In the pre-hospital and acute care treatment setting, medical responders often have difficulties in accurately determining the proper diagnosis of a particular patient. Even well-trained physicians often have difficulty under emergency conditions in which split second decisions are required with limited information. Computer-automated diagnosis was developed to improve the accuracy, effectiveness, and reliability of both field and hospital of patient treatment.
Automated differential diagnosis utilizes computer inference algorithms such as Bayesian algorithms, neural networks, or genetic algorithms. According to a Wikipedia posting:                The Bayesian network is a knowledge-based graphical representation that shows a set of variables and their probabilistic relationships between diseases and symptoms. They are based on conditional probabilities, the probability of an event given the occurrence of another event, such as the interpretation of diagnostic tests. Bayes' rule helps us compute the probability of an event with the help of some more readily information and it consistently processes options as new evidence is presented. In the context of CDSS [(clinical decision support system)], the Bayesian network can be used to compute the probabilities of the presence of the possible diseases given their symptoms. Some of the advantages of Bayesian Network include the knowledge and conclusions of experts in the form of probabilities, assistance in decision making as new information is available and are based on unbiased probabilities that are applicable to many models. Some of the disadvantages of Bayesian Network include the difficulty to get the probability knowledge for possible diagnosis and not being practical for large complex systems given multiple symptoms. The Bayesian calculations on multiple simultaneous symptoms could be overwhelming for users. Example of a Bayesian network in the CDSS context is the Iliad system which makes use of Bayesian reasoning to calculate posterior probabilities of possible diagnoses depending on the symptoms provided. The system now covers about 1500 diagnoses based on thousands of findings. Another example is the DXplain system that uses a modified form of the Bayesian logic. This CDSS produces a list of ranked diagnoses associated with the symptoms.        Artificial Neural Networks (ANN) is a nonknowledge-based adaptive CDSS that uses a form of artificial intelligence, also known as machine learning, that allows the systems to learn from past experiences/examples and recognizes patterns in clinical information. It consists of nodes called neurodes and weighted connections that transmit signals between the neurodes in a unidirectional fashion. An ANN consists of 3 main layers: Input (data receiver or findings), Output (communicates results or possible diseases) and Hidden (processes data). The system becomes more efficient with known results for large amounts of data. The advantages of ANN include the elimination of needing to program the systems and providing input from experts. The ANN CDSS can process incomplete data by making educated guesses about missing data and improves with every use due to its adaptive system learning. Additionally, ANN systems do not require large databases to store outcome data with its associated probabilities. Some of the disadvantages are that the training process may be time consuming leading users to not make use of the systems effectively. The ANN systems derive their own formulas for weighting and combining data based on the statistical recognition patterns over time which may be difficult to interpret and doubt the system's reliability. Examples include the diagnosis of appendicitis, back pain, myocardial infarction, psychiatric emergencies and skin disorders. The ANN's diagnostic predictions of pulmonary embolisms were in some cases even better than physician's predictions. Additionally, ANN based applications have been useful in the analysis of ECG (a.k.a. EKG) waveforms.        A Genetic Algorithm (GA) is a nonknowledge-based method developed in the 1940s at the Massachusetts Institute of Technology based on Darwin's evolutionary theories that dealt with the survival of the fittest. These algorithms rearrange to form different re-combinations that are better than the previous solutions. Similar to neural networks, the genetic algorithms derive their information from patient data. An advantage of genetic algorithms is these systems go through an iterative process to produce an optimal solution. The fitness function determines the good solutions and the solutions that can be eliminated. A disadvantage is the lack of transparency in the reasoning involved for the decision support systems making it undesirable for physicians. The main challenge in using genetic algorithms is in defining the fitness criteria. In order to use a genetic algorithm, there must be many components such as multiple drugs, symptoms, treatment therapy and so on available in order to solve a problem. Genetic algorithms have proved to be useful in the diagnosis of female urinary incontinence.        
Despite the fact that automated differential diagnosis systems have been developed and attempted to be implemented for more than 35 years now, they have not achieved any acceptance in the emergency medical setting for acute care treatment (ACT). In large part, this failure is due to the conditions under which emergency care of acute conditions are practiced. In those situations, such as the treatment of trauma, cardiac arrest or respiratory arrest, speed of decision-making is critical and caregivers already must split their time and attention between the patient and the physiological monitors and defibrillators. In such situations, automated differential diagnosis (ADD) tools are often viewed as interfering with the caregiving process and as a delay to treatment of the patient. Given that every minute can result in a 10% drop in survival rate, such as is the case for cardiac arrest, it is not surprising that ADD tools are ignored by the very people that they were designed to assist.
It has also been found that much of the patient's medical history is inaccessible by the caregiver at the time of the acute medical condition because patients are often treated in the prehospital setting where family members are often not present at the time of the injury.
USPTO Backgrounds
USPTO Backgrounds