新生儿喂养观察记录数据分析

1、背景分析

对喂养数据进行建模，从新生儿的喂养中抽象出关键的事件，包括：

喝奶
拉尿
拉屎

对每个关键事件的多维度属性值进行量化、归一：

上次喂奶量 / 两次间隔时间 = 单位奶支撑的单位时间，代表新生儿对营养的吸收情况
本次拉屎量 / 两次间隔时间 = 单位时间产生的屎量，代表新生儿的肠胃运行情况
本次拉尿量 / 两次间隔时间 = 单位时间产生的尿量，代表新生儿的肾脏和膀胱运行情况

由于屎量不好精确定义，因此采用一个枚举区间表示：

10：大量
5：中等
2：少量

对尿的定义也采取类似的归一化处理：

1.2：多
1：中等
0.8：少量

2、数据拟合

用我家宝出生后72小时数据作为训练数据，

import matplotlib.pyplot as plt
import datetime

y_data_milk = [
    .5 / (datetime.datetime.strptime("2022-01-20 23:30", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-20 21:32", "%Y-%m-%d %H:%M")).seconds,
    .5 / (datetime.datetime.strptime("2022-01-21 01:30", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-20 23:30", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-21 03:24", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 01:30", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-21 05:20", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 03:24", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-21 06:55", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 05:20", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-21 09:25", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 06:55", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-21 11:00", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 09:25", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-21 12:08", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 11:00", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-21 14:35", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 12:08", "%Y-%m-%d %H:%M")).seconds,
    .5 / (datetime.datetime.strptime("2022-01-21 16:20", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 14:35", "%Y-%m-%d %H:%M")).seconds,
    .15 / (datetime.datetime.strptime("2022-01-21 18:15", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 16:20", "%Y-%m-%d %H:%M")).seconds,
    .15 / (datetime.datetime.strptime("2022-01-21 20:00", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 18:15", "%Y-%m-%d %H:%M")).seconds,
    .20 / (datetime.datetime.strptime("2022-01-21 21:20", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 20:00", "%Y-%m-%d %H:%M")).seconds,
    .30 / (datetime.datetime.strptime("2022-01-22 00:21", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 21:20", "%Y-%m-%d %H:%M")).seconds,
    .18 / (datetime.datetime.strptime("2022-01-22 02:10", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 00:21", "%Y-%m-%d %H:%M")).seconds,
    .30 / (datetime.datetime.strptime("2022-01-22 03:20", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 02:10", "%Y-%m-%d %H:%M")).seconds,
    .30 / (datetime.datetime.strptime("2022-01-22 05:25", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 03:20", "%Y-%m-%d %H:%M")).seconds,
    .30 / (datetime.datetime.strptime("2022-01-22 08:06", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 05:25", "%Y-%m-%d %H:%M")).seconds,
    .30 / (datetime.datetime.strptime("2022-01-22 11:00", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 08:06", "%Y-%m-%d %H:%M")).seconds,
    .30 / (datetime.datetime.strptime("2022-01-22 13:10", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 11:00", "%Y-%m-%d %H:%M")).seconds,
    .30 / (datetime.datetime.strptime("2022-01-22 14:50", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 13:10", "%Y-%m-%d %H:%M")).seconds,
    .30 / (datetime.datetime.strptime("2022-01-22 18:25", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 14:50", "%Y-%m-%d %H:%M")).seconds,
    .30 / (datetime.datetime.strptime("2022-01-22 20:15", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 18:25", "%Y-%m-%d %H:%M")).seconds,
    .37 / (datetime.datetime.strptime("2022-01-22 21:51", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 20:15", "%Y-%m-%d %H:%M")).seconds,
    .35 / (datetime.datetime.strptime("2022-01-23 00:00", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 21:51", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-23 01:15", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-23 00:00", "%Y-%m-%d %H:%M")).seconds,
    .20 / (datetime.datetime.strptime("2022-01-23 01:50", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-23 01:15", "%Y-%m-%d %H:%M")).seconds,
    .20 / (datetime.datetime.strptime("2022-01-23 02:23", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-23 01:50", "%Y-%m-%d %H:%M")).seconds,
    .15 / (datetime.datetime.strptime("2022-01-23 03:26", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-23 02:23", "%Y-%m-%d %H:%M")).seconds,
    .20 / (datetime.datetime.strptime("2022-01-23 05:20", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-23 03:26", "%Y-%m-%d %H:%M")).seconds,
    .30 / (datetime.datetime.strptime("2022-01-23 06:52", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-23 05:20", "%Y-%m-%d %H:%M")).seconds,
    .35 / (datetime.datetime.strptime("2022-01-23 08:30", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-23 06:52", "%Y-%m-%d %H:%M")).seconds,
    .40 / (datetime.datetime.strptime("2022-01-23 10:54", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-23 08:30", "%Y-%m-%d %H:%M")).seconds,
    .20 / (datetime.datetime.strptime("2022-01-23 12:45", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-23 10:54", "%Y-%m-%d %H:%M")).seconds
]
x_data_milk = list(range(0, len(y_data_milk)))

