Hypothetico-nomological aspects of medical diagnosis Part I: General structure of the diagnostic process and its hypothesis-directed stage

Metamedicine ◽  
1980 ◽  
Vol 1 (2) ◽  
pp. 177-194 ◽  
Author(s):  
Jan Doroszewski
Keyword(s):  
Medical Diagnosis ◽  
General Structure ◽  
Diagnostic Process

The Diagnostic Process with Special Reference to Errors

1971 ◽  
Vol 10 (03) ◽  
pp. 176-188 ◽  
Author(s):  
J. GOOD ◽  
I. W. CARD
Keyword(s):  
Special Reference ◽  
Expected Utility ◽  
Medical Diagnosis ◽  
Information Transfer ◽  
Basic Assumption ◽  
Search Tree ◽  
Weight Of Evidence ◽  
Diagnostic Process ◽  
Large Tree

An analysis is made of the losses due to errors in the diagnostic process. The basic assumption is that the doctor should try to maximize expected utility, where the utility allows both for the health of the patient and for »costs« of various kinds. This assumption leads to the view that in general the doctor should make use of a diagnostic search tree. Owing to the difficulty of estimating utilities and of back-tracking in a large tree it is convenient for him to use substitutes for utility, called quasi-utilities, such as mean information transfer or expected weight of evidence. After listing a number of such quasi-utilities, the effect on their expectations due to error is considered. The losses can be larger than might have been supposed. Much of the analysis could also be applied to scientific problems other than to medical diagnosis.

scholarly journals Karl Popper 120th anniversary; Some reflections on the decision-making process in medicine

2021 ◽  
Author(s):  
Elisha Krasin
Keyword(s):  
Decision Making ◽  
Medical Diagnosis ◽  
Scientific Method ◽  
Scientific Discovery ◽  
Diagnostic Process ◽  
Decision Making Process ◽  
Karl Popper ◽  
Relative Truth ◽  
Basic Concepts ◽  
120Th Anniversary

Rereading Popper’s “The Logic of scientific discovery”, at his 120th anniversary, brings some thoughts regarding the diagnostic process and decision making in medicine from the viewpoint of the classical scientific method. In recent years physicians are increasingly becoming technical experts who base their decision-making on uniform criteria, guidelines and classifications but unfortunately have moved away from understanding the basic concepts in the philosophy of science. This raises an ethically and philosophically important issue; what does a medical diagnosis mean? Is this an absolute or a relative truth? The implications of this question are enormous in terms of prognosis and treatment. Both patients and physicians should be educated about the nature of the diagnostic process.

The Diagnostic Process with Special Reference to Errors

1971 ◽  
Vol 10 (03) ◽  
pp. 176-188 ◽  
Author(s):  
I. J. Good ◽  
W. I. Card
Keyword(s):  
Special Reference ◽  
Expected Utility ◽  
Medical Diagnosis ◽  
Information Transfer ◽  
Basic Assumption ◽  
Search Tree ◽  
Weight Of Evidence ◽  
Diagnostic Process ◽  
Large Tree

An analysis is made of the losses due to errors in the diagnostic process. The basic assumption is that the doctor should try to maximize expected utility, where the utility allows both for the health of the patient and for »costs« of various kinds. This assumption leads to the view that in general the doctor should make use of a diagnostic search tree. Owing to the difficulty of estimating utilities and of back-tracking in a large tree it is convenient for him to use substitutes for utility, called quasi-utilities, such as mean information transfer or expected weight of evidence. After listing a number of such quasi-utilities, the effect on their expectations due to error is considered. The losses can be larger than might have been supposed. Much of the analysis could also be applied to scientific problems other than to medical diagnosis.

Virtual Patient Simulation

2016 ◽  
Vol 60 (1) ◽  
pp. 533-537
Author(s):  
Daniel T. Nystrom ◽  
Douglas E. Paull ◽  
Ashley N. D. Meyer ◽  
Hardeep Singh
Keyword(s):  
Health Care Providers ◽  
Medical Diagnosis ◽  
Diagnostic Tests ◽  
Safety Concern ◽  
Patient Simulation ◽  
Virtual Patient ◽  
Diagnostic Process ◽  
Care Providers ◽  
Sources Of Information ◽  
Information Sampling

Medical diagnosis has begun to draw attention as a patient safety concern that is valid, yet difficult to study. In the current study, we implement a virtual patient simulation to assess different information sampling techniques practiced by a variety of health care providers including physicians, nurses, health technicians, and pharmacists who were tasked with diagnosing a virtual patient. Results suggest there are three different information sampling approaches used to arrive at a medical diagnosis: iteration, batch, and haste. In the iterative approach, clinicians sampled a series of hypothesis-generating sources of information (e.g., patient history, physical exam, etc.) that were immediately followed by a series of diagnostic tests (e.g., X-ray, EKG, etc.) and this process was repeated for 2-4 cycles before arriving at a diagnosis. In the batch approach, hypothesis-generating sources of information were sampled in a single series or “batch” that was then followed by a single series of diagnostic tests. In the haste approach, only a few sources of hypothesis-generating information were sampled before arriving at a medical diagnosis, and none of the information sampled was tested using diagnostic tests. Results suggest virtual patient simulation is a useful format to observe the emergence of clinicians’ diagnostic process and to collect a variety of measures and outcomes associated with medical diagnosis.

A Fuzzy Logical Model of Computer-Assisted Medical Diagnosis

1980 ◽  
Vol 19 (03) ◽  
pp. 141-148 ◽  
Author(s):  
K.-P. Adlassnig
Keyword(s):  
Medical Diagnosis ◽  
Diagnostic System ◽  
Diagnostic Process ◽  
Computer Assisted ◽  
Fuzzy Relations ◽  
Fuzzy Subset ◽  
Symptom Pattern ◽  
A Value ◽  
Fuzzy Logical Model ◽  
Fuzzy Subsets

A model of a computer-assisted diagnostic system using fuzzy subsets has been developed. The physician documents symptom—diagnosis presence relationships and symptom—diagnosis conclusiveness relationships by means of labels of the fuzzy subsets never, almost never, very very seldom, very seldom, seldom, more or less seldom, not known, more or less often, often, very often, very very often, almost always, always. Symptoms are regarded as fuzzy subsets of reference sets. The reference set includes all values the symptom may assume. The degree of membership of a value in the fuzzy subset of a symptom is calculated when the patient’s symptom pattern is available. By means of compositions of fuzzy relations, four different diagnostic indications are determined for every diagnosis under consideration: presence indication, conclusiveness indication, non-presence indication and non-symptom presence indication. By performing the diagnostic process, the system provides the physician with proven diagnoses, excluded diagnoses and diagnostic hints, including reasons for the diagnoses displayed. Proposals for further investigations may also be requested.

A Fuzzy Logical Model of Computer-Assisted Medical Diagnosis

1980 ◽  
Vol 19 (03) ◽  
pp. 141-148
Author(s):  
K.-P. Adlassnig
Keyword(s):  
Medical Diagnosis ◽  
Diagnostic System ◽  
Diagnostic Process ◽  
Computer Assisted ◽  
Fuzzy Relations ◽  
Fuzzy Subset ◽  
Symptom Pattern ◽  
A Value ◽  
Fuzzy Logical Model ◽  
Fuzzy Subsets

A model of a computer-assisted diagnostic system using fuzzy subsets has been developed. The physician documents symptom—diagnosis presence relationships and symptom—diagnosis conclusiveness relationships by means of labels of the fuzzy subsets never, almost never, very very seldom, very seldom, seldom, more or less seldom, not known, more or less often, often, very often, very very often, almost always, always. Symptoms are regarded as fuzzy subsets of reference sets. The reference set includes all values the symptom may assume. The degree of membership of a value in the fuzzy subset of a symptom is calculated when the patient’s symptom pattern is available. By means of compositions of fuzzy relations, four different diagnostic indications are determined for every diagnosis under consideration: presence indication, conclusiveness indication, non-presence indication and non-symptom presence indication. By performing the diagnostic process, the system provides the physician with proven diagnoses, excluded diagnoses and diagnostic hints, including reasons for the diagnoses displayed. Proposals for further investigations may also be requested.

Work Domain Analysis Applied to Medical Diagnosis

2014 ◽  
Vol 3 (1) ◽  
pp. 157-161 ◽  
Author(s):  
Daniel Nystrom ◽  
Linda Williams ◽  
Douglas Paull ◽  
Mark Graber
Keyword(s):  
Medical Diagnosis ◽  
Domain Analysis ◽  
Diagnostic Error ◽  
Diagnostic Process ◽  
Cognitive Components ◽  
Decomposition Space ◽  
Work Domain Analysis ◽  
Future Work ◽  
Work Domain

Few studies have described the cognitive components that characterize the diagnostic process. This article illustrates the use of work domain analysis to create a functional depiction of diagnosis. The resulting abstraction-decomposition space clarifies the medical diagnostician’s work domain and provides a glimpse into the fundamental cognitive features of diagnosis. To facilitate comprehension of the abstraction-decomposition space’s portrayal of diagnosis, features of a generalized depiction of diagnosis and the abstraction-decomposition space were graphically coded to communicate areas of similarity. Limitations, suggestions for future work, and future applications of this work to reduce diagnostic error are provided.

scholarly journals Diagnostic reasoning in cardiovascular medicine

BMJ ◽  
2022 ◽  
pp. e064389
Author(s):  
John E Brush ◽  
Jonathan Sherbino ◽  
Geoffrey R Norman
Keyword(s):  
Cognitive Psychology ◽  
Medical Diagnosis ◽  
Diagnostic Reasoning ◽  
Diagnostic Process ◽  
Educational Strategies ◽  
Cardiovascular Medicine ◽  
Clinical Teachers ◽  
Base Rates ◽  
Novices And Experts ◽  
Intuitive Sense

ABSTRACT Research in cognitive psychology shows that expert clinicians make a medical diagnosis through a two step process of hypothesis generation and hypothesis testing. Experts generate a list of possible diagnoses quickly and intuitively, drawing on previous experience. Experts remember specific examples of various disease categories as exemplars, which enables rapid access to diagnostic possibilities and gives them an intuitive sense of the base rates of various diagnoses. After generating diagnostic hypotheses, clinicians then test the hypotheses and subjectively estimate the probability of each diagnostic possibility by using a heuristic called anchoring and adjusting. Although both novices and experts use this two step diagnostic process, experts distinguish themselves as better diagnosticians through their ability to mobilize experiential knowledge in a manner that is content specific. Experience is clearly the best teacher, but some educational strategies have been shown to modestly improve diagnostic accuracy. Increased knowledge about the cognitive psychology of the diagnostic process and the pitfalls inherent in the process may inform clinical teachers and help learners and clinicians to improve the accuracy of diagnostic reasoning. This article reviews the literature on the cognitive psychology of diagnostic reasoning in the context of cardiovascular disease.

Knowledge Representation Using Fuzzy XML Rules in Web-Based Expert System for Medical Diagnosis

2015 ◽  
pp. 138-167
Author(s):  
Priti Srinivas Sajja
Keyword(s):  
Knowledge Representation ◽  
Medical Diagnosis ◽  
General Structure ◽  
Web Based ◽  
Knowledge Based ◽  
Extensible Markup ◽  
Generic Architecture ◽  
Fuzzy Xml ◽  
Management Of Uncertainty ◽  
The Web

The Web is a huge repository of information for large spectrum of decision making and advise. To effectively utilise it, there is a need for knowledge-based techniques. This chapter proposes a novel technique of knowledge representation using a fuzzy eXtensible Markup Language (XML). XML is an efficient tool to represent content; however, it lacks management of uncertainty and vagueness. The proposed technique serves dual advantages such as making the application Web-enabled and imparting benefits of uncertainty and intelligence. This chapter presents the general structure of fuzzy XML rule, DTD model, and the generic architecture of Web-based expert systems using fuzzy XML knowledge base for a variety of applications in different areas. To demonstrate the architecture proposed, an abdomen pain diagnosing system for appendicitis is discussed with sample rules along with a decision tree for the case.

Risk and Reason in Clinical Diagnosis

2019 ◽  
Author(s):  
Cym Anthony Ryle
Keyword(s):  
Medical Diagnosis ◽  
Process Model ◽  
Medical Training ◽  
Diagnostic Reasoning ◽  
Dual Process ◽  
Diagnostic Process ◽  
Near Misses ◽  
Diagnostic Thresholds ◽  
Enhance Patient Safety

This book provides, without the use of specialist language, a description of diagnostic reasoning and error and a discussion of steps that could improve diagnostic accuracy. Drawing on work in cognitive psychology, it presents the key characteristics of human reasoning. It notes that complex cognitive tasks such as medical diagnosis require a synergy of intuition and analytical thinking and introduces the concept of bias. The book considers the value of current classifications of disease, the meaning of diagnostic thresholds, and the potential for overdiagnosis. It examines the role of the patient-centred approach in this context. It develops a description of the diagnostic process, provides illustrative examples and metaphors, and refers to the dual-process model. It suggests that medical training does not consistently provide a coherent account of diagnostic thinking and the associated risks of error. It considers the role of probability in diagnostic reasoning, noting the contribution and the limitations of both informal and mathematical estimates. It refers to clear evidence that error in medical diagnosis is a prevalent and potent cause of harm and may result from systems factors or cognitive glitches such as bias and logical fallacy. It presents cases with commentaries, highlighting the cognitive processes in diagnostic successes, near misses, and disasters. It concludes with proposals for change, notably in institutional culture; in professional culture, education, and training; and in the structure of medical records. The book advocates the development and deployment of computerized diagnostic decision support. It argues that these changes could significantly enhance patient safety.

Sign in / Sign up

Export Citation Format

Share Document