Artificial intelligence can improve decision-making in infection management

The straight and narrow: A game theory model of broad- and narrow-spectrum empiric antibiotic therapy

Physicians prescribe empiric antibiotic treatment when definitive knowledge of the pathogen causing an infection is lacking. The options of empiric treatment can be largely divided into broad- and narrow-spectrum antibiotics. Prescribing a broad-spectrum antibiotic increases the chances of covering the causative pathogen, and hence benefits the current patient's recovery. However, prescription of broad-spectrum antibiotics also accelerates the expansion of antibiotic resistance, potentially harming future patients. We analyse the social dilemma using game theory. In our game model, physicians choose between prescribing broad and narrow-spectrum antibiotics to their patients. Their decisions rely on the probability of an infection by a resistant pathogen before definitive laboratory results are available. We prove that whenever the equilibrium strategies differ from the socially optimal policy, the deviation is always towards a more excessive use of the broad-spectrum antibiotic. We further show that if prescribing broad-spectrum antibiotics only to patients with a high probability of resistant infection is the socially optimal policy, then decentralization of the decision making may make this policy individually irrational, and thus sabotage its implementation. We discuss the importance of improving the probabilistic information available to the physician and promoting centralized decision making.

Salzburg Intensive Care database (SICdb): a detailed exploration and comparative analysis with MIMIC-IV

The utilization of artificial intelligence (AI) in healthcare is on the rise, demanding increased accessibility to (public) medical data for benchmarking. The digitization of healthcare in recent years has facilitated medical data scientists' access to extensive hospital data, fostering AI-based research. A notable addition to this trend is the Salzburg Intensive Care database (SICdb), made publicly available in early 2023. Covering over 27 thousand intensive care admissions at the University Hospital Salzburg from 2013 to 2021, this dataset presents a valuable resource for AI-driven investigations. This article explores the SICdb and conducts a comparative analysis with the widely recognized Medical Information Mart for Intensive Care - version IV (MIMIC-IV) database. The comparison focuses on key aspects, emphasizing the availability and granularity of data provided by the SICdb, particularly vital signs and laboratory measurements. The analysis demonstrates that the SICdb offers more detailed information with higher data availability and temporal resolution for signal data, especially for vital signs, compared to the MIMIC-IV. This is advantageous for longitudinal studies of patients' health conditions in the intensive care unit. The SICdb provides a valuable resource for medical data scientists and researchers. The database offers comprehensive and diverse healthcare data in a European country, making it well suited for benchmarking and enhancing AI-based healthcare research. The importance of ongoing efforts to expand and make public datasets available for advancing AI applications in the healthcare domain is emphasized by the findings.

Development of a knowledge-based healthcare-associated infections surveillance system in China

BACKGROUND

In the modern era of antibiotics, healthcare-associated infections (HAIs) have emerged as a prominent and concerning health threat worldwide. Implementing an electronic surveillance system for healthcare-associated infections offers the potential to not only alleviate the manual workload of clinical physicians in surveillance and reporting but also enhance patient safety and the overall quality of medical care. Despite the widespread adoption of healthcare-associated infections surveillance systems in numerous hospitals across China, several challenges persist. These encompass incomplete coverage of all infection types in the surveillance, lack of clarity in the alerting results provided by the system, and discrepancies in sensitivity and specificity that fall short of practical expectations.

METHODS

We design and develop a knowledge-based healthcare-associated infections surveillance system (KBHAIS) with the primary goal of supporting clinicians in their surveillance of HAIs. The system operates by automatically extracting infection factors from both structured and unstructured electronic health data. Each patient visit is represented as a tuple list, which is then processed by the rule engine within KBHAIS. As a result, the system generates comprehensive warning results, encompassing infection site, infection diagnoses, infection time, and infection probability. These knowledge rules utilized by the rule engine are derived from infection-related clinical guidelines and the collective expertise of domain experts.

RESULTS

We develop and evaluate our KBHAIS on a dataset of 106,769 samples collected from 84,839 patients at Gansu Provincial Hospital in China. The experimental results reveal that the system achieves a sensitivity rate surpassing 0.83, offering compelling evidence of its effectiveness and reliability.

CONCLUSIONS

Our healthcare-associated infections surveillance system demonstrates its effectiveness in promptly alerting patients to healthcare-associated infections. Consequently, our system holds the potential to considerably diminish the occurrence of delayed and missed reporting of such infections, thereby bolstering patient safety and elevating the overall quality of healthcare delivery.

Evaluation of an artificial intelligence-based algorithm for automated localization of craniofacial landmarks

OBJECTIVES

Due to advancing digitalisation, it is of interest to develop standardised and reproducible fully automated analysis methods of cranial structures in order to reduce the workload in diagnosis and treatment planning and to generate objectifiable data. The aim of this study was to train and evaluate an algorithm based on deep learning methods for fully automated detection of craniofacial landmarks in cone-beam computed tomography (CBCT) in terms of accuracy, speed, and reproducibility.

MATERIALS AND METHODS

A total of 931 CBCTs were used to train the algorithm. To test the algorithm, 35 landmarks were located manually by three experts and automatically by the algorithm in 114 CBCTs. The time and distance between the measured values and the ground truth previously determined by an orthodontist were analyzed. Intraindividual variations in manual localization of landmarks were determined using 50 CBCTs analyzed twice.

RESULTS

The results showed no statistically significant difference between the two measurement methods. Overall, with a mean error of 2.73 mm, the AI was 2.12% better and 95% faster than the experts. In the area of bilateral cranial structures, the AI was able to achieve better results than the experts on average.

CONCLUSION

The achieved accuracy of automatic landmark detection was in a clinically acceptable range, is comparable in precision to manual landmark determination, and requires less time.

CLINICAL RELEVANCE

Further enlargement of the database and continued development and optimization of the algorithm may lead to ubiquitous fully automated localization and analysis of CBCT datasets in future routine clinical practice.

Prediction of ciprofloxacin resistance in hospitalized patients using machine learning

BACKGROUND

Ciprofloxacin is a widely used antibiotic that has lost efficiency due to extensive resistance. We developed machine learning (ML) models that predict the probability of ciprofloxacin resistance in hospitalized patients.

METHODS

Data were collected from electronic records of hospitalized patients with positive bacterial cultures, during 2016-2019. Susceptibility results to ciprofloxacin (n = 10,053 cultures) were obtained for Escherichia coli, Klebsiella pneumoniae, Morganella morganii, Pseudomonas aeruginosa, Proteus mirabilis and Staphylococcus aureus. An ensemble model, combining several base models, was developed to predict ciprofloxacin resistant cultures, either with (gnostic) or without (agnostic) information on the infecting bacterial species.

RESULTS

The ensemble models' predictions are well-calibrated, and yield ROC-AUCs (area under the receiver operating characteristic curve) of 0.737 (95%CI 0.715-0.758) and 0.837 (95%CI 0.821-0.854) on independent test-sets for the agnostic and gnostic datasets, respectively. Shapley additive explanations analysis identifies that influential variables are related to resistance of previous infections, where patients arrived from (hospital, nursing home, etc.), and recent resistance frequencies in the hospital. A decision curve analysis reveals that implementing our models can be beneficial in a wide range of cost-benefits considerations of ciprofloxacin administration.

CONCLUSIONS

This study develops ML models to predict ciprofloxacin resistance in hospitalized patients. The models achieve high predictive ability, are well calibrated, have substantial net-benefit across a wide range of conditions, and rely on predictors consistent with the literature. This is a further step on the way to inclusion of ML decision support systems into clinical practice.

Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review

Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician's point of view.

Artificial Intelligence Can't Be Charmed: The Effects of Impartiality on Laypeople's Algorithmic Preferences

Over the coming years, AI could increasingly replace humans for making complex decisions because of the promise it holds for standardizing and debiasing decision-making procedures. Despite intense debates regarding algorithmic fairness, little research has examined how laypeople react when resource-allocation decisions are turned over to AI. We address this question by examining the role of perceived impartiality as a factor that can influence the acceptance of AI as a replacement for human decision-makers. We posit that laypeople attribute greater impartiality to AI than human decision-makers. Our investigation shows that people value impartiality in decision procedures that concern the allocation of scarce resources and that people perceive AI as more capable of impartiality than humans. Yet, paradoxically, laypeople prefer human decision-makers in allocation decisions. This preference reverses when potential human biases are made salient. The findings highlight the importance of impartiality in AI and thus hold implications for the design of policy measures.

Application of Artificial Intelligence in Combating High Antimicrobial Resistance Rates

Artificial intelligence (AI) is a branch of science and engineering that focuses on the computational understanding of intelligent behavior. Many human professions, including clinical diagnosis and prognosis, are greatly useful from AI. Antimicrobial resistance (AMR) is among the most critical challenges facing Pakistan and the rest of the world. The rising incidence of AMR has become a significant issue, and authorities must take measures to combat the overuse and incorrect use of antibiotics in order to combat rising resistance rates. The widespread use of antibiotics in clinical practice has not only resulted in drug resistance but has also increased the threat of super-resistant bacteria emergence. As AMR rises, clinicians find it more difficult to treat many bacterial infections in a timely manner, and therapy becomes prohibitively costly for patients. To combat the rise in AMR rates, it is critical to implement an institutional antibiotic stewardship program that monitors correct antibiotic use, controls antibiotics, and generates antibiograms. Furthermore, these types of tools may aid in the treatment of patients in the event of a medical emergency in which a physician is unable to wait for bacterial culture results. AI’s applications in healthcare might be unlimited, reducing the time it takes to discover new antimicrobial drugs, improving diagnostic and treatment accuracy, and lowering expenses at the same time. The majority of suggested AI solutions for AMR are meant to supplement rather than replace a doctor’s prescription or opinion, but rather to serve as a valuable tool for making their work easier. When it comes to infectious diseases, AI has the potential to be a game-changer in the battle against antibiotic resistance. Finally, when selecting antibiotic therapy for infections, data from local antibiotic stewardship programs are critical to ensuring that these bacteria are treated quickly and effectively. Furthermore, organizations such as the World Health Organization (WHO) have underlined the necessity of selecting the appropriate antibiotic and treating for the shortest time feasible to minimize the spread of resistant and invasive resistant bacterial strains.

Microfluidics-Based Point-of-Care Testing (POCT) Devices in Dealing with Waves of COVID-19 Pandemic: The Emerging Solution

Recent advances in microfluidics-based point-of-care testing (POCT) technology such as paper, array, and beads have shown promising results for diagnosing various infectious diseases. The fast and timely detection of viral infection has proven to be a critical step for deciding the therapeutic outcome in the current COVID-19 pandemic, which in turn not only enhances the patient survival rate but also reduces the disease-associated comorbidities. In the present scenario, rapid, noninvasive detection of the virus using low cost and high throughput microfluidics-based POCT devices embraces the advantages over existing diagnostic technologies, for which a centralized lab facility, expensive instruments, sample pretreatment, and skilled personnel are required. Microfluidic-based multiplexed POCT devices can be a boon for clinical diagnosis in developing countries that lacks a centralized health care system and resources. The microfluidic devices can be used for disease diagnosis and exploited for the development and testing of drug efficacy for disease treatment in model systems. The havoc created by the second wave of COVID-19 led several countries' governments to the back front. The lack of diagnostic kits, medical devices, and human resources created a huge demand for a technology that can be remotely operated with single touch and data that can be analyzed on a phone. Recent advancements in information technology and the use of smartphones led to a paradigm shift in the development of diagnostic devices, which can be explored to deal with the current pandemic situation. This review sheds light on various approaches for the development of cost-effective microfluidics POCT devices. The successfully used microfluidic devices for COVID-19 detection under clinical settings along with their pros and cons have been discussed here. Further, the integration of microfluidic devices with smartphones and wireless network systems using the Internet-of-things will enable readers for manufacturing advanced POCT devices for remote disease management in low resource settings.

[Artificial Intelligence: Challenges and Applications in Intensive Care Medicine]

The high workload in intensive care medicine arises from the exponential growth of medical knowledge, the flood of data generated by the permanent and intensive monitoring of intensive care patients, and the documentation burden. Artificial intelligence (AI) is predicted to have a great impact on ICU work in the near future as it will be applicable in many areas of critical care medicine. These applications include documentation through speech recognition, predictions for decision support, algorithms for parameter optimisation and the development of personalised intensive care medicine. AI-based decision support systems can augment human therapy decisions. Primarily through machine learning, a sub-discipline of AI, self-adaptive algorithms can learn to recognise patterns and make predictions. For actual use in clinical settings, the explainability of such systems is a prerequisite. Intensive care staff spends a large amount of their working hours on documentation, which has increased up to 50% of work time with the introduction of PDMS. Speech recognition has the potential to reduce this documentation burden. It is not yet precise enough to be usable in the clinic. The application of AI in medicine, with the help of large data sets, promises to identify diagnoses more quickly, develop individualised, precise treatments, support therapeutic decisions, use resources with maximum effectiveness and thus optimise the patient experience in the near future.

Optimising antimicrobial use in humans - review of current evidence and an interdisciplinary consensus on key priorities for research

Addressing the silent pandemic of antimicrobial resistance (AMR) is a focus of the 2021 G7 meeting. A major driver of AMR and poor clinical outcomes is suboptimal antimicrobial use. Current research in AMR is inequitably focused on new drug development. To achieve antimicrobial security we need to balance AMR research efforts between development of new agents and strategies to preserve the efficacy and maximise effectiveness of existing agents. Combining a review of current evidence and multistage engagement with diverse international stakeholders (including those in healthcare, public health, research, patient advocacy and policy) we identified research priorities for optimising antimicrobial use in humans across four broad themes: policy and strategic planning; medicines management and prescribing systems; technology to optimise prescribing; and context, culture and behaviours. Sustainable progress depends on: developing economic and contextually appropriate interventions; facilitating better use of data and prescribing systems across healthcare settings; supporting appropriate and scalable technological innovation. Implementing this strategy for AMR research on the optimisation of antimicrobial use in humans could contribute to equitable global health security.

Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

Artificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

A Real-world Evaluation of a Case-based Reasoning Algorithm to Support Antimicrobial Prescribing Decisions in Acute Care

Background

A locally developed case-based reasoning (CBR) algorithm, designed to augment antimicrobial prescribing in secondary care was evaluated.

Methods

Prescribing recommendations made by a CBR algorithm were compared to decisions made by physicians in clinical practice. Comparisons were examined in 2 patient populations: first, in patients with confirmed Escherichia coli blood stream infections (“E. coli patients”), and second in ward-based patients presenting with a range of potential infections (“ward patients”). Prescribing recommendations were compared against the Antimicrobial Spectrum Index (ASI) and the World Health Organization Essential Medicine List Access, Watch, Reserve (AWaRe) classification system. Appropriateness of a prescription was defined as the spectrum of the prescription covering the known or most-likely organism antimicrobial sensitivity profile.

Results

In total, 224 patients (145 E. coli patients and 79 ward patients) were included. Mean (standard deviation) age was 66 (18) years with 108/224 (48%) female sex. The CBR recommendations were appropriate in 202/224 (90%) compared to 186/224 (83%) in practice (odds ratio [OR]: 1.24 95% confidence interval [CI]: .392–3.936; P = .71). CBR recommendations had a smaller ASI compared to practice with a median (range) of 6 (0–13) compared to 8 (0–12) (P < .01). CBR recommendations were more likely to be classified as Access class antimicrobials compared to physicians’ prescriptions at 110/224 (49%) vs. 79/224 (35%) (OR: 1.77; 95% CI: 1.212–2.588; P < .01). Results were similar for E. coli and ward patients on subgroup analysis.

Conclusions

A CBR-driven decision support system provided appropriate recommendations within a narrower spectrum compared to current clinical practice. Future work must investigate the impact of this intervention on prescribing behaviors more broadly and patient outcomes.

Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies

BACKGROUND

Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work.

OBJECTIVES

To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management.

SOURCES

A Medline search was performed with the keywords artificial intelligence, machine learning, infection∗, and infectious disease∗ for the years 2014-2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included.

CONTENT

Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n = 19), hospital-acquired infections (n = 11), surgical site infections and other postoperative infections (n = 11), microbiological test results (n = 4), infections in general (n = 2), musculoskeletal infections (n = 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n = 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking.

IMPLICATIONS

Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed.

Artificial Intelligence in Dermatology-Where We Are and the Way to the Future: A Review

Although artificial intelligence has been available for some time, it has garnered significant interest recently and has been popularized by major companies with its applications in image identification, speech recognition and problem solving. Artificial intelligence is now being increasingly studied for its potential uses in medicine. A sound understanding of the concepts of this emerging field is essential for the dermatologist as dermatology has abundant medical data and images that can be used to train artificial intelligence for patient care. There are already a number of artificial intelligence studies focusing on skin disorders such as skin cancer, psoriasis, atopic dermatitis and onychomycosis. This article aims to present a basic introduction to the concepts of artificial intelligence as well as present an overview of the current research into artificial intelligence in dermatology, examining both its current applications and its future potential.

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications

BACKGROUND

Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID).

OBJECTIVES

We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID.

SOURCES

References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019.

CONTENT

We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n = 24, 40%), ID consultation (n = 15, 25%), medical or surgical wards (n = 13, 20%), emergency department (n = 4, 7%), primary care (n = 3, 5%) and antimicrobial stewardship (n = 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low- and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%).

IMPLICATIONS

Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.