- 分析与建模 - 机器学习
Partho Sengupta 博士需要一种方法来准确识别超声心动图导致的疾病模式，以改进诊断并挽救更多生命。具体来说，他想区分两种不同的疾病：心肌病，它直接影响心肌并经常导致心力衰竭，以及心包炎，它就像心脏受累但实际上并不影响心脏。虽然这两种疾病都存在类似的心脏病，但治疗方法却大不相同。对于心包炎，治疗可能包括药物治疗，很少包括手术。然而，如果诊断为心肌病，则患者接受医疗管理（即心脏起搏器）或在极端情况下进行心脏移植。对这些疾病状况的误诊可能会使患者的生命处于危险之中，并且对医院来说是非常昂贵的。因此，Sengupta 博士求助于 Saffron 的自然智能平台，以帮助他的团队提高对这些疾病的诊断准确性。
Partho Sengupta 博士，西奈山医院心脏超声研究主任和心脏病学医学副教授
Sengupta 博士与 Saffron 的自然智能平台合作，发起了一项盲研究，其中包括 15 名缩窄性心包炎患者和 15 名限制性心肌病患者。当多维超声心动图诊断数据被输入 Saffron 的联想记忆库时，该数据由每位患者每次心跳 10,000 个属性组成。这些属性是从复杂超声心动图数据中心的六个位置的 90 个指标中收集的，并且在单个心跳内收集了 20 次。
Electronic Medical Record, Medical Diagnostic Instruments, Personal Medical, Health Symptoms
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