Phenotype clustering in health care: A narrative review for clinicians

Abstract

Human pathophysiology is occasionally too complex for unaided hypothetical-deductive reasoning and the isolated application of additive or linear statistical methods. Clustering algorithms use input data patterns and distributions to form groups of similar patients or diseases that share distinct properties. Although clinicians frequently perform tasks that may be enhanced by clustering, few receive formal training and clinician-centered literature in clustering is sparse. To add value to clinical care and research, optimal clustering practices require a thorough understanding of how to process and optimize data, select features, weigh strengths and weaknesses of different clustering methods, select the optimal clustering method, and apply clustering methods to solve problems. These concepts and our suggestions for implementing them are described in this narrative review of published literature. All clustering methods share the weakness of finding potential clusters even when natural clusters do not exist, underscoring the importance of applying data-driven techniques as well as clinical and statistical expertise to clustering analyses. When applied properly, patient and disease phenotype clustering can reveal obscured associations that can help clinicians understand disease pathophysiology, predict treatment response, and identify patients for clinical trial enrollment.

The persistent inflammation, immunosuppression, and catabolism syndrome 10 years later

“…Precision medicine has emerged as an attractive approach to incorporate different factors (epigenomic, metabolomic, and proteomic) to identify specific pathologic endotypes 76,77 . Readily available tests to determine the functional immune status of individual patients is critically important in determining personalized options.…”

“…58,75 Precision Medicine and Artificial Intelligence Precision medicine has emerged as an attractive approach to incorporate different factors (epigenomic, metabolomic, and proteomic) to identify specific pathologic endotypes. 76,77 Readily available tests to determine the functional immune status of individual patients is critically important in determining personalized options. "Big Data" may prove to be particularly useful in PICS-CCI patients to quickly endotype a patient and prognosticate.…”

Severe Acute Respiratory Syndrome Coronavirus 2 Pneumonia in Critically Ill Patients: A Cluster Analysis According to Baseline Characteristics, Biological Features, and Chest CT Scan on Admission

“…The median time from onset of symptoms to ICU admission was 9 days (7)(8)(9)(10)(11); and from hospital to ICU admission, 2 days (1-5). On ICU admission, the median SAPS II was 36 (29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45), the median SOFA score was 5 (3)(4)(5)(6), 109 patients (40.4%) presented moderate AHRF, and 108 patients (40%) presented severe AHRF. Only 31 (11.5%) of the patients were intubated at admission.…”

Application of multimodal identification technology in the innovative management operation department

“…Multi-model group: Patients in the Multi-model group were managed by a multimodal surgical information recognition system. The multimodal surgical information recognition system adopts information technology for surgical management, and designs intelligent information processes, including task release process, task acceptance process, data statistics module, and function setting module (18). Before and after the operation, the nurses of the operating department use the mobile phone application to make an appointment to dispatch the order to the ward to pick up the patient.…”