Artificial Intelligence: Bayesian versus Heuristic Method for Diagnostic Decision Support

Abstract

Evoking strength is one of the important contributions of the field of Biomedical Informatics to the discipline of Artificial Intelligence. The University at Buffalo's Orthopedics Department wanted to create an expert system to assist patients with self-diagnosis of knee problems and to thereby facilitate referral to the right orthopedic subspecialist. They had two independent sports medicine physicians review 469 cases. A board-certified orthopedic sports medicine practitioner, L.B., reviewed any disagreements until a gold standard diagnosis was reached. For each case, the patients entered 126 potential answers to 26 questions into a Web interface. These were modeled by an expert sports medicine physician and the answers were reviewed by L.B. For each finding, the clinician specified the sensitivity (term frequency) and both specificity (Sp) and the heuristic evoking strength (ES). Heuristics are methods of reasoning with only partial evidence. An expert system was constructed that reflected the posttest odds of disease-ranked list for each case. We compare the accuracy of using Sp to that of using ES (original model, < 0.0008; term importance * disease importance [DItimesTI] model, < 0.0001: Wilcoxon ranked sum test). For patient referral assignment, Sp in the DItimesTI model was superior to the use of ES. By the fifth diagnosis, the advantage was lost and so there is no difference between the techniques when serving as a reminder system.

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

“…Out of the 75 papers we analysed, 26 reported the adoption of knowledge-based AI methods 60 – 85 , possibly in combination with data- driven methods 61 , 63 , 70 , 71 , 74 , 76 . This later group of 6 hybrid approaches will be specifically detailed in the next subsection.…”

“…The knowledge-based methods explicitly represent knowledge as symbols in the form of rules, ontologies, past cases, or other types of knowledge structures. Specifically, the surveyed approaches ranged from the use of ontologies 61 , 62 , 67 – 69 , to rule-based reasoning (including the adoption of fuzzy rules) 64 , 66 – 68 , 72 – 75 , 78 – 80 , 82 – 85 , to the definition of semantic or associative networks 76 , 77 , to temporal reasoning 65 , to probabilistic models such as Bayesian Networks 60 , 63 , 70 , 81 , and to case base reasoning CBR 61 , 71 .…”

“…As regards to the addressed task, most works deal with diagnosis 60 , 62 , 64 , 70 , 73 , 74 , 77 – 80 , 82 , 84 , 85 or therapy/treatment support 61 – 63 , 66 , 75 , 81 , while some works address classification 83 , patient’s behaviour tracking or activity scheduling 65 , and training 68 . A set of papers deals with computer-interpretable guidelines (CIGs) 67 , 69 , 71 , 72 .…”

Herausforderungen der Digitalisierung in der Traumaversorgung

“…Potenzielle Anwendungen für IA-gestützte Systeme finden sich in 4 klinischen Bereichen: 4 Analyse und Interpretation anamnestischer Daten und Untersuchungsergebnissen [16][17][18][19][20][21], digitaler Algorithmen die Fehlerrate in den ersten 30 min der Schockraumversorgung signifikant reduziert werden kann [26]. Assistenzsysteme unterstützen zudem die Datenerhebung für die innerklinische Triage [19].…”

“…Potenzielle Anwendungen für IA-gestützte Systeme finden sich in 4 klinischen Bereichen: Analyse und Interpretation anamnestischer Daten und Untersuchungsergebnissen [ 16 – 21 ], Interpretation radiologischer Datensätze [ 22 – 24 ], OP-Assistenzsystem aus dem Bereich der Robotik [ 25 ], Entscheidungsfindung/-assistenz [ 26 ]. …”