)

There are more references available in the full text version of this article.

Cited by (26)

How should i explain? A comparison of different explanation types for recommender systems
2014, International Journal of Human Computer Studies
Citation Excerpt :
While many recommendation applications today represent a “black box” that accepts inputs and returns recommendation lists, a transparent recommender would also try to explain (parts of) the reasoning behind a recommendation to the user. Historically, such explanations were particularly important in classical knowledge-based systems and helped these system produce more credible predictions or more accountable decision support (see, e.g., Rowe and Wright, 1993 or Ong et al., 1997). In the domain of recommender systems, transparency is considered as an important factor that contributes to users building trust in the system (Swearingen and Sinha, 2002).
Recommender systems help users locate possible items of interest more quickly by filtering and ranking them in a personalized way. Some of these systems provide the end user not only with such a personalized item list but also with an explanation which describes why a specific item is recommended and why the system supposes that the user will like it. Besides helping the user understand the output and rationale of the system, the provision of such explanations can also improve the general acceptance, perceived quality, or effectiveness of the system.
In recent years, the question of how to automatically generate and present system-side explanations has attracted increased interest in research. Today some basic explanation facilities are already incorporated in e-commerce Web sites such as Amazon.com. In this work, we continue this line of recent research and address the question of how explanations can be communicated to the user in a more effective way.
In particular, we present the results of a user study in which users of a recommender system were provided with different types of explanation. We experimented with 10 different explanation types and measured their effects in different dimensions. The explanation types used in the study include both known visualizations from the literature as well as two novel interfaces based on tag clouds. Our study reveals that the content-based tag cloud explanations are particularly helpful to increase the user-perceived level of transparency and to increase user satisfaction even though they demand higher cognitive effort from the user. Based on these insights and observations, we derive a set of possible guidelines for designing or selecting suitable explanations for recommender systems.
An intelligent decision support algorithm for diagnosis of colorectal cancer through serum tumor markers
2010, Computer Methods and Programs in Biomedicine
Nowadays, a wide range of serum tumor markers can be applied in the diagnosis of colorectal cancer. There exists a wide variability in the type and number of routinely used markers so that, sometimes, patients may receive redundant or insufficient checks. Furthermore, the traditional single cutoff point also hinders the efficient utilization of the continuous check value of a tumor marker. In order to improve the diagnostic accuracy (DA) and decrease the cost, it is necessary to optimize the check combinations and exploit the check values fully. To this end, focusing on colorectal cancer (CRC), an artificial intelligent algorithm entitled DS-STM (diagnosis strategy of serum tumor makers) is developed in this paper. DS-STM can provide decision support for physicians on the usage of different tumor markers and diagnosis of colorectal cancer (CRC). The study demonstrates that, instead of five or more tumor markers, two markers are already enough for diagnosis for most CRC patients. The experimental study shows, compared to the traditional serial test, DS-STM can improve DA from 67.53% to 73.87% for the same validation dataset. In addition, a significant cost reduction can be achieved with the new developed diagnosis strategy.
Expert Systems
2005, The Electrical Engineering Handbook
Expert Systems
2004, The Electrical Engineering Handbook
Rough-fuzzy functions in classification
2002, Fuzzy Sets and Systems
This paper generalizes the concept of rough membership functions in pattern classification tasks to rough–fuzzy membership functions and rough–fuzzy ownership functions. Unlike the rough membership value of a pattern, which is sensitive only towards the rough uncertainty associated with the pattern, the rough–fuzzy membership (or ownership) value of the pattern signifies the rough uncertainty as well as the fuzzy uncertainty associated with the pattern. In this paper, various set theoretic properties of the rough–fuzzy functions are exploited to characterize the concept of rough–fuzzy sets. These properties are also used to measure the rough–fuzzy uncertainty associated with the given output class. Finally, a few possible applications of the rough–fuzzy functions are mentioned.
Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer
2022, World Journal of Gastrointestinal Oncology

View all citing articles on Scopus

View full text

PaperThe colorectal cancer recurrence support (CARES) System

Abstract

Analysis of recurrence patterns following curative resection for carcinoma of the colon and rectum

Surg. Gynecol. Obstet.

Tumor markers in cancer of the colon and rectum

Dis. Colon Rectum

Clinico-pathological prognostic factors of large bowel cancer in Singapore: a multivariate analysis

Ann. Acad. Med. Singapore

Case-Based Reasoning

Cognitive Systems

ReMind Developer's Reference Manual

Discriminatory analysis: non-parametric discrimination, consistency properties

USAF School of Aviation Medicine, Randolph Field, Texas, Project 21-49-004, Rept. No. 4

Discriminatory analysis: small sample performance

USAF School of Aviation Medicine, Randolph Field, Texas, Project 21-49-004, Rept. No. 11

Classification and Regression Trees

Paper
The colorectal cancer recurrence support (CARES) System