y_data_shit = [
    .5 / (datetime.datetime.strptime("2022-01-20 23:30", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-20 21:32", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-21 00:20", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-20 23:30", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-21 03:24", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 00:20", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-21 04:20", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 03:24", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-21 06:55", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 04:20", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-21 09:25", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 06:55", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-21 14:35", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 09:25", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-21 18:15", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 14:35", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-22 05:32", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 18:15", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-22 13:10", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 05:32", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-22 14:50", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 13:10", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-22 18:25", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 14:50", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-23 08:30", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 18:25", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-23 12:45", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-23 08:30", "%Y-%m-%d %H:%M")).seconds,
    .10 / (datetime.datetime.strptime("2022-01-23 13:10", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-23 12:45", "%Y-%m-%d %H:%M")).seconds
]
x_data_shit = list(range(0, len(y_data_shit)))

y_data_urine = [
    .1 / (datetime.datetime.strptime("2022-01-21 00:20", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-20 21:32", "%Y-%m-%d %H:%M")).seconds,
    .1 / (datetime.datetime.strptime("2022-01-21 03:24", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 00:20", "%Y-%m-%d %H:%M")).seconds,
    .1 / (datetime.datetime.strptime("2022-01-21 04:20", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 03:24", "%Y-%m-%d %H:%M")).seconds,
    .1 / (datetime.datetime.strptime("2022-01-21 06:55", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 04:20", "%Y-%m-%d %H:%M")).seconds,
    .1 / (datetime.datetime.strptime("2022-01-21 09:25", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 06:55", "%Y-%m-%d %H:%M")).seconds,
    .1 / (datetime.datetime.strptime("2022-01-21 14:35", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 09:25", "%Y-%m-%d %H:%M")).seconds,
    .1 / (datetime.datetime.strptime("2022-01-21 18:15", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 14:35", "%Y-%m-%d %H:%M")).seconds,
    1.2 / (datetime.datetime.strptime("2022-01-22 00:21", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-21 18:15", "%Y-%m-%d %H:%M")).seconds,
    1.2 / (datetime.datetime.strptime("2022-01-22 05:32", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 00:21", "%Y-%m-%d %H:%M")).seconds,
    1.2 / (datetime.datetime.strptime("2022-01-22 08:06", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 05:32", "%Y-%m-%d %H:%M")).seconds,
    .1 / (datetime.datetime.strptime("2022-01-22 13:10", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 08:06", "%Y-%m-%d %H:%M")).seconds,
    .1 / (datetime.datetime.strptime("2022-01-22 14:50", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 13:10", "%Y-%m-%d %H:%M")).seconds,
    .1 / (datetime.datetime.strptime("2022-01-22 18:25", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 14:50", "%Y-%m-%d %H:%M")).seconds,
    .1 / (datetime.datetime.strptime("2022-01-23 00:00", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-22 18:25", "%Y-%m-%d %H:%M")).seconds,
    .1 / (datetime.datetime.strptime("2022-01-23 01:50", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-23 00:00", "%Y-%m-%d %H:%M")).seconds,
    .1 / (datetime.datetime.strptime("2022-01-23 02:23", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-23 01:50", "%Y-%m-%d %H:%M")).seconds,
    .1 / (datetime.datetime.strptime("2022-01-23 03:26", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-23 02:23", "%Y-%m-%d %H:%M")).seconds,
    .1 / (datetime.datetime.strptime("2022-01-23 05:20", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-23 03:26", "%Y-%m-%d %H:%M")).seconds,
    .1 / (datetime.datetime.strptime("2022-01-23 06:52", "%Y-%m-%d %H:%M") - datetime.datetime.strptime("2022-01-23 05:20", "%Y-%m-%d %H:%M")).seconds
]
x_data_urine = list(range(0, len(y_data_urine)))

plt.plot(x_data_milk, y_data_milk, color='red', linewidth=2.0, linestyle='--')
plt.plot(x_data_shit, y_data_shit, color='blue', linewidth=3.0, linestyle='-.')
plt.plot(x_data_urine, y_data_urine, color='green', linewidth=4.0, linestyle='--')
plt.show()

3、数据洞察

喂奶指数总体呈现上升趋势，相比于刚出生时候，总体均值提升了50%，表现该婴儿的肠胃和营养吸收情况正在提升。另一方面，该指数的方差相对还较大，表明该婴儿的身体机能还处于构建期
粪便指数和排尿指数的总体情况相对较平均，表明身体脏器功能总体情况不错
红色折线，也就是喂奶指数并不是一条平滑上升的曲线，而是呈现一个总体上升但是不断波动的形状。这背后的原因是：婴儿的肠胃能力的增长是线性的，但是负责喂奶的阿姨每次给小孩喂的量总是滞后的，阿姨总是要等到发现上次喂奶量不够后，才会在下一次增加一定的量。得到这个洞察后，就可以知道我们更好的进行喂奶量的精细化控制，那就是根据拟合后的模型，按照分钟级进行精细化的预测，精确到分支和毫升预测下一次喂奶的时间和喂奶量。

目前数据量还不太足够，后期收集足够数据后，应该可以得到更多的数据趋势洞察。

另一方便，从医院的视角来看，如果能够将全部婴儿的喂养数据全部录入数据仓库，进行大数据分析，从而可以得到整体新生儿的健康均值和方差情况，然后就可以基于标准模型，对之后的新生儿进行实时的指标监控，及时发现问题。

posted @ 2022-01-23 14:50 郑瀚阅读(347) 评论(0) 编辑收藏举报

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Han Zheng, Thinker and Doer

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新生儿喂养观察记录数据分析

1、背景分析

2、数据拟合

3、数据洞察

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