Approaches to Medical Decision-Making Based on Big Clinical Data

Prediction of lymph node metastasis of lung squamous cell carcinoma by machine learning algorithm classifiers

BACKGROUND

Lymph node metastasis (LNM) is an essential factor affecting the prognosis of patients with lung squamous cell carcinoma (LUSC), as well as a critical consideration for the choice of treatment strategy. Exploring effective methods for predicting LNM in LUSC may benefit clinical decision making.

MATERIALS AND METHODS

We used data collected from the Surveillance, Epidemiology, and End Results (SEER) database to develop machine learning algorithm classifiers, including boosted trees (BTs), based on the primary clinical parameters of patients to predict LNM in LUSC. Training on a large-sample training cohort (n = 8,063) allowed for the construction of several concise classifiers for LNM prediction in LUSC, which were then validated using test and in-house cohorts (n = 2,017 and 57, respectively).

RESULTS

The six classifiers established in this research enabled distinction between patients with and without LNM. Among these classifiers, the BT classifier was the top performer, with accuracy, F1 scores, precision, recall, sensitivity, and specificity values of 0.654, 0.621, 0.654, 0.592, 0.592, and 0.711, respectively. The precision recall (PR) and receiver operating characteristic (ROC) (with area under the curve = 0.714) curves also supported this result, which was validated by the in-house cohort. Notably, the tumor stage was a critical factor in determining LNM in patients with LUSC.

CONCLUSIONS

The use of classifiers, especially the BT classifier, may serve as a useful tool for improving clinical precision and individualized treatment of patients with LUSC.

A Study on Epidemic Information Screening, Prevention and Control of Public Opinion Based on Health and Medical Big Data: A Case Study of COVID-19

The outbreak of the coronavirus disease 2019 (COVID-19) represents an alert for epidemic prevention and control in public health. Offline anti-epidemic work is the main battlefield of epidemic prevention and control. However, online epidemic information prevention and control cannot be ignored. The aim of this study was to identify reliable information sources and false epidemic information, as well as early warnings of public opinion about epidemic information that may affect social stability and endanger the people's lives and property. Based on the analysis of health and medical big data, epidemic information screening and public opinion prevention and control research were decomposed into two modules. Eight characteristics were extracted from the four levels of coarse granularity, fine granularity, emotional tendency, and publisher behavior, and another regulatory feature was added, to build a false epidemic information identification model. Five early warning indicators of public opinion were selected from the macro level and the micro level to construct the early warning model of public opinion about epidemic information. Finally, an empirical analysis on COVID-19 information was conducted using big data analysis technology.

German Dentists' Preferences for the Treatment of Apical Periodontitis: A Cross-Sectional Survey

Currently, there is no standard treatment protocol for apical periodontitis (AP). Thus, restorable teeth might get extracted and replaced prosthetically. This study evaluated German dentists’ preferred AP treatment decisions and the influencing factors for selecting tooth retention by initial/repeated surgical/non-surgical root-canal treatment (RCT) or extraction with/without prosthetic replacement. Through an online-survey, participants (n = 260) rated different treatment options for four case scenarios with AP in anterior/posterior teeth without/with previous RCT. Statistical analysis included the Friedman test for intra-case comparisons and Chi-squared test for factor-associations (p ≤ 0.05). Tooth retention using initial/repeated RCT was ranked first in all scenarios and rated as (very) appropriate by most participants, while implant-supported crowns (ISC) and apicoectomy had the second ratings. ISC were preferred more on posterior teeth or previous root-canal-treated teeth. Rating levels of treatment options displayed significant differences for all case scenarios. Posterior tooth retention by RCT demonstrated a significant association with work experience. Tooth retention with previous RCT displayed a significant correlation with dentists’ privately insured patients. Most dentists preferred tooth preserving with initial/repeated RCT, while others selected non-evidence-based choices. This reflects a lack of consensus of AP treatment decisions in Germany. Fixed treatment guidelines and further evaluation of treatment–decision-correlated factors are recommended for correct treatment planning.

Usability Testing of an Interactive Dashboard for Surgical Quality Improvement in a Large Congenital Heart Center

BACKGROUND

Interactive data visualization and dashboards can be an effective way to explore meaningful patterns in large clinical data sets and to inform quality improvement initiatives. However, these interactive dashboards may have usability issues that undermine their effectiveness. These usability issues can be attributed to mismatched mental models between the designers and the users. Unfortunately, very few evaluation studies in visual analytics have specifically examined such mismatches between these two groups.

OBJECTIVES

We aimed to evaluate the usability of an interactive surgical dashboard and to seek opportunities for improvement. We also aimed to provide empirical evidence to demonstrate the mismatched mental models between the designers and the users of the dashboard.

METHODS

An interactive dashboard was developed in a large congenital heart center. This dashboard provides real-time, interactive access to clinical outcomes data for the surgical program. A mixed-method, two-phase study was conducted to collect user feedback. A group of designers (N = 3) and a purposeful sample of users (N = 12) were recruited. The qualitative data were analyzed thematically. The dashboards were compared using the System Usability Scale (SUS) and qualitative data.

RESULTS

The participating users gave an average SUS score of 82.9 on the new dashboard and 63.5 on the existing dashboard (p = 0.006). The participants achieved high task accuracy when using the new dashboard. The qualitative analysis revealed three opportunities for improvement. The data analysis and triangulation provided empirical evidence to the mismatched mental models.

CONCLUSION

We conducted a mixed-method usability study on an interactive surgical dashboard and identified areas of improvements. Our study design can be an effective and efficient way to evaluate visual analytics systems in health care. We encourage researchers and practitioners to conduct user-centered evaluation and implement education plans to mitigate potential usability challenges and increase user satisfaction and adoption.