Artificial intelligence (AI) assisting with antimicrobial prescribing raises significant moral questions. Utilising ethical frameworks alongside AI-driven systems, while considering infection specific complexities, can support moral decision making to tackle antimicrobial resistance.

In a new study published in the journal Nature Biomedical Engineering, researchers at IBM say they've developed an AI model that can assist in the rapid design of antimicrobial peptides -- the building blocks of proteins. The researchers say that the model outperforms other AI methods at designing such peptides and increases the success rate of identifying a viable candidate by 10%. Antibiotics have transformed the world of medicine over the past century or so, but they've also been overused, leading to the emergence of bacteria with powerful resistance. According to the Centers for Disease Control and Prevention (CDC), antibiotic resistance is one of the biggest public health challenges of our time. In fact, in the U.S. alone, nearly 3 million people die annually as a result of antibiotic-resistant infections.

The first-ever Poultry Tech Summit brought together tech innovators, venture capitalists and poultry companies from 20 countries to triangulate on the next generation of technology that will solve problems and open new opportunities in poultry production. Poultry Tech Summit, a WATT Global Media event, was held November 5-7, 2018, in Atlanta. Inventor-entrepreneurs pitched innovations ranging from robots that patrol poultry houses to more mundane problem solvers like rust-proof gearboxes for the poultry processing plant. From live production to processing and all through the supply chain, every facet of the poultry business is touched by the innovations presented at Poultry Tech Summit. Robotics and automation generated intense interest for their potential to reduce labor, be on duty 24/7 and report remotely.

One of the difficulties of antimicrobial prescribing lies in the necessity to sequentially adjust the treatment of a patient as new clinical data become available. The lack of specialized healthcare resources and the overwhelming amount of information to process make manual surveillance unsustainable. To solve this problem, we have developed and deployed an automated antimicrobial prescription surveillance system that assists hospital pharmacists in identifying and reporting inappropriate prescriptions. Since its deployment, the system has improved antimicrobial prescribing and decreased antimicrobial use. However, the highly sensitive knowledge base used by the system leads to many false alerts.

Cephalothin and play an educational role by reminding the health professional cephaloridine therapy for bacterial meningitis. Ann Intern Med. of critical factors to consider when prescribing therapy 1975; 82:689-93.

A number of computer programs have been developed to assist physicians with diagnostic or treatment decisions, and many of them are potentially very useful tools. However, few systems have undergone evaluation by independent experts. The task evaluated was the selection of antimicrobials for cases of acute infectious meningitis before the causative agent was identified. MYCIN was originally developed in the domain of bacteremias and then expanded to include meningitis. Its task is a complicated one; it must decide whether and how to treat a patient, often in the absence of microbiological evidence. It must allow for the possibility that any important piece of information might be unknown or uncertain.

When GENTAMICIN is given for MENINGITIS, the recommended dosage is: if age is 2 yrs then 1.7 mg/kg q8h IV plus consider giving 5 mg q24h IT, else 2.3 mg/kg q8h IV plus consider giving 2.5-4 rag/day IT. The normal dose for John Jones is: 119 mg (3.0 ml, 80mg/2ml ampule) q8h [calculated on the basis of 1.7 mg/kg] plus consider giving 5 mg q24h IT GENTAMICIN is excreted by the kidneys, so its dosage must be modified in renal failure. The following table shows how the patient's renal function was determined: Identifier Value Definition SCR1 1.9 the most recent serum creatinine (mg/100ml) SCR2 1.8 the previous serum creatinine (mg/100ml) CCr(f) 42.7 estimated creatinine clearance, adjusted for normal body surface area (ml/min/1.73

Inappropriate prescribing of antimicrobials is a major clinical concern that affects as many as 50 percent of prescriptions. To solve this problem, we have developed and deployed an automated antimicrobial prescription surveillance system that assists hospital pharmacists in identifying and reporting inappropriate prescriptions. Since its deployment, the system has improved antimicrobial prescribing and decreased antimicrobial use. As a remedy, we are developing a machine learning algorithm that combines instance-based learning and rule induction techniques to discover new rules for detecting inappropriate prescriptions from previous false alerts.

Inappropriate prescribing of antimicrobials is a major clinical concern that affects as many as 50 percent of prescriptions. One of the difficulties of antimicrobial prescribing lies in the necessity to sequentially adjust the treatment of a patient as new clinical data become available. The lack of specialized healthcare resources and the overwhelming amount of information to process make manual surveillance unsustainable. To solve this problem, we have developed and deployed an automated antimicrobial prescription surveillance system that assists hospital pharmacists in identifying and reporting inappropriate prescriptions. Since its deployment, the system has improved antimicrobial prescribing and decreased antimicrobial use. However, the highly sensitive knowledge base used by the system leads to many false alerts. As a remedy, we are developing a machine learning algorithm that combines instance-based learning and rule induction techniques to discover new rules for detecting inappropriate prescriptions from previous false alerts. In this article, we describe the system, point to results and lessons learned so far and provide insight into the machine learning capability.