[{"bbox": [114, 94, 1612, 1121], "category": "Table", "text": "<table><thead><tr><th>Results</th><th>Results chain (@):<br>Main expected results</th><th>Indicators (@):</th><th>Baselines<br>(values / years)</th><th>Targets<br>(values / years)</th><th>Sources of data</th><th>Assumptions</th></tr></thead><tbody><tr><td></td><td></td><td>on increasing the quality and availability of information.</td><td></td><td>3.3.1. Improvements developed in the Technological Platform of the Social Registry. (2027)</td><td></td><td>detecting vulnerabilities in the target population.<br>STECSDI has sufficient quantity and quality of data to achieve a level of recall above 70%.</td></tr><tr><td rowspan=\"3\">Output 2<br>related to<br>specific<br>objective<br>3</td><td rowspan=\"3\">Piloted Artificial Intelligence model to test predictive analytics systems based on the level of risk of suffering malnutrition or any of its associated factors (e.g. low birth weight, teenage pregnancy, anaemia, premature birth, among other conditions), as well as systems that predict probability to skip critical services for the prevention of malnutrition (e.g. vaccination, prenatal check-ups, well-baby check-ups, among others), and provide personalised solutions and services to families and mothers in disadvantaged conditions or from vulnerable groups</td><td>1. Number of exploratory diagnoses of adjustment of artificial intelligence models for the following topics: prevalence of chronic child malnutrition, adolescent pregnancy, low birth weight, anemia in children, anemia among pregnant women or premature birth.</td><td>1.1. 0/2023<br>2.1. 0/2023<br>3.1. 0/2023</td><td>1.1.1. One (1) exploratory diagnosis developed to adjust artificial intelligence models for the following topics: prevalence of chronic child malnutrition, adolescent pregnancy, low birth weight, anemia in children, anemia among pregnant women or premature birth.<br>2.1.1. Algorithms raised: Minimum 5 that exceed 70% recall and precision of 60%.<br>(2027)</td><td>1.1.1.1.<br>Institutional project progress reports<br>2.1.1.1.<br>Institutional project progress reports<br>3.1.1.1 Institutional project progress reports/ SUUSEN functionalities</td><td>A recall level greater than 70% and precision of 60% are achieved in at least five (5) algorithms.</td></tr><tr><td>2. Number of algorithms developed that exceed the criterion of minimum recall of 70% and precision of 40% based on the results of the diagnosis.</td><td></td><td></td><td>3.1.1 At least 5 artificial intelligence algorithms, implemented and automated with their respective documentation and transfer of knowledge to the STECSDI team.<br>(2027)</td><td></td><td></td></tr><tr><td>3. Number of Artificial Intelligence algorithms implemented and</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>"}, {"bbox": [1493, 1149, 1614, 1173], "category": "Page-footer", "text": "Page 33 of 42"}]