{
    "abstract": "The American College of Obstetricians and Gynecologists (ACOG) defines postpartum hemorrhage (PPH) as a blood loss of >500mL following vaginal delivery or >1000mL following cesarean section. PPH is widely recognized as a common cause of maternal death. However, there is currently no effective method to predict its risk of occurrence.\nTo develop a fuzzy expert system to predict the risk of developing PPH and to evaluate its performance in the clinical setting.\nThis system was developed using MATLAB software. Mamdani inference was used to simulate reasoning of experts in the field. To evaluate the performance of the system, a dataset of 1705 patients admitted at the Labor and Delivery ward of The Second Affiliated Hospital of Nanjing Medical University from 2017-10 to 2018-04, was considered.\nThe Negative Predictive value (NPV), Positive Predictive value PPV), Specificity and Sensitivity were calculated and were 99.72%, 18.50%, 87.48% and 92.16% respectively.\nOur findings suggest that the fuzzy expert system can be used to predict PPH in clinical settings and thus decrease maternal mortality rate due to hemorrhage.",
    "authors": [
        {
            "affiliation": "Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China.",
            "firstname": "Yussriya Hanaa",
            "initials": "YH",
            "lastname": "Doomah"
        },
        {
            "affiliation": "Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China.",
            "firstname": "Song-Yuan",
            "initials": "SY",
            "lastname": "Xu"
        },
        {
            "affiliation": "Department of Obstetrics and Gynecology, Jiangsu Jianyin People's Hospital, Jianyin, Jiangsu Province, China.",
            "firstname": "Li-Xia",
            "initials": "LX",
            "lastname": "Cao"
        },
        {
            "affiliation": "Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China.",
            "firstname": "Sheng-Lian",
            "initials": "SL",
            "lastname": "Liang"
        },
        {
            "affiliation": "Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China.\nDepartment of Obstetrics and Gynecology, Cape Coast Teaching Hospital, Cape Coast, Central Region, Ghana.",
            "firstname": "Gloria Francisca",
            "initials": "GF",
            "lastname": "Nuer-Allornuvor"
        },
        {
            "affiliation": "Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China.",
            "firstname": "Xiao-Yan",
            "initials": "XY",
            "lastname": "Ying"
        }
    ],
    "conclusions": null,
    "copyrights": "\u00a9 2019 Yussriya Hanaa Doomah, Song-Yuan Xu, Li-Xia Cao, Sheng-Lian Liang, Gloria Francisca Nuer-Allornuvor, Xiao-Yan Ying.",
    "doi": "10.5455/aim.2019.27.318-326",
    "journal": "Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH",
    "keywords": [
        "Postpartum hemorrhage",
        "maternal death",
        "retained placenta",
        "uterine inertia"
    ],
    "methods": null,
    "publication_date": "2020-03-27",
    "pubmed_id": "32210499\n25103301\n17012482\n27981719\n18715412\n20237047\n26094567\n25041161\n23061690\n16272036\n24201690\n26557577\n28847038\n19750391\n27041808\n29560150\n25731692\n27831987\n29911829\n29067758",
    "results": "The Negative Predictive value (NPV), Positive Predictive value PPV), Specificity and Sensitivity were calculated and were 99.72%, 18.50%, 87.48% and 92.16% respectively.",
    "title": "A Fuzzy Expert System to Predict the Risk of Postpartum Hemorrhage.",
    "xml": "<Element 'PubmedArticle' at 0x788503515c60>"
}