{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T01:53:19Z","timestamp":1781661199934,"version":"3.54.5"},"reference-count":0,"publisher":"IBERAMIA: Sociedad Iberoamericana de Inteligencia Artificial","issue":"77","license":[{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ia"],"abstract":"<jats:p>In India's mountainous areas, maternal mortality is still a serious public health concern, especially in Uttarakhand, where access to healthcare is hampered by geographical obstacles. UttaraRisk-Next, a multi-task ensemble learning framework for thorough maternal health risk assessment, is presented in this paper. Three crucial outcomes are simultaneously predicted by the model: the probability of abortion, the continuous risk percentage (0\u2013100%), and the risk of maternal mortality. We created 78 clinical features in accordance with WHO guidelines using a synthetic but epidemiologically representative dataset of 2,500 pregnancies from 13 districts in Uttarakhand. These features included blood pressure classifications, hemoglobin categories, and socioeconomic vulnerability indicators. UttaraRisk-Next employs an ensemble architecture combining gradient boosting and random forest models with isotonic calibration for probability refinement. On validation data (n=500), the model achieved: risk prediction MAE 5.557% with R^2=0.708 and 97.6% interval coverage; abortion classification ROC-AUC 0.558 with excellent calibration (ECE=0.020); mortality prediction ECE=0.001 despite rare event frequency (0.6%). Comprehensive fairness analysis across rural-urban, age, and socioeconomic dimensions demonstrated equitable performance (ECE differences &lt;0.025). The model identifies 22.4% of pregnancies as high-risk, enabling targeted resource allocation. With 2.1ms inference time and 45MB memory footprint, UttaraRisk-Next is deployable in resource-constrained settings, directly supporting SDG-3.1 (maternal mortality reduction) and SDG-5 (gender equality) objectives in the Indian Himalayan region<\/jats:p>","DOI":"10.4114\/intartif.vol29iss77pp151-181","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T08:43:34Z","timestamp":1779439414000},"page":"151-181","source":"Crossref","is-referenced-by-count":0,"title":["UttaraRisk-Next: A Multi-Task Ensemble Learning Framework for Maternal Health Risk Prediction"],"prefix":"10.4114","volume":"29","author":[{"given":"Mohit Lal Sah","family":"Mohit","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahul Kumar Mishra","family":"Rahul","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pranjali","family":"Bafila","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2598","published-online":{"date-parts":[[2026,4,30]]},"container-title":["Inteligencia Artificial"],"original-title":[],"link":[{"URL":"https:\/\/journal.iberamia.org\/index.php\/intartif\/article\/download\/2807\/283","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journal.iberamia.org\/index.php\/intartif\/article\/download\/2807\/283","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:52:06Z","timestamp":1781657526000},"score":1,"resource":{"primary":{"URL":"https:\/\/journal.iberamia.org\/index.php\/intartif\/article\/view\/2807"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,30]]},"references-count":0,"journal-issue":{"issue":"77","published-online":{"date-parts":[[2025,12,8]]}},"URL":"https:\/\/doi.org\/10.4114\/intartif.vol29iss77pp151-181","relation":{},"ISSN":["1988-3064","1137-3601"],"issn-type":[{"value":"1988-3064","type":"electronic"},{"value":"1137-3601","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4,30]]}}}