Interpretable machine learning text classification for clinical computed tomography reports – a case study of temporal bone fracture

Background: Machine learning (ML) has demonstrated success in classifying patients’ diagnostic outcomes in free-text clinical notes. However, due to the machine learning model's complexity, interpreting the mechanism behind classification results remains difficult. Methods: We investigated interpretable representations of text-based machine learning classification models. We created machine learning models to classify temporal bone fractures based on 164 temporal bone computed tomography (CT) text reports. We adopted the XGBoost, Support Vector Machine, Logistic Regression, and Random Forest algorithms. To interpret models, we used two major methodologies: (1) We calculated the average word frequency score (WFS) for keywords. The word frequency score shows the frequency gap between positively and negatively classified cases. (2) We used Local Interpretable Model-Agnostic Explanations (LIME) to show the word-level contribution to bone fracture classification. Results: In temporal bone fracture classification, the random forest model achieved an average F1-score of 0.93. WFS revealed a difference in keyword usage between fracture and non-fracture cases. Additionally, LIME visualized the keywords' contributions to the classification results. The evaluation of LIME-based interpretation achieved the highest interpreting accuracy of 0.97. Conclusion: The interpretable text explainer can improve physicians' understanding of machine learning predictions. By providing simple visualization, our model can increase the trust of computerized models. Our model supports more transparent computerized decision-making in clinical settings

Team principles for successful interdisciplinary research teams

Interdisciplinary research teams can be extremely beneficial when addressing difficult clinical problems. The incorporation of conceptual and methodological strategies from a variety of research disciplines and health professions yields transformative results. In this setting, the long-term goal of team science is to improve patient care, with emphasis on population health outcomes. However, team principles necessary for effective research teams are rarely taught in health professional schools. To form successful interdisciplinary research teams in cardio-oncology and beyond, guiding principles and organizational recommendations are necessary. Cardiovascular disease results in annual direct costs of $220 billion (about$680 per person in the US) and is the leading cause of death for cancer survivors, including adult survivors of childhood cancers. Optimizing cardio-oncology research in interdisciplinary research teams has the potential to aid in the investigation of strategies for saving hundreds of thousands of lives each year in the United States and mitigating the annual cost of cardiovascular disease. Despite published reports on experiences developing research teams across organizations, specialties and settings, there is no single journal article that compiles principles for cardiology or cardio-oncology research teams. In this review, recurring threads linked to working as a team, as well as optimal methods, advantages, and problems that arise when managing teams are described in the context of career development and research. The worth and hurdles of a team approach, based on practical lessons learned from establishing our multidisciplinary research team and information gleaned from relevant specialties in the development of a successful team are presented

The CanMars Mars Sample Return analogue mission

The return of samples from known locations on Mars is among the highest priority goals of the international planetary science community. A possible scenario for Mars Sample Return (MSR) is a series of 3 missions: sample cache, fetch, and retrieval. The NASA Mars 2020 mission represents the first cache mission and was the focus of the CanMars analogue mission described in this paper. The major objectives for CanMars included comparing the accuracy of selecting samples remotely using rover data versus a traditional human field party, testing the efficiency of remote science operations with periodic pre-planned strategic observations (Strategic Traverse Days), assessing the utility of realistic autonomous science capabilities to the remote science team, and investigating the factors that affect the quality of sample selection decision-making in light of returned sample analysis. CanMars was conducted over two weeks in November 2015 and continued over three weeks in October and November 2016 at an analogue site near Hanksville, Utah, USA, that was unknown to the Mission Control Team located at the University of Western Ontario (Western) in London, Ontario, Canada. This operations architecture for CanMars was based on the Phoenix and Mars Exploration Rover missions together with previous analogue missions led by Western with the Mission Control Team being divided into Planning and Science sub-teams. In advance of the 2015 operations, the Science Team used satellite data, chosen to mimic datasets available from Mars-orbiting instruments, to produce a predictive geological map for the landing ellipse and a set of hypotheses for the geology and astrobiological potential of the landing site. The site was proposed to consist of a series of weakly cemented multi-coloured sedimentary rocks comprising carbonates, sulfates, and clays, and sinuous ridges with a resistant capping unit, interpreted as inverted paleochannels. Both the 2015 CanMars mission, which achieved 11 sols of operations, and the first part of the 2016 mission (sols 12–21), were conducted with the Mars Exploration Science Rover (MESR) and a series of integrated and hand-held instruments designed to mimic the payload of the Mars 2020 rover. Part 2 of the 2016 campaign (sols 22–39) was implemented without the MESR rover and was conducted exclusively by the field team as a Fast Motion Field Test (FMFT) with hand-carried instruments and with the equivalent of three sols of operations being executed in a single actual day. A total of 8 samples were cached during the 39 sols from which the Science Team prioritized 3 for “return to Earth”. Various science autonomy capabilities, based on flight-proven or near-future techniques intended for actual rover missions, were tested throughout the 2016 CanMars activities, with autonomous geological classification and targeting and autonomous pointing refinement being used extensively during the FMFT. Blind targeting, contingency sequencing, and conditional sequencing were also employed. Validation of the CanMars cache mission was achieved through various methods and approaches. The use of dedicated documentarians in mission control provided a detailed record of how and why decisions were made. Multiple separate field validation exercises employing humans using traditional geological techniques were carried out. All 8 of the selected samples plus a range of samples from the landing site region, collected out-of-simulation, have been analysed using a range of laboratory analytical techniques. A variety of lessons learned for both future analogue missions and planetary exploration missions are provided, including: dynamic collaboration between the science and planning teams as being key for mission success; the more frequent use of spectrometers and micro-imagers having remote capabilities rather than contact instruments; the utility of strategic traverse days to provide additional time for scientific discussion and meaningful interpretation of the data; the benefit of walkabout traverse strategies along with multi-sol plans with complex decisions trees to acquire a large amount of contextual data; and the availability of autonomous geological targeting, which enabled complex multi-sol plans gathering large suites of geological and geochemical survey data. Finally, the CanMars MSR activity demonstrated the utility of analogue missions in providing opportunities to engage and educate children and the public, by providing tangible hands-on linkages between current robotic missions and future human space missions. Public education and outreach was a priority for CanMars and a dedicated lead coordinated a strong presence on social media (primarily Twitter and Facebook), articles in local, regional, and national news networks, and interaction with the local community in London, Ontario. A further core objective of CanMars was to provide valuable learning opportunities to students and post-doctoral fellows in preparation for future planetary exploration missions. A learning goals survey conducted at the end of the 2016 activities had 90% of participants “somewhat agreeing” or “strongly agreeing” that participation in the mission has helped them to increase their understanding of the four learning outcomes