Understanding Artificial Intelligence and Predictive Analytics: A Clinically Focused Review of Machine Learning Techniques

Identifying clinically meaningful subgroups following open reduction and internal fixation for proximal humerus fractures: a risk stratification analysis for mortality and 30-day complications using machine learning

Background

Identification of prognostic variables for poor outcomes following open reduction internal fixation (ORIF) of displaced proximal humerus fractures have been limited to singular, linear factors and subjective clinical intuition. Machine learning (ML) has the capability to objectively segregate patients based on various outcome metrics and reports the connectivity of variables resulting in the optimal outcome. Therefore, the purpose of this study was to (1) use unsupervised ML to stratify patients to high-risk and low-risk clusters based on postoperative events, (2) compare the ML clusters to the American Society of Anesthesiologists (ASA) classification for assessment of risk, and (3) determine the variables that were associated with high-risk patients after proximal humerus ORIF.

Methods

The American College of Surgeons-National Surgical Quality Improvement Program database was retrospectively queried for patients undergoing ORIF for proximal humerus fractures between 2005 and 2018. Four unsupervised ML clustering algorithms were evaluated to partition subjects into "high-risk" and "low-risk" subgroups based on combinations of observed outcomes. Demographic, clinical, and treatment variables were compared between these groups using descriptive statistics. A supervised ML algorithm was generated to identify patients who were likely to be "high risk" and were compared to ASA classification. A game-theory-based explanation algorithm was used to illustrate predictors of "high-risk" status.

Results

Overall, 4670 patients were included, of which 202 were partitioned into the "high-risk" cluster, while the remaining (4468 patients) were partitioned into the "low-risk" cluster. Patients in the "high-risk" cluster demonstrated significantly increased rates of the following complications: 30-day mortality, 30-day readmission rates, 30-day reoperation rates, nonroutine discharge rates, length of stay, and rates of all surgical and medical complications assessed with the exception of urinary tract infection (P < .001). The best performing supervised machine learning algorithm for preoperatively identifying "high-risk" patients was the extreme-gradient boost (XGBoost), which achieved an area under the receiver operating characteristics curve of 76.8%, while ASA classification had an area under the receiver operating characteristics curve of 61.7%. Shapley values identified the following predictors of "high-risk" status: greater body mass index, increasing age, ASA class 3, increased operative time, male gender, diabetes, and smoking history.

Conclusion

Unsupervised ML identified that "high-risk" patients have a higher risk of complications (8.9%) than "low-risk" groups (0.4%) with respect to 30-day complication rate. A supervised ML model selected greater body mass index, increasing age, ASA class 3, increased operative time, male gender, diabetes, and smoking history to effectively predict "high-risk" patients.

Knowledge Transfer and Networking Upon Implementation of a Transdisciplinary Digital Health Curriculum in a Unique Digital Health Training Culture: Prospective Analysis

Background

Digital health has been taught at medical faculties for a few years. However, in general, the teaching of digital competencies in medical education and training is still underrepresented.

Objective

This study aims to analyze the objective acquisition of digital competencies through the implementation of a transdisciplinary digital health curriculum as a compulsory elective subject at a German university. The main subject areas of digital leadership and management, digital learning and didactics, digital communication, robotics, and generative artificial intelligence were developed and taught in a transdisciplinary manner over a period of 1 semester.

Methods

The participants evaluated the relevant content of the curriculum regarding the competencies already taught in advance during the study, using a Likert scale. The participants' increase in digital competencies were examined with a pre-post test consisting of 12 questions. Statistical analysis was performed using an unpaired 2-tailed Student t test. A P value of <.05 was considered statistically significant. Furthermore, an analysis of the acceptance of the transdisciplinary approach as well as the application of an alternative examination method (term paper instead of a test with closed and open questions) was carried out.

Results

In the first year after the introduction of the compulsory elective subject, students of human medicine (n=15), dentistry (n=3), and medical biotechnology (n=2) participated in the curriculum. In total, 13 participants were women (7 men), and 61.1% (n=11) of the participants in human medicine and dentistry were in the preclinical study stage (clinical: n=7, 38.9%). All the aforementioned learning objectives were largely absent in all study sections (preclinical: mean 4.2; clinical: mean 4.4; P=.02). The pre-post test comparison revealed a significant increase of 106% in knowledge (P<.001) among the participants.

Conclusions

The transdisciplinary teaching of a digital health curriculum, including digital teaching methods, considers perspectives and skills from different disciplines. Our new curriculum facilitates an objective increase in knowledge regarding the complex challenges of the digital transformation of our health care system. Of the 16 student term papers arising from the course, robotics and artificial intelligence attracted the most interest, accounting for 9 of the submissions.

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review

» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.

Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions

BACKGROUND

The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods.

MAIN TEXT

This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of "why" the field of implementation science should consider artificial intelligence, for "what" (the purpose and methods), and the "what" (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly.

CONCLUSIONS

Artificial intelligence holds promise to advance implementation science methods ("why") and accelerate its goals of closing the evidence-to-practice gap ("purpose"). However, evaluation of artificial intelligence's potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically.

Application of machine learning in predicting the risk of postpartum depression: A systematic review

BACKGROUND

Postpartum depression (PPD) presents a serious health problem among women and their families. Machine learning (ML) is a rapidly advancing field with increasing utility in predicting PPD risk. We aimed to synthesize and evaluate the quality of studies on application of ML techniques in predicting PPD risk.

METHODS

We conducted a systematic search of eight databases, identifying English and Chinese studies on ML techniques for predicting PPD risk and ML techniques with performance metrics. Quality of the studies involved was evaluated using the Prediction Model Risk of Bias Assessment Tool.

RESULTS

Seventeen studies involving 62 prediction models were included. Supervised learning was the main ML technique employed and the common ML models were support vector machine, random forest and logistic regression. Five studies (30 %) reported both internal and external validation. Two studies involved model translation, but none were tested clinically. All studies showed a high risk of bias, and more than half showed high application risk.

LIMITATIONS

Including Chinese articles slightly reduced the reproducibility of the review. Model performance was not quantitatively analyzed owing to inconsistent metrics and the absence of methods for correlation meta-analysis.

CONCLUSIONS

Researchers have paid more attention to model development than to validation, and few have focused on improvement and innovation. Models for predicting PPD risk continue to emerge. However, few have achieved the acceptable quality standards. Therefore, ML techniques for successfully predicting PPD risk are yet to be deployed in clinical environments.