Application of Process Mining for Modelling Small Cell Lung Cancer Prognosis.

Process mining is a relatively new method that connects data science and process modelling. In the past years a series of applications with health care production data have been presented in process discovery, conformance check and system enhancement. In this paper we apply process mining on clinical oncological data with the purpose of studying survival outcomes and chemotherapy treatment decision in a real-world cohort of small cell lung cancer patients treated at Karolinska University Hospital (Stockholm, Sweden). The results highlighted the potential role of process mining in oncology to study prognosis and survival outcomes with longitudinal models directly extracted from clinical data derived from healthcare.

A novel analytical framework for risk stratification of real-world data using machine learning: A small cell lung cancer study.

In recent studies, small cell lung cancer (SCLC) treatment guidelines based on Veterans' Administration Lung Study Group limited/extensive disease staging and resulted in broad and inseparable prognostic subgroups. Evidence suggests that the eight versions of tumor, node, and metastasis (TNM) staging can play an important role to address this issue. The aim of the present study was to improve the detection of prognostic subgroups from a real-word data (RWD) cohort of patients and analyze their patterns using a development pipeline with thoracic oncologists and machine learning methods. The method detected subgroups of patients informing unsupervised learning (partition around medoids) including the impact of covariates on prognosis (Cox regression and random survival forest). An analysis was carried out using patients with SCLC (n = 636) with stage IIIA-IVB according to TNM classification. The analysis yielded k = 7 compacted and well-separated clusters of patients. Performance status (Eastern Cooperative Oncology Group-Performance Status), lactate dehydrogenase, spreading of metastasis, cancer stage, and CRP were the baselines that characterized the subgroups. The selected clustering method outperformed standard clustering techniques, which were not capable of detecting meaningful subgroups. From the analysis of cluster treatment decisions, we showed the potential of future RWD applications to understand disease, develop individualized therapies, and improve healthcare decision making.

Patterns of patient reluctance to receive complementary-medicine treatments in a hospital setting: A cross-sectional study.

OBJECTIVE: We aim to characterize the patient population that exhibits reluctance to undergo complementary medicine (CM) treatments in a hospital setting. METHODS: We conducted a cross-sectional prospective study among patients prior to hospitalization using structured questionnaires in a single center in Israel. Participants were asked to rate their degree of consent to receiving CM treatments during hospitalization. RESULTS: The CM-reluctant group was 7.1 % of the study cohort. The CM modalities most commonly refused were spiritual guidance, acupuncture, and energy and healing therapies. The CM-reluctant population showed a weaker relation to spiritual content and tended to value complementary medicine's effectiveness less in comparison to the CM-consenting group. The main reason for reluctance was skepticism of the perceived effectiveness of CM. CONCLUSIONS: With skepticism playing a major role in decision making, we should question whether the Stakeholders in the field of CM and public health services are succeeding in explaining the benefits and risks of CM treatments.