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The hypothetico-deductive method, which involves an iterative process of hypothesis generation and evaluation, has been used for decades by physicians to diagnose patients. This study focuses on the levels of support that medical information systems can provide during these stages of the diagnostic reasoning process. The physician initially generates a list of possible diagnoses (hypotheses) based on the patients' symptoms. Later, those hypotheses are examined to determine which ones best account for the signs, symptoms, physical examination findings, and laboratory test results. Hypothesis generation is especially challenging for medical students because the organization of knowledge in medical school curricula is disease-centered. Furthermore, the clinical reference tools that are regularly used by medical students (such as Harrison's Online, UpToDate, and eMedicine) are mostly organized by disease. To address this issue, Abduction, a hypothesis generation tool; was developed for this study. Sixteen medical students were asked to solve two patient cases in two different conditions: A (support of clinical reference tools chosen by the participant and Abduction ) and B (support of clinical reference tools chosen by the participant). In Condition A, participants were able to generate the correct diagnosis in all 16 occasions (100%) and were able to confirm it in 13 occasions (81.25%). In Condition B, participants were able to generate the correct diagnosis in three out of 16 occasions (18.75%) and were able to confirm it once (6.25%). The implications of this study are discussed with respect to the cognitive support that Abduction can provide to medical students for clinical diagnosis.

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The primary purpose of the study was to develop an expert system that could C D perform medical diagnoses In selected problem areas, and C2) provide diagnostic Insights to assist medical students In their training. An expert system Is a computer-based set of procedures and algorithms that can solve problems In a given domain. Two research questions were proposed. The first was "Given a problem space defined by a matrix of diseases and symptoms, can a computer-based model be derived that will consistently perform accurate and efficient diagnoses of cases within that problem area?" The second question was "If the techniques derived from the model are taught to a medical student, is there a subsequent improvement of diagnostic skill?" An expert system was developed which met the objectives of the study. It was able to diagnose cases in the two problem areas studied with an accuracy of 94-95%. Furthermore, it was able to perform those diagnoses in a very efficient manner, often using no more than the theoretical minimum number of steps. The expert system employed three phases: rapid search by discrimination, confirmation by pattern matching against prototypes, and elimination of some candidates (impossible states) by making use of negative information. The discrimination phase alone achieved accuracies of 73-78%. By comparison, medical students achieved mean accuracies of 54-55% in the same problem areas. This suggests that novices could improve their diagnostic accuracy by approximately 20% by following the simple rules used in the first phase of the expert system. Curricular implications are discussed. When 49 first-year medical students at the Texas College of Osteopathic Medicine were exposed to some of the insights of the expert system by means of a videotaped 10- minute lecture, their diagnostic approach was modified and the accuracy of their diagnoses did improve. However, the degree of Improvement was not statistically significant. Recommendations for further research are made.

Typescript (photocopy). / Digitized by Kansas Correctional Industries / Department: Computer Science.

Computers are used for diagnostic and training in the neuropsychological rehabilitation. PLANTEST is a program for the IBM-PC that was developed for diagnostic support. It implements a test that gives information about the reduced ability of brain-injured patients to make plans regarding a certain task.The presented thesis describes a knowledge-based system that can be used to develop new test versions for PLANTEST. The program is called SolvePT and it can prove the solubility of test material used in PLANTEST. It can also automatically generate new test material. The program uses an exhaustive forward-chaining, depth-first search and is implemented in Prolog. The datastructures and algorithm of the program as well as space and time requirements are discussed. / Department of Computer Science

MicroRNAs (miRNAs) may serve as diagnostic and predictive biomarkers for cancer. The aim of this study was to identify novel cancer biomarkers from miRNA datasets, in addition to those already known. Three published miRNA cancer datasets (liver, breast, and brain) were evaluated, and the performance of the entire feature set was compared to the performance of individual feature filters, an ensemble of those filters, and a support vector machine (SVM) wrapper. In addition to confirming many known biomarkers, the main contribution of this study is that seven miRNAs have been newly identified by our ensemble methodology as possible important biomarkers for hepatocellular carcinoma or breast cancer, pending wet lab confirmation. These biomarkers were identified from miRNA expression datasets by combining multiple feature selection techniques (i.e., creating an ensemble) or by the SVM-wrapper, and then classified by different learners. Generally speaking, creating a subset of features by selecting only the highest ranking features (miRNAs) improved upon results generated when using all the miRNAs, and the ensemble and SVM-wrapper approaches outperformed individual feature selection methods. Finally, an algorithm to determine the number of top-ranked features to include in the creation of feature subsets was developed. This algorithm takes into account the performance improvement gained by adding additional features compared to the cost of adding those features. / by Alex Kotlarchyk. / Thesis (Ph.D.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.