Using public and private data sets, participants will have to solve relevant challenges for national public health, such as the study of the COVID-19 pandemic and the design of strategies for cancer staging. Teams area composed by professionals from data science, health, engineering and anyone who seeks to collaboratively answer challenging questions and impact the local and international health ecosystem.
Each team will work with one of the three challenges. In this repository you will find the data and resources available for each challenge. The description of each dataset can be found inside the folders.
1. Impact analysis of sociodemographic features, mobility and information in the dissemination/vaccination of COVID-19 in Chile
COVID-19 pandemic spread rapidly around the world, forcing the population to take severe actions to deal with it. Its development and impact over the years have been affected not only by its transmission mechanisms and clinical manifestations, but also by demographic, social and cultural factors.
This challenge counts with a dataset that contains the daily report of contagion cases, number of deaths, mobility index, positivity of RT-PCR tests and administered doses of COVID-19 vaccine for the different communes of the country. The registry of deaths from 2016 to 2022 and the sociodemographic data of the communes based on the CENSUS carried out in 2017 are also available. On the other hand, to complement the traditional data sources, you will have tools for extracting data from internet sources such as google trends, publications in magazines, newspapers, among others.
Based on the available data, you must analyze the impact of the variables associated with each commune on the behavior of the new cases detected, number of deaths and/or vaccinations. The models developed should serve as risk predictors for early decision-making based on data.
Cancer is one of the most relevant pathologies worldwide given its high incidence and mortality rates. As an effect of the pandemic, it is estimated that in Chile during the 2022-2030 period there will be an excess diagnosis of approximately 2,000 cases, which between 2020 and 2022 will be in advanced stages. Due to the reduction of non-COVID-19 diagnosis and treatment during the pandemic, an excess of 3,542 deaths is projected for the 2022-2030 period (Ward et al, 2021). Late consultation for suspected cancer implies a diagnosis in advanced stages of the disease, which has repercussions on the prognosis, timely curative treatment and patient survival.
Cancer staging, from stage 0 to IV, allows describing the state of the present cancer through the location of the primary tumor and its respective morphology, in addition to determine if it has spread to lymph nodes or other areas of the body. Once the stage of the cancer is defined, it is possible to choose the therapeutic route for each patient.
An automatic tumor staging algorithm makes it possible to identify the common characteristics of patients at a certain stage and complete the missing information in the Hospital Tumor Registry, both from FALP and from institutions with similar registries, such as the Valdivia Hospital. A complete Hospital Registry of Tumors allows the analysis of indicated treatment and survival of patients in each of the stages. The FALP Hospital Tumor Registry contains a demographic, clinical and pathological description of the tumors, in addition to the sequence of treatment received by the patient. Based on the available data, an Artificial Intelligence model for tumor stage prediction should be developed for patients with incomplete TNM diagnosis. Based on preliminary studies, performance greater than 95% accuracy is expected, with no false negatives for stage I and IV.
3. Cancer staging of patients in FONASA waiting list (for priority cancer: breast, uterus, lung, colon, gastric and thyroid)
The COVID-19 pandemic not only generated an increase in healthcare pressure during the periods of greatest contagion, but also produced a shift in therapeutic priorities as an externality, reducing the number of cancer cases screened and treated. This has a direct impact on the waiting lists corresponding to GES pathologies. In mid-2020, more than 6,000 oncological guarantees had expired, where two thirds of them corresponded to diagnostic benefits. Currently, the GES waiting list is managed in order of arrival, which means an opportunity for optimization both in the delivery of curative and palliative treatment to whom it corresponds, as well as in relation to the use of care resources.
For this challenge, participants will count with a database from FONASA that contains demographic variables and oncological benefits, from which it is expected the development of an Artificial Intelligence model for the estimation of stage and/or extension of the tumor. The developed model will work as a tool for patients prioritization on the waiting list. Based on preliminary studies, a performance greater than 70% accuracy is expected, minimizing the number of false negatives for stage I and IV.
In January 5th a training session was held for the Datathon's participants. You can access the video and resources here.