{'fraction_non_alphanumeric': 0.011343283582089553, 'fraction_numerical': 0.008955223880597015, 'mean_word_length': 5.3007518796992485, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Mathematical models for the diagnosis of liver disease. Problems arising in the use of conditional probability theory.

This study describes two approaches to computer-assisted diagnosis using mathematical models based on the concepts of Bayesian probability and maximum likelihood respectively. The assumptions underlying the models and the restrictions such assumptions impose on their use are discussed within the context of liver disease, as are the practical difficulties arising in the implementation of the procedures.

From a study of 419 patients, information was obtained for the construction of a probability matrix for 14 diseases of the liver and biliary system. For each disease the matrix contained the incidence figures for 72 clinical details from the history and physical examination and the incidence figures for the results of 25 routine laboratory investigations.

The computed diagnoses using the models were compared with the hospital diagnosis confirmed by a liver biopsy, autopsy, or operative findings. Seventy-five per cent and 78 per cent of cases contributing to the matrix were reidentified correctly using respectively the likelihood and Bayesian methods of analysis. On the evidence within the matrix, 67 per cent of new cases on whom assessment was possible were diagnosed correctly and unequivocally, using a simple threshold test to distinguish equivocal cases. Additional information from more specific tests held in a subsidiary matrix increased the diagnostic accuracy of new cases to 73 per cent.

Some additional criteria are considered which might be used to identify new cases with diseases not represented in the probability matrix